What is Big Data

1.

Data with greater variety, volume, and velocity that traditional data processing software cannot manage. Learn more in: Past, Present, and Future of Artificial Intelligence in Library Services

2.

Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications. Learn more in: Data, Knowledge, and Intelligence

3.

Any kind of data characterized by high volume, variety and/or velocity, which are difficult to process using traditional database management tools or data processing applications. Learn more in: Using Big Data in Healthcare

4.

Big data is an extremely large set of data that allows computational analysis to reveal particular human behavior, trends, and relationships. Learn more in: Fintech Ecosystem and Banking: The Case of Turkey

5.

A relatively large amount of data, defined sometimes as over a million rows of data or a certain size of data (such as by terabytes and petabytes). Learn more in: Reading Data Possibilities From an LMS Data Portal Data Dictionary

6.

Big data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. Learn more in: Challenges in Big Data Analysis

7.

Is data with at least one of the ten big characteristics, consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: Applying Intelligent Big Data Analytics in a Smart Airport Business: Value, Adoption, and Challenges

8.

Big data is a combination of structured and unstructured data which is abundant in quantity, variety as well as the rate at which it gets generated resulting in a complex data set which cannot be stored, managed and processed by conventional statistical tools. Learn more in: Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

9.

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations. Learn more in: Philosophy in the Knowledge Structure Pyramid: Knowledge Elicitation and Management

10.

Is enormous data set (mostly unstructured data) that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to interactions. Learn more in: Integration of Data Mining and Business Intelligence in Big Data Analytics: A Research Agenda on Scholarly Publications

11.

This expression describes the large volume of both structured and unstructured data that is created in an organization or business. Data grow exponentially, making it difficult to manage them. Traditional data management tools are unable to store or process data efficiently, thus new systems and tools are necessary. Learn more in: Challenges to Records and Information Management in the Fourth Industrial Revolution

12.

High-velocity, valuable, and/or multi-variety data with volumes beyond the ability of commonly used software to capture, manage, and process within a tolerable elapsed time. These Big Data necessitate new forms of processing to deliver high veracity (and low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization. Learn more in: Big Data Visualization of Association Rules and Frequent Patterns

13.

It is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. Learn more in: Platform for Citizen Engagement for Good Governance in India: A Case Study of MyGov.in

14.

A large volume of structured and unstructured data that inundates an organisation. Learn more in: Cloud Computing Big Data Adoption Impacts on Teaching and Learning in Higher Education: A Systematic Review

15.

This is data characterized by its large volume, wide variety, and high velocity. Volume refers to high quantity of data usually running into terabytes. The data is not only numbers or text, but can include images, videos, etc., making it very varied and the data is generated rapidly which is called its high velocity. Learn more in: Social Media Analytics for Maintaining Financial Stability

16.

Is the area of knowledge that studies how to treat, analyze, and obtain information from data sets that are too large to be analyzed by traditional systems. Learn more in: Challenges of Design Thinking in the Innovation of Individuals and Organizations

17.

The large-scale aggregation of data from software and wired devices, used for the purposes of analyzing systems or user behavior; it is characterized by its large volume, wide variety, velocity (or speed of transfer), its increased variability, and undetermined veracity. Learn more in: Massive Digital Libraries (MDLs) and the Impact of Mass-Digitized Book Collections

18.

The data which are not possible to be stored in relational databases at low cost and cannot be analyzed by classical methods, due to its volume and characteristics. Learn more in: Analysis of the 5Vs of Big Data in Virtual Travel Organizations

19.

Vast amounts of data generated today characterized by volume, velocity, and variety. Learn more in: Speeding Up the Internet in Big Data Era: Exploiting Historical User Request Patterns for Web Caching to Reduce User Delays

20.

According to some sources ( Boyd & Crawford, 2012 ; Mayer-Schönberger & Cukier, 2013 ) data volumes in the range of petabytes and beyond, which exceed the capacity of most current online storage and processing systems; critical issues related to big data include their volume, velocity, variety, value and veracity. However according to Parks (2014 : 255) data volume does not qualify in itself the “big data”, because the criteria should be relative to the “representativeness” of the specific field analysed. Learn more in: The Impact of the Impact of Meta-Data Mining From the SoReCom “A.S. de Rosa” @-Library

21.

Data that spans over 3 Vs namely Volume, Variety and Velocity. Learn more in: Driving Big Data with Hadoop Technologies

22.

Is a concept related to dealing with really high amounts of data. Traditional databases can not store and analyze such big amounts of data. Learn more in: Ethical Issues in Transhumanism

23.

Is an evolving term that describes any voluminous amount of structured, semi-structured, and unstructured data that has the potential to be mined for information. Learn more in: Digital Transformation: Impact of 5G Technology in Supply Chain Industry

24.

Big data also known as mega-data refers to all the digital data produced by the use of new technologies for personal or professional purposes. Learn more in: Recommendation Systems

25.

A broad term for large and complex data sets that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Learn more in: Data Science and Big Data Analytics in Financial Services: A Case Study

26.

Data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: Big Data-Driven Socioeconomic Development: An Interdisciplinary Approach

27.

Collaborative analysis done from structured (formally, predictable and formatted) and unstructured (posts, informal communication, social media usage) data regarding one specific topic. Learn more in: Approaching Information Architecture for a Market Intelligence System Based on Emerging Technologies

28.

Data sets created by the convergence of various devices, equipment, and systems and that are too complex for traditional data processing software. Learn more in: Knowledge Management and the Digital Native Enterprise

29.

The concept that near-infinite storage and computer processing has permitted the mass collection and storage of information about every aspect of our lives. Learn more in: Use of Big Data Analytics by Tax Authorities

30.

Datasets that are large and complex. Learn more in: Machine Learning in the Catering Industry

31.

In MICE industry, it is a common tool in following and tracking some useful information in regards to reaching the targeted meetings’ location history, subject details, national representatives, local ambassadors, hosting organizations and contact details of sponsors that supported those meetings, events or exhibitions. Learn more in: Personal Data Privacy and Protection in the Meeting, Incentive, Convention, and Exhibition (MICE) Industry

32.

Very large data sets. These are used in higher education in a number of ways. Most relevant to this chapter are the ways they are used to anticipate students’ needs, choices, risks, etc. Learn more in: Foundational Theory: A New Conceptualization of Relevant Ideas

33.

Datasets of diverse types and formats with various non-standard properties, such as large volume, enormous complexity, or heterogeneous quality. Learn more in: Ethical Aspects of Information-Based Medicine (With a Focus on Mental Health)

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Is data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: Big Data Analytics for Smart Airport Management

35.

The volume of data that is just beyond technology’s capability to store, manage and process efficiently. Learn more in: Big Data Issues and Challenges

36.

Datasets whose size, speed and variety are beyond the ability of typical database software tools to capture, store, administer, and analyze. Learn more in: Current and Future Years of E-Commerce

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Consists of large data sets that can be analyzed via computers to reveal trends, patterns, and associations, made up of five key parts: volume, variety, velocity, veracity, and value. Learn more in: Data Analysis and Integration in Healthcare

38.

Extremely large set of data which is used to extract some meaningful information. Learn more in: A Generalized Overview of the Biomedical Image Processing From the Big Data Perspective

39.

A term that defines a large dataset that produces in the size of collected data over time. It refers to the size of dataset that surpasses the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has huge, more diverse, and multifaceted structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with the commonly known attributes: high-volume, -velocity, and –variety information assets. Learn more in: What Do Facebook Users Feel About Facebook Advertising?: Using an Experience Sampling Method (ESM) to Explore Their Digital Advertising Experiences

40.

The term that represents data sets that are extremely large to handle through traditional methods. Big data represents information that has such a high volume, velocity, variety, variability, veracity and complexity that require specific mechanisms in order to produce value from it. Learn more in: Security and Privacy Issues of Big Data

41.

Is extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Digital Technology Advancements in Knowledge Management in Domestic Tour Products in the Russian Federation: Theoretical and Methodological Aspects

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Big data is a huge amount of data that able to capture, link, collect, store, analized, and organize to provide value for the users. Learn more in: Learning Management System 2.0: Higher Education

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Data that is so large and complex that it cannot be processed using traditional data processing tools or applications. Learn more in: Distributed Programming Models for Big Data Analytics

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It is the form of the data that has been analyzed, classified, and transformed into a meaningful and processable form. Learn more in: Innovation in Sustainability of Tourism After the COVID-19 Pandemic

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Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Real World Applications: Using Technology to Improve Supply Chain Management and Total Asset Visibility (TAV)

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Large or complex datasets that are too difficult to easily analyze with traditional processes including the utilization of predictive analysis for decision making. Learn more in: New Visual Literacies and Competencies for Education and the Workplace

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Is an extremely large amount and varied data sets that may be analyzed computationally to unearth valuable information patterns, trends, relations, and associations, especially relating to human treatments, behaviour and interactions that can help institutions make informed decisions. Learn more in: Unleashing the Convergence of Cloud Computing With Internet of Things: Drivers for Integration

48.

As with the concept of “artificial intelligence”, there is a great variety of definitions that different authors have proposed for the concept of Big Data, a term whose use is relatively recent. A simple way to define it is as large sets of data, structured or unstructured, that can come from different sources and in different formats and that have the potential to be analyzed by computer techniques to extract information that is difficult to obtain directly. Learn more in: Information Technology in the Search for Employment: Artificial Intelligence to Find the Best Job

49.

Big data refers to the very large and diverse sets of structured, semi-structured and unstructured digital data from different sources that accumulate and grow very rapidly on continuous basis. Big data technologies enable enhanced decision making in various types of businesses. Learn more in: Big Data in Business: Digital Transformation for Enhanced Decision-making and Superior Customer Experience

50.

Very large sets of data that can only be stored, used, and analyzed with the help of technological tools and methods. Learn more in: A Framework for Longitudinal Analysis of University Spin-Offs

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Data sets that are too large and complex to handle with traditional information technology tools. Learn more in: Big Data Transforming Small and Medium Enterprises

52.

A term that describes the large, diverse set of information. Learn more in: Artificial Intelligence and K-12: How to Explain?

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Enormous amount of unstructured, heterogeneous and distributed data that is constantly changing over time and depending on the surrounding context. Big data can be seen as both a resource and a process, both of which are linked to the different interactions occurring on the web. Learn more in: Big Data, Semantics, and Policy-Making: How Can Data Dynamics Lead to Wiser Governance?

54.

Big data is a discipline that deals with methods for analysing, methodically extracting information from, or otherwise dealing with data volumes that are too massive or complicated for typical data-processing application software to handle. Learn more in: Analysis of Big Data

55.

High-velocity, valuable, and/or multi-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big Data necessitate new forms of processing to deliver high veracity (and low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization. Learn more in: Big Data Mining and Analytics With MapReduce

56.

A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.” Learn more in: Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

57.

The term is used for large and/or complex sets of structured or unstructured data that cannot be processed in traditional ways. As a concept, big data is defined around the four V’s: volume (scale of data), velocity (analysis of streaming data), variety (different forms of data), and veracity (uncertainty of data). Learn more in: New Perspectives on Sustainable Healthcare Delivery Through Web of Things

58.

Very large sets of data that can only be stored, used, and analyzed with the help of technological tools and methods. Learn more in: Marketing and Technologies Platforms in Smart F-Store

59.

The size, speed and sorts of data that exceed an organization’s traditional database capacity to access, integrate, store, and analyze for accurate and timely decision making. Learn more in: Big Data Analytics for Business

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A buzzword for huge amounts of data. Learn more in: Anonymity and Pseudonymity in Data-Driven Science

61.

Is defined as the processing of massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. The data are very unstructured and analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. Learn more in: Big Data Analytics in the Healthcare Industry

62.

An extremely large and complex data set of structured and unstructured data that cannot be processed using traditional data processing applications. Learn more in: Spotted: Connecting People, Locations, and Real-World Events in a Cellular Network

63.

Broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. Learn more in: Living inside the NET: The Primacy of Interactions and Processes

64.

Big data frequently refers to data sets with sizes away from the ability of frequently used software tools to confine, curate, administer, and process data contained by a supportable elapsed instance. Big data philosophy encompasses shapeless, semi-structured and prepared data, however the main focal point is on unstructured data. Learn more in: Big Data Analytics and IoT in Smart City Applications

65.

The broad spectrum of data that have appeared throughout the development of the global information society. Learn more in: Big Data and Simulations for the Solution of Controversies in Small Businesses

66.

Is a term referring to an extremely large collection of data, typically in the petabyte range or larger. The data is only considered “big” if it taxes current state-of-the-art computing capabilities. Learn more in: Artificial Neural Networks in Medicine: Recent Advances

67.

Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. Learn more in: Awareness of Big Data Usage and Applications Among Librarians in Zimbabwe

68.

Data set that is extremely large in size and mostly unstructured in nature. Learn more in: Education in the Era of Industry 4.0: Qualifications, Challenges, and Opportunities

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Big data is defined as a large amount of data sets that cannot be analyzed and managed with traditional data processing tools. Learn more in: Digital Art Events and Digital Art Museums

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Data with at least one of the ten big characteristics, consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: Enhancing Airport Business Services Using Big Data Analytics

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Big data is a field that deals with methods for analyzing and methodically extracting information from, or otherwise dealing with data volumes that are too large or complicated for typical data-processing application software to handle. Learn more in: Abductive Strategies in Human Cognition and in Deep Learning Machines

72.

Literally means a gigantic volume of data. Therefore, it is usually defined through five Vs: volume, velocity, variety, value, and veracity. Volume comes from the growing increase in data with which the Web is fed today. Velocity refers to the increased scalability of the computer that allows for faster processing of data. Variety accounts for the heterogeneity of data types. Value implies the concern with the reliability of the data and, finally, Veracity corresponds to the distinction that must be established between false and true data. Learn more in: The Ambivalences of Smart Cities

73.

Datasets characterized by Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value. Learn more in: Big Data for Digital Transformation of Public Services

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It refers to the possibility of analyzing and systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Learn more in: How Artificial Intelligence Can Enhance Predictive Maintenance in Smart Factories

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It is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. Learn more in: Business Intelligence

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Is a combination of structured, semistructured, and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. Learn more in: The Incorporation of Big Data in Mathematical Training for the Fourth Industrial Revolution

77.

It refers to the possibility of analyzing and systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Learn more in: Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services

78.

Term used to describe the set of technologies and methods of analysis of massive data. The term indicates the ability to analyze and relate a huge amount of data to discover the links between different phenomena and formulate predictions. Learn more in: From the Data to the Statistical Analysis of Football: The Case of the Italian Serie A League

79.

Large amount of data, in many different formats, that flows rapidly in real time. This data should undergo some sort of analysis in order to extract useful information or knowledge. Learn more in: Opportunities and Challenges of Big Data in Healthcare

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A large quantity of data engendered by interconnected Internet of things in real time. Learn more in: Review of the Role of the Internet of Things (IoT) on the Consumer Market: Focusing on Smart Tourism, Healthcare, and Retailing

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Large and complex data sets which cannot be adequately processed using traditional data processing software or techniques. New techniques of statistics and machine learning have been developed to deal with big data. Learn more in: Big Brothers Are Seducing You: Consumerism, Surveillance, and the Agency of Consumers

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Gartner defines Big data as high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization (The importance of Big Data: A Definition. Douglas L). Learn more in: New Payment Models and Big Data Analytics

83.

The evolving term that describes a large volume of structured, semi-structured and unstructured data that has the potential to be mined for information and used in machine learning projects and other advanced analytics applications. Learn more in: Big Data Analytics in Supply Chain Management

84.

The conventional techniques to store data are relational databases and sequential data processing. These techniques are efficient to handle and process limited data size. However, in today's scenario, there is very huge data been generated, through these conventional mechanisms, whereas fail to handle huge volumes of data. As a solution to this problem, big data mechanisms and tools are effective to handle these huge volumes of data sets. Learn more in: Enabling Technologies for IoT: Issues, Challenges, and Opportunities

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The big data is data that contains greater variety, arriving in increasing volumes and with more velocity. Learn more in: Digital Transformation Stemming From a Business Assessment of Construction Industry 4.0

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Data that contains greater variety, arriving in increasing volumes and with high velocity. Learn more in: Oracle 19c's Multitenant Container Architecture and Big Data

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The capability to manage a huge volume of disparate data, at the right speed and within the right time frame, to allow real time analysis and reaction. Learn more in: BIG Data: An Enabler in Developing Business Models in Cloud Computing Environments

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A data set this is too large for “standard” software programs, and is generally used to identify large trends, and predict behaviors and outcomes. Learn more in: Internet Privacy

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Large data sets that are analysed using a computer to reveal patterns, trends, and relations pertaining to human behaviour and interactions. Learn more in: Emerging EdTechs Amidst the COVID-19 Pandemic: Cases in Higher Education Institutions

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A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.” Learn more in: First of All, Understand Data Analytics Context and Changes

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Traditional technologies of data processing, have difficulties to handle in tolerable time a large dataset. Learn more in: Big Data Analytics for Intrusion Detection: An Overview

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An exponentially increasing volume of heterogeneous data which is differentiated from traditional data based on its volume, variety, veracity, velocity and value. Learn more in: Artificial Intelligence, Big Data, and Machine Learning in Industry 4.0

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Big data refers to large amounts of heterogeneous data that cannot be easily processed using conventional database techniques. Big data characteristics include volume, variety, and velocity of data. This data is generated by many sources including smart sensors, smartwatches, wristbands, medical equipment/devices, tablets, mobile phones, web usage records, social media, and simulation. Learn more in: Blockchain Technology in the Automotive Industry: Use Cases and Statistical Evaluation

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Refers to large data sets. The definition of the term may also include the specific tools for their processing and structured or unstructured analysis that facilitate business decision making by revealing new patterns, associations, and trends. Learn more in: Research Trends in Information Systems From the Management Discipline Based on Co-Occurrence Analysis

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A collection of models, techniques and algorithms that aim at representing, managing, querying and mining large-scale amounts of data (mainly semi-structured data) in distributed environments (e.g., Clouds). Learn more in: Data Science and Distributed Intelligence

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Unconventionally large data streams from many sources to a bunch of (not necessarily known) recipients. Learn more in: Generating Big Data in (Micro)Economics

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Big data – a simple word turned into a technical jargon now-a-days to many common people because of its extensive use in the computing world with both its implicit and explicit meanings. The connotation of the term does not mean only the size of the raw facts the concerned people have to deal with, it also gives an impression about the scope and importance of those facts, budget, tools, technologies, and expertise to process those unstructured facts, and an apprehension of the utility of the information to be derived from those raw facts. Learn more in: Emerging Tools and Technologies in Data Science

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A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.” Learn more in: Big Data, Who Are You?

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Very large sets of data that are produced by people using the internet, and that can only be stored, understood, and used with the help of special tools and methods. Learn more in: E-Court: The Future of Commercial Dispute and the Quality of Judicial Processes in Indonesia

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A collection of data sets which are too large and complex to process using traditional methods or techniques. Learn more in: Supply Chain Analytics in the Era of Big Data

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A large-scale data collection that greatly exceeds the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis. Learn more in: Smart Management for Digital Transformation in China

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Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Learn more in: Segmenting Big Data Time Series Stream Data

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The term that represents data sets that are extremely large to handle through traditional methods. Big data represents information that has such a high volume, velocity, variety, variability, veracity and complexity that require specific mechanisms to produce real value from it in a timely way. Learn more in: Intelligent Management and Efficient Operation of Big Data

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It is the technology that uses solutions based on algorithms that capture and cross-data organized, which allows the storage and retrieval of data of various formats at a high scale. Learn more in: Applying a Methodology in Data Transmission of Discrete Events From the Perspective of Cyber-Physical Systems Environments

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Digital data whose massive scale and complex, unstructured content defy analysis with conventional tools, but which can increasingly be put to useful purposes through emerging data processing technologies. Learn more in: Assessment in Academic Libraries

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The use of very large and complex data sets for business analytics. Big Data sets are usually defined by their size (exceeding the capability of common statistical software and metods), complexity (as they can contain data of multiple types, including numbers, text, image, etc.), velocity (as these sets can be collected and analyzed in a continuous fashion, e.g. in case of data capturing the patterns of use of an application by clients). Learn more in: Digital Transformation in Banks of Different Sizes: Evidence From the Polish Banking Sector

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A set of data whose size makes it difficult for it to be recorded, managed and analysed by using conventional tools. Learn more in: Content Bubbles: How Platforms Filter What We See

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Large collections of either structured, semi-structured, or unstructured data that is processed by computer applications. Learn more in: Digital Humanities Strategy

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Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Cyber Security in Health: Standard Protocols for IoT and Supervisory Control Systems

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Voluminous amount of structured, semi-structured and unstructured data that has potential to be mined for information. Learn more in: Price Strategies in a Big Data World

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It refers to the emerging technologies that are designed to extract value from data having four Vs characteristics; volume, variety, velocity and veracity. Big data is set to affect the future network traffic and hence the network architectures. Learn more in: Future Networked Healthcare Systems: A Review and Case Study

112.

Big data is a term that refers to massive, various amounts of data that are accumulating at an exponential rate. Learn more in: Big Data Analytics for Search Engine Optimization in Intelligent Business

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The whole of the processable form of high-volume data obtained from different sources. Learn more in: Emerging Auditing Perspectives in the Age of the Fourth Industrial Revolution

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Is a recurring term when talking about technology and digitalization, its involvement in many areas of life begins to be relevant. Big Data refers to the management and analysis of huge volumes of data, which due to the magnitude of the information, exceed the capacity of data processing and should be treated in a special way. Learn more in: Big Data and Digital Tools Applied to the Teaching: Learning in Graduate Programs in Mexico

115.

Is data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: The Nature of Intelligent Analytics

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Is a field that deals with analysis, information extraction and visualization from very large data sets that are too complex to be processed by traditional systems. Learn more in: Circular Economy in Energizing Smart Cities

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Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Learn more in: Big Data Time Series Stream Data Segmentation Methods

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Extensive amount of data which cannot be gathered, saved, or converted by traditional instruments such as word processor within a given time span. Learn more in: Smart Tourism in Destinations: Can It Be the Way Forward?

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Sum of the digital data gathered. Learn more in: Circular, Smart, and Connected Cities: A Key for Enhancing Sustainability and Resilience of the Cities

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data which exceeds the processing capabilities of traditional database systems because of greater volume, velocity and variety of the data but which also has the potential to become a source of great value for the organization collecting it. Learn more in: Business Intelligence through Analytics and Foresight

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The term is used for massive amounts of increasing data, structured or unstructured data, which can be processed and analyzed through various techniques/algorithms. As a concept, big data is defined around the five V’s: volume (scale of data), velocity (analysis of streaming data), variety (different forms of data), veracity (uncertainty of data), and visibility (accessed from the disparate geographic location). But in the last years, another V is added: value. Learn more in: New Perspectives on Industrial Engineering Education

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Exceedingly large volumes of data to be analyzed. Learn more in: Big Data, Dashboards, and Data-Driven Educational Decision Making

123.

A large volume of structured, semi-structured, and unstructured data that come from different sources with the difficulties of storage and analysis. Learn more in: AI-Driven Big Healthcare Analytics: Contributions and Challenges

124.

Data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: The Elements of Intelligent Business Analytics: Principles, Techniques, and Tools

125.

High-velocity, valuable, and/or multi-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big data necessitate new forms of processing to deliver high veracity (& low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization. Learn more in: Big Data Analytics and Mining for Knowledge Discovery

126.

Is extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies for Sustainable Farming and Smart Agriculture

127.

Big data is a vast collection of information that continues to grow exponentially over time. It's a data set that's so large and complex that traditional data management tools can't store or analyze it efficiently. The difference between big data and regular data is that big data is substantially greater. Learn more in: Integration of Blockchain and IoT (BIoT): Applications, Security Issues, and Challenges

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Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. Learn more in: Supply Chain Analytics: Challenges and Opportunities

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Refers to data sets that are too big to be handled by regular storage and processing infrastructures. Learn more in: The Impact of Big Data on Security

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Big and complex data sets that necessitates advanced processing software and hardware. Learn more in: Industry 4.0 Technologies Used in Project Management

131.

High-velocity, high-value, and/or high-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big data necessitate new forms of processing to deliver high veracity (& low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization. Learn more in: Big Data Analysis and Mining

132.

Big Data is mainly defined by its 3Vs fundamental characteristics. The 3Vs include Velocity (data are growing and changing in a rapid way), Variety (data come in different and multiple formats) and Volume (huge amount of data is generated every second). Learn more in: Big Data Security: Challenges, Recommendations and Solutions

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Big data is data that contains huge, variable and growing data. Learn more in: Management of Big Data Projects: PMI Approach for Success

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A dataset that is larger than a typical dataset that requires unique tools, techniques, and technologies to store, manage, and analyze data. Learn more in: Big Data Adoption: A Comparative Study of the Indian Manufacturing and Services Sectors

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Very large and complex datasets that cannot be manipulated by traditional data processing methods. Learn more in: Feature Selection Algorithm Using Relative Odds for Data Mining Classification

136.

A term presenting to datasets that own big amounts of data (volume), various data (variety), and rising speed of generation (velocity). Learn more in: Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

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It is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. Learn more in: Business Intelligence

138.

The use of extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, that can be used to describe human interactions which can be used to assess learning, evaluate marketing behavior, determine the effects of a policy and other forms of assessment. Learn more in: AI and AR: A Copacetic Approach in the New Educational Environment

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Data generated in the large volume, diverse, complex, longitudinal, and/or distributed form. Learn more in: Cluster Analysis as a Decision-Making Tool

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Big data is defined as a large amount of data sets that cannot be analyzed and managed with traditional data processing tools. Learn more in: Big Data, Artificial Intelligence, and Their Implications in the Tourism Industry

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It is a term used in the field of Information Technology that difines a large volume of data. It can be related to the analisys and interpretation of a variety of data. It is related as the instrument used in Data science. Learn more in: Academy and Company Needs: The Past and Future of NLP

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Indicates the huge volume of data sets used computationally to expose beneficial patterns or trends. Learn more in: Real-Time Problems to Be Solved by the Combination of IoT, Big Data, and Cloud Technologies

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Very large sets of data that can only be stored, used, and analyzed with the help of technological tools and methods. Learn more in: UX Challenges and Best Practices in Designing Web and Mobile Solutions

144.

Is extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Big Data Analytics and Internet of Things in Industrial Internet in Former Soviet Union Countries

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Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications. Learn more in: Big Data, Knowledge, and Business Intelligence

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Data obtained from variety of sources and demonstrate the characteristics of high volume, high velocity, and high veracity that can be used in creating value by using analytical methods. Learn more in: Integrating Big Data to Smart Destination Heritage Management

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Big data refers to the situation when the dataset exhibits several characteristics, such as high volume, high variety, and high data processing velocity ( Choi et al., 2018 ). Learn more in: The Use of Big Data in Marketing Analytics

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Big data, obtained automatically by advanced Web technology, digital sensors, and so forth, refers to data so large and complex that they create significant challenges for data management and analysis tools. Learn more in: Large-Scale LP in Business Analytics

149.

Is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Learn more in: Big Data-Driven Smart University Architecture and Strategy Development: A Case Study

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Large volumes of structured, semi-structured and unstructured data from one or more sources, created and analyzed through innovative forms of information processing to develop insights on patterns, especially on human behaviour and corporate interactions, resulting in better decisions, strategic business decisions, and process adaptation. Characterized by high volume, high velocity and/or high variety. Learn more in: Digital Transformation Journeys in a Digitized Reality

151.

The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.” Learn more in: Investigating the Factors for Predictive Marketing Implementation in Algerian Organizations

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An extremely large database which generally defies standard methods of analysis. Learn more in: Teaching and Using Analytics in Management Education

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Big Data includes datasets that are too large and complex to manipulate or interrogate with standard methods or tools. It is characterized by ‘‘three Vs’’: Volume, Velocity and Variety ( Cao et al., 2015 ). Learn more in: Big Data and Its Role in Facilitating the Visualization of Financial Analytics

154.

Big data has a broad concept that includes large, comprehensive and always available data, which is nourished by various data sources such as information-sensing mobile devices, software logs, digital images and videos, GPS signals and wireless communication networks or so on. Learn more in: Review of Big Data Applications in Finance and Economics

155.

A term that describes a large dataset that grows in size over time. It refers to the size of dataset that exceeds the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has large, more varied, and complex structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with their high-volume, -velocity and –variety information assets. Learn more in: Social Media, Political Mobilization, and Citizen Engagement: A Case Study of the March 18, 2014, Sunflower Student Movement in Taiwan

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Big data can generally be characterized by a set of V’s: volume, variety, velocity, value, and veracity. Learn more in: Big Data and Analytics: Application to Healthcare Industry

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Is a set of data that enables the coordination of the services offered through the Internet, organizing and classifying customer data. Learn more in: Management and Marketing Events in a Digital Era: Opportunities and Challenges

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is huge data sets that may be analysed computationally to discover patterns, trends, and associations, especially relating to human behaviour and interactions. Learn more in: Smart Hotels and Technological Applications

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Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Encoded Memory: Artificial Intelligence and Deep Learning in Architecture

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Technologies for collecting, processing, and storing significant arrays of heterogeneous information; are the basis for the development of machine learning and artificial intelligence algorithms, solving analytical problems and optimizing business processes. Learn more in: Digital Transformation of Small and Medium Businesses

161.

It is a large, more complex data set, especially from new data sources data that contains greater variety, arriving in increasing volumes and with more velocity. Big data can be used to address business problems that cannot be dealt with by simple data collection. Learn more in: How Can Technology Advance the Public Administration Discipline in Higher Education?: A Comprehensive Analysis of the U.S. Scenario

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A term for data sets that is so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision-making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk. Learn more in: Critical Infrastructure Protection in Developing Countries

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Unstructured date with five characteristics: volume, velocity, variety, veracity, and value. Learn more in: Big Data Security Management

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Huge amounts of unstructured data (e-mail messages, internet traffic, etc.) and structural data (e.g., data bases) the analysis of which is not possible by means of traditional analytical tools. Learn more in: Non-Technological and Technological (SupTech) Innovations in Strengthening the Financial Supervision

165.

Big data is a general term used to describe the voluminous amount of unstructured and semi-structured data a company creates, data that would take too much time and cost to load into a relational Database for analysis. Learn more in: Big Data Analytics Demystified

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Big data can be defined as larger, more complex data sets arising from new data sources, and the techniques to analyze and extract meaning from it. Learn more in: How Artificial Intelligence Is Impacting Marketing?

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Huge amount of unstructured data, generated and retrieved digitally. Learn more in: Role of Indian Print and Digital Media in Learning Analytics: A Tool to Sustain Education in the New Normal

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It is a mass of data that is constantly flowing through various channels at a great speed and in a wide variety of structures and is difficult to process with traditional database systems. Learn more in: Digital Transformation Journey of HR: The Effect of Big Data and Artificial Intelligence in HR Strategies and Roles

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Massive amount of structured or unstructured data generated from social networks and Google searches. Learn more in: Adoption of Industry 4.0: Analysis and Action of Mexican Case

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A collection of data that has become so large and complex that it is difficult to process using traditional database and software applications. Learn more in: Teaching a Data Mining Course to MBA Students

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Big data is a combination of structured, semi structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. Learn more in: Key Strategic Drivers for Business Digital Transformation: Systematic Literature Review

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It is a research field studying methods and algorithms for too large and complex data sets extraction, processing, and analysis. Learn more in: Modelling in Support of Decision Making in Business Intelligence

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Huge amount of information sets that can be computationally analyzed to disclose patterns, trends, and associations, particularly with respect to human behavior and related issues. Learn more in: Mutual Relationships Between Digital Transformation and Leadership

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It is considered an area of knowledge that studies how to obtain, process, and analyze data and information considering a large magnitude of structured and unstructured data. Learn more in: Digital Transformation of the Real Estate Segment With Big Data and Marketing Analytics: A Case Study From QUOT

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Large volumes of structured and unstructured data whether homogeneous or heterogeneous data. In general, it is not the amount of data that matters the most but what organizations intend to do with the data. In this sense, the goal of Big Data analysis is to provide some insights that may lead to better decisions and strategic business support. Learn more in: Big Data in Massive Parallel Processing: A Multi-Core Processors Perspective

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Big Data may be defined as a collection of huge data that becomes challenging to process through available conventional data processing tools and applications. It involves high-volume, high-velocity and high-variety of information resources that require economical, productive and new methods of information processing for improved and enhanced decision making. Learn more in: Importance of Big Data

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Technologies for collecting, processing, and storing significant arrays of heterogeneous information; are the basis for the development of machine learning and artificial intelligence algorithms, solving analytical problems and optimizing business processes. Learn more in: Electronic Government

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Dataset with a very large number of units. Learn more in: Understanding Big Data and Techniques in Cultural Tourism

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The broad spectrum of data that have appeared throughout the development of the global information society. Learn more in: Automation of Marketing Processes and the Discovery of Knowledge for CRM

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A means of identifying structured and unstructured data of large volumes and considerable diversity which is hard to process using traditional methods, including structured data, media, and random objects. Learn more in: Is Artificial Intelligence a New Dawn or Challenge for Corporate Decision Making?

181.

A term that describes a large dataset that grows in size over time. It refers to the size of dataset that exceeds the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has large, more varied, and multifaceted data structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with their high volume, velocity, and variety information assets. Learn more in: Political Mobilization Strategies in Taiwan's Sunflower Student Movement on March 18, 2014: A Text-Mining Analysis of Cross-National Media Corpus

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Broadly defined as information whose size is beyond the ability of typical data and information software tools to capture, store, manage, and analyze it. Learn more in: Information Governance Maturity Model: Should Retention Be Rethought?

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Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Learn more in: Big Data: An Emerging Field of Data Engineering

184.

Data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: Intelligent Big Data Analytics: A Managerial Perspective

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A type of structured and un-structured data that is characterized by its big volume usually measured in Terabytes, Exabytes or Petabytes and requires specific hardware and software tools for processing. Learn more in: New Advances in E-Commerce

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refers to massive complex structured and unstructured data sets that are rapidly generated and transmitted from a wide variety of sources. Will play a key role in the future of fast-moving industry like fashion (Kim & Lee, 2018). Learn more in: Big Data Optimization in Zara: How Zara Will Optimize Its Process With Big Data Due to the COVID-19 Situation

187.

Relatively large data-sets that originate from diverse sources and that are not analyzable through standard methods and tools. Learn more in: Big Data and Innovation in the Delivery of Public Services: The Case of Predictive Policing in Kent

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Is data sets that cannot be captured, managed and processed by general computers within an acceptable scope (Chen, Mao and Liu, 2014; Song and Liu, 2017). Learn more in: Artificial Intelligence Applications for Event Management and Marketing

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Evolving term that describes a large volume of structured, semi-structured and unstructured data that has the potential to be mined for information and used in machine learning projects and other advanced analytics applications. Learn more in: The Digital Enterprise as an Emerging Landscape for Universities and Their Operation

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Huge and complex quantities of data sets that can be analyzed to reveal trends, patterns and behaviors. The resulting insights can be used for better decision making or for the development of new products/services. Learn more in: Smart City = Smart Citizen = Smart Economy?: An Economic Perspective of Smart Cities

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It is a field of study by which it analyses the various systematic extracted information to take a decision. Learn more in: Neuromarketing Trends and Opportunities for Companies

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Data that exceeds the processing capacity of conventional database systems. Learn more in: Business Analytics and Big Data: Driving Organizational Change

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Large amounts of structured and unstructured data that is too complex to be handled by standard data-processing software. Learn more in: Artificial Intelligence a Driver for Digital Transformation

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Massive amounts of information which is difficult to store, manage, and analyze. Learn more in: The Role of Big Data in Digital Marketing

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Using sophisticated analytical tools, big data analytics is the study of huge and varied data sets. Learn more in: Data Analytics in Electric Vehicles

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Refers to large volumes of data, dealing with the computational techniques to manage them. Learn more in: Nature-Inspired Techniques for Data Security in Big Data

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The datasets that are of large size and have a greater complexity. Learn more in: Mining Big Data and Streams

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A big data set is one that is too large or complicated for standard data processing techniques. Learn more in: Contemporary Trends and Technologies in Research Libraries: An Overview

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Volumes of data that are too large and too complex to be processed by traditional data-processing application software. Learn more in: Expansion of Technology Utilization Through Tourism 4.0 in Slovenia

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A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.” Learn more in: Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

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Very large data sets which can be analyzed computationally to find patterns, trends, and associations, based on human behavior and interactions. Learn more in: Big Data Analytics in Retail Supply Chain

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Big data is a collection of data that is larger in volume,has variety and cannot be processed by traditional tools. Learn more in: Big Data Analytics in E-Governance and Other Aspects of Society

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Large complex data sets that can be analyzed on a computer to inform patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Mobile Security in Low-Income Households' Businesses: A Measure of Financial Inclusion

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An analysis technique that systematically extracts information from structured or unstructured data by means of advanced algorithms and supercomputing competence. Learn more in: The Role of the Hospitality Industry in Preventing Human Trafficking

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It is a phenomenon that involves a set of tools, technologies and techniques that aim to process huge amounts of data of all types in order to exploit the information contained and obtain some kind of advantage. Learn more in: New Trends in Databases to NonSQL Databases

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The set of methodologies and technologies that allow the capture, management and exploitation of considerable amounts of data within a tolerable period of time. Learn more in: Big Data and Web Intelligence for Condition Monitoring: A Case Study on Wind Turbines

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Extremely large data sets that may be analyzed to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: The Benefits of Technology in Healthcare Management: Lessons Learned During the COVID-19 Pandemic

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A field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Learn more in: Learning Path Recommendation Method Based on Knowledge Map

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A term used to describe large volumes of data of both structured and unstructured data type. Gartner defines it as data with three dimensions of Volume, Velocity and Variety. Learn more in: Big Data Computing Strategies

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Data that are being generated by the environment, namely, by nature, technology and humans. Sometimes these data exist as a side effect of some process, for example, the vehicles' positions on the roads are generated automatically as a side effect of the traffic. Learn more in: Visualization and Storage of Big Data for Linguistic Applications

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Mainly was used as a term to define a large or complex volume of structured, semi-structured and unstructured data that can be mined for information and used in machine learning projects and other advanced analytics applications. Learn more in: The Integrated Tourism Analysis Platform (ITAP) for Tourism Destination Management

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Extremely large data sets that can be analyzed in order to reveal patterns, trends and associations. Learn more in: Social Media Applied to Tourism and Hospitality: The Case of Hotels in the Porto Metropolitan Area

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This refers to large amounts of data from different sources; it is made up of all kinds of data (e.g., photos, music, text, figures and numerals, voice recording, videos, and a jumble of electronic data), which needs ordering in order to convey meaning to end-users. Learn more in: Intelligent Cities: A Compendious and Multidisciplinary Approach – Issues and Opportunities

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Term employed to describe large data sets, it is commonly used to describe analytical methods to obtain value from data. The proper use of Big Data may improve the decision making process. Learn more in: Big Data in Higher Education

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Big data is an “umbrella term” that describes data processing approaches in situations where enormous and/or unstructured datasets cannot be efficiently manipulated via traditional handling techniques (e.g., relational databases). Learn more in: Machine Learning in Healthcare: Introduction and Real-World Application Considerations

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A gargantuan dataset having the feature of high volume, variety, velocity, value, variability, and veracity which is captured through technologies like Optical Character Readers (OCRs), Smart Cards, Biometrics, Voice Recognition, Magnetic strips, etc. Learn more in: Product Innovation and Personalization via Social Media: Learnings From the Phenomenal Success of BYJUs

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Large or complex datasets that too difficult to easily analyze with traditional processes including the utilization of predictive analysis for decision making. Learn more in: Green Computing through Virtual Learning Environments

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Extremely large volume of data - both structured and unstructured - that inundates a business on a day-to-day basis and may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Evaluation of Strategic Opportunities and Resulting Business Models for SMEs: Employing IoT in Their Data-Driven Ecosystems

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Big data can be referred to as data characterized by high levels of volume (amount of data), variety (number of types), and velocity (speed). Learn more in: Industry 4.0 and Sustainability

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The generation of data has started to outpace the processing capabilities of the typical technology tools. Learn more in: The Transformation of Auditing From Traditional to Continuous Auditing in the Era of Big Data

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Large and complex datasets that require advanced data processing technology in order to be analyzed. Learn more in: From “Yucky” to “Yummy”: Drivers and Barriers in the Meat Alternatives Market

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The large amount of data that is being generated and needs to be processed by new digital tools and technologies. Learn more in: Internet Search Behavior in Times of COVID-19 Lockdown and Opening

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A large volume of data that may be analyzed computationally and systematically to reveal characteristics, patterns, trends, and associations in various fields. Learn more in: Data Visualization in Online Educational Research

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Complex multi-dimensional digital data sets. Learn more in: Big Data – Small World: Materializing Digital Information for Discourse and Cognition

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Big data is a combination of structured, semi structured, and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications. Learn more in: Blockchain Platform to Resolve Security Issues in IoT and Smart Networks

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The gigantic digital databases, held, acquired, and used by governments, companies or other organizations, and later analyzed through the use of algorithms. Learn more in: Is “Privacy” a Means to Protect the Competition or Advance Objectives of Innovation and Consumer Welfare?

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Large volume of data that inundates a business on a day-to-day basis. Learn more in: Business Applications of Big Data: Considerations for Not-for-Profits

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The current phrase used to describe the changes in the accumulation of data over the past decade; the distinguishing factors of which are volume (2.5 Exabyte’s per day), velocity (speed at which data is created), and variety (images, texts, videos, etc.). Learn more in: Business Analytics for Business Analysts in Manufacturing

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Unstructured data that does not fit into traditional tables found on the Internet—e.g. photos, videos and blog posts, among others. Learn more in: A Cognitive Analytics Management Framework (CAM-Part 2): Societal Needs, Shared-Value Models, Performance Indicators, Big Data, Business Analytics Models and Tools

230.

Large-scale data, often obtained from online environments. Learn more in: Determination of Guest Satisfaction by Text Mining: Case of Turkey Hotels

231.

Large volumes of structured, semi-structured and unstructured data from one or more sources, created and analyzed through innovative forms of information processing to develop insights on patterns, especially on human behaviour and corporate interactions, resulting in better decisions, strategic business decisions, and process adaptation. Characterized by high volume, high velocity and/or high variety. Learn more in: Digital Transformation Journeys in a Digitized Reality

232.

A term used to describe large volume of structured and unstructured data generated through multiple devices and platforms that require non-conventional handling and analytical tools to ascertain patterns and trends that are useful for effective decision making. Learn more in: Marketing Trends in the Digital Age: The Rise of New Marketing Paradigms (Virtual Marketplaces, Connectivity, and Advocacy)

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An exponentially increasing volume of heterogeneous data which is differentiated from traditional data based on its volume, variety, veracity, velocity, and value. Learn more in: Educational Data Mining and Learning Analytics in the 21st Century

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Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications. Learn more in: Data, Information, and Knowledge: Developing an Intangible Assets Strategy

235.

A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.” Learn more in: Big Data Applications in Business

236.

Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Learn more in: Approaches to Tackle Smart Cities Challenges in Brunei

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Data that exceeds that processing capacity of conventional database systems. Learn more in: Big Data Analytics: Trends and Case Studies

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Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Achieving Environmental Sustainability Through Industry 4.0 Tools: The Case of the “Symbiosis” Digital Platform

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The high number and complexity of the data records that are processed and stored by electronic applications. Learn more in: Enterprise Data Lake Management in Business Intelligence and Analytics: Challenges and Research Gaps in Analytics Practices and Integration

240.

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it's not the amount of data that's important. It is data with a huge size; a term used to describe a collection of data that is huge in volume and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently. In other words, it is a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Learn more in: Records Management in the Fourth Industrial Revolution: Challenges and the Way Forward

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Big data is a buzz word that refers to collections of data that are big considering three Vs: volume, velocity, and variety. Some authors refer two more Vs: veracity and value. Learn more in: Data Mining and Business Intelligence: A Bibliometric Analysis

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A concept mainly characterized by the volume, velocity and variety of data being generated. Learn more in: Real-Time Big Data Warehousing

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Datasets that are (1) extremely large, (2) generated by many different sensors and devices, and (3) collected and transmitted quickly and frequently. Learn more in: The Application of the Internet of Things in Managing Supply Chains

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Computationally analysed sets of data that is disclose complex associations, trends, and patterns for human interactions. Learn more in: Digital Citizenship and E-Government Integration: The Case of North Cyprus

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Various data types are brought together by recording large amounts of data, obtaining meaningful results. Big data analytics provides a potential competitive advantage that radically changes social, cultural, political, and economic relations. Learn more in: Big Data in Digital Media Platforms

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Large, dynamic and unstructured collections of data often distributed and streamed. Learn more in: Outlier Detection in Big Data

247.

Is data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market. Learn more in: Only Can AI Understand Me?: Big Data Analytics, Decision Making, and Reasoning

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This refers to extremely large data sets that are analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Internet of Things Application for Intelligent Cities: Security Risk Assessment Challenges

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Although there is no consensus on how much should be considered Big Data, it is possible to define it as the point at which the computing capabilities of a given piece of equipment are exceeded. Learn more in: Improving the K-Means Clustering Algorithm Oriented to Big Data Environments

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Data that is collectively too large and complex to be analyzed with traditional data mining techniques. Learn more in: Querying of Time Series for Big Data Analytics

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Huge Quantity of Data generated by Smart Devices. Learn more in: Big Data Analysis in IoT

252.

Big data is any data that is difficult to handle, and cannot be managed by the standard data processing software. The three popular terms associated with Big Data are Volume, Velocity and Variety known as the three V’s. Learn more in: Storage and Query Processing Architectures for RDF Data

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Any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. Learn more in: Big Data Processing and Big Analytics

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Large data sets that cannot be processed and analysed using traditional processing systems. Learn more in: Opportunities and Challenges of Using Big Data Applications in Institutions of Higher Learning Libraries and Research Institutions

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Refers to the large volume of data – both structured and unstructured – that a business deals with on a day-to-day basis. Learn more in: Social Media as Mirror of Society

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A term used to refer to the massive datasets generated in the digital age. Both the volume and speed at which data are generated is far greater than in the past and requires powerful computing technologies. Learn more in: Educational Data Mining: A Guide for Educational Researchers

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The massive amount of data which is identified by four characteristics including high volume, velocity, variety, and veracity. Learn more in: Overview of Big Data in Healthcare

258.

Vastly large data sets that cannot be analyzed through traditional data processing techniques are collectively known as Big Data. Organizations in possession of Big Data (majorly business organizations) are attempting to capture and analyze even more samples in order to make sound decisions based on human behavior. Learn more in: Need for Rethinking Modern Urban Planning Strategies Through Integration of ICTs

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Extremely large and highly complex data sets that can be analyzed computationally contextually which describes approximately contemporary to human interaction based on oneself emotion. Learn more in: The Effectiveness of Big Data in Social Networks

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There are large, complex, and unprocessed datasets that cannot be processed by traditional application but can offer knowledge and value if properly analyzed. Learn more in: The Big Data Era: Data Management Novelties for Visualizing, Exploring, and Processing Big Data

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A collection of huge amounts of real time data in very high volume, variety, and velocity in nature that cannot be managed effectively with traditional database management systems. Learn more in: The Potential and Capabilities of NoSQL Databases for ERP Systems

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The challenge of Big Data was initially posed by the aspects of increasing data volumes, data that needs to be rapidly handled and the increasing complexity of data structures in data processing. Learn more in: Data Science Methodology

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A large amount of data that can be utilized to find patterns related to a given outcome or output. Learn more in: The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance

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Stored data sets that can be manipulated and analysed to develop new interventions. Learn more in: Genetics and Public Health

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Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. Learn more in: Integrating Ontologies and Bayesian Networks in Big Data Analysis

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A computational domain for analysis and systematical extraction of information from too large/complex data sets, which cannot be already processed by the traditional application software (Big data, n.d.), useful also in media analysis and prediction. Learn more in: The Cognitive-Sentient Exploration of Mediated Reality: From Proto-Cognition/Epigenetic Informational Processes to Big Data-Assisted Prediction

267.

Are interesting high-velocity, high-value, and/or high-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big Data necessitate new forms of processing to deliver high veracity (& low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization. Learn more in: Big Data Mining and Analytics

268.

A business management term that refers to applications and technologies that are used to gather, provide access to, and analyze data and information about their company operations. Learn more in: Leadership for Big Data and Business Intelligence

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A term commonly used to describe vast and complex volumes of unstructured and structured dataset generated and conveyed from different sources. Learn more in: Integrating Knowledge-Driven and Data-Driven Methodologies for an Efficient Clinical Decision Support System

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A term used to describe large-scale data sets that include multiple kinds of demographic, behavioral, or performance data. Sometimes used to describe the unique kinds of data analysis that can be performed on these data sets. Learn more in: Expect What You Inspect: A Worked Example of Dashboards That Support Continuous Quality Improvement in Medical Education

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Is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. Learn more in: Big Data, Data Management, and Business Intelligence

272.

Big data is a general term used to describe the voluminous amount of unstructured and semi-structured data a company creates, data that would take too much time and cost to load into a relational Database for analysis. Learn more in: Big Data Computing and the Reference Architecture

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It is the collection of such a huge amount of data (both structured and unstructured) with much complexity that traditional data management tool cannot store or process it efficiently. Learn more in: Big Data Analysis: Basic Review on Techniques

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Data that is received in high volume and it can be stored in databases, and it comes at a high speed with various data formats. Learn more in: Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree

275.

Big data is a combination of structured, semi structured, and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. Learn more in: Demystifying Federated Learning in Artificial Intelligence With Human-Computer Interaction

276.

Big data is a collection of data sets so large and complex that it becomes difficult to process using traditional analytics algorithms. The challenges include capture, creation, storage, search, sharing, analysis, and visualization. The 4 V’s of big data are Volume (large volumes of data), Velocity (speed of data generation), Variety (structured, unstructured), and Veracity (trust and integrity). Learn more in: Leveraging Applications of Data Mining in Healthcare Using Big Data Analytics: An Overview

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Datasets that are in large volume and high variety. Learn more in: Virtual Singers Empowered by Machine Learning

278.

Data featuring high volume, high variety, high velocity, high veracity, and high value. Learn more in: Social Media Data Into Performance Measurement Systems: Methodologies, Opportunities, and Risks

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Refers to the growth in the volume of structured and unstructured data, the speed at which it is created and collected, and the scope of how many data points are covered. Big data often comes from multiple sources and arrives in multiple formats. Learn more in: Value Propositions for Companies in Their Payment Ads in Networks: How to Generate Value and Return

280.

Data that exceeds the capacity of commonly used hardware and software tools to capture, store, and analyze within a tolerable elapsed time is considered big data. The three main characteristics of big data are volume, variety, and velocity. Learn more in: Overview of Big Data and Its Visualization

281.

A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Learn more in: Revenue Management in the Hotel Industry

282.

Refers to data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them. Learn more in: Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection

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A voluminous amount of structured, semi-structured, and unstructured data from various sources around us, such us Internet, sensors, nature, and society. Learn more in: High Performance Computing, Big Data, and Cloud Computing: The Perfect De Facto Trio or Converging Technological Mantras?

284.

Area of knowledge that studies how to treat, analyze, and obtain information from data sets that are too large to be analyzed by traditional systems. Learn more in: A Systematic Bibliometric Literature Review on Data Science in Marketing

285.

A huge and moving set of data of various varieties, structure, and format, produced by various sources, at various frequencies that must be processed quickly using specialized tools, other than common database management software. Learn more in: A Preliminary Framework to Fight Tax Evasion in the Home Renovation Market

286.

A term that describes large amount of data that could be structured, unstructured or real time and is difficult to be handled by traditional systems.It is identified by major 3 V’s: Volume, Variety and Velocity. Learn more in: Adapting Big Data Ecosystem for Landscape of Real World Applications

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This term emerged to solve the treatment and storage massive data that has specific defined features as speed, volume and variety. Learn more in: Smart Gateways for IOT-Factory Integration: Trends and Use Case

288.

An increasingly popular term that refers to the large amount of digital data that is being accumulated about individuals, where the data is voluminous, collected at great velocity and extremely variegated in detail offering a nearly precise identity narrative of the individual. Learn more in: The 2018 Facebook Data Controversy and Technological Alienation

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Are large data sets that are so big and complex that conventional computers are unable to process the data at all or not in real time. Additionally, big data refers to the use of large volumes of data to make business decisions or to predict customer choices in real time using customer data. Learn more in: Procurement Strategies for Digital Supply Chains: Concepts and Best Practices

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Data that contains greater variety, arriving in increasing volumes and with more velocity. Learn more in: Digital Transformation and Circular Economy for Sustainability

291.

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. Learn more in: IT Strategy Follows Digitalization

292.

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyse it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. Learn more in: Insight Into Big Data Analytics: Challenges, Recent Trends, and Future Prospects

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Is data that exceeds that processing capacity of conventional database systems. Learn more in: Big Data Problem, Technologies and Solutions

294.

A term used to refer to the study and applications of data sets that are too complex for traditional data-processing application software to adequately deal with. Learn more in: Use of Big Data in Aviation: New Opportunities, Use Cases, and Solutions

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Large amounts of information on a topic that are sometimes difficult to manage. It can be structured or unstructured. This term is often associated with Data Bases or Deep Learning. Learn more in: Generation of Synthetic Data: A Generative Adversarial Networks Approach

296.

Big data is a combination of structured, semi-structured, and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. Learn more in: Applications of Big Data Analytics in Healthcare Informatics

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Big data is a combination of structured, semi structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. Learn more in: Fog-IoT-Assisted-Based Smart Agriculture Application

298.

Is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Learn more in: How Visualisation and Interaction Can Optimize the Cognitive Processes Towards Big Data

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An extremely large dataset that cannot be analysed by traditional methods, and require distributed, highly paralelizable computing to extract the information. Learn more in: Learning Analytics

300.

Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Learn more in: The Role of Information Technologies to Adapt to a Global Pandemic: Digitalization Disruption in the New Renaissance

301.

A rea of knowledge that studies how to treat, analyze and obtain information from data sets that are too large to be analyzed by traditional systems. Learn more in: Future Challenges of Marketing Online-to-Offline (O2O)

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A large amount of information about an organization (both structured and unstructured) that cannot be analysed using traditional computing methods. Learn more in: The Effective Use of Digital Technology by SMEs

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Big data refers to large amount of transactional data which could be computationally retrieved and analsyed to understand hidden trends, patterns and various attribute relationships. Learn more in: Web 2.0 Mash-Up System for Real Time Data Visualisation and Analysis Using OSS

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Big and versatile data sets, that are hard to analyze using standard analytical methods. Analysis of big data enables to identify new information and gain knowledge by finding relationships between variables not visible before. Learn more in: Big Data Is Decision Science: The Case of COVID-19 Vaccination

305.

A numerous amount of information available on the internet, which can be utilized to figure out the problem in various fields. Learn more in: Big Data and Islamic Finance

306.

Is an extremely large amount and varied data sets that may be analyzed computationally to reveal valuable information like patterns, trends, relations, and associations, especially relating to human treatments and behaviour and interactions that can help institutions make informed decisions. Learn more in: Integrated Big Data E-Healthcare Solutions to a Fragmented Health Information System in Namibia

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The size, speed and sorts of data that exceed an organization’s traditional database capacity to access, integrate, store, and analyze for accurate and timely decision making. Learn more in: Integrated Data Architecture for Business

308.

Data sets that are too large and cannot sit on a single computer or storage device. Learn more in: How Can Education Use Artificial Intelligence?: A Brief History of AI, Its Usages, Its Successes, and Its Problems When Applied to Education.

309.

large databases of behavioural tracking as well as the tools required to handle these data. Ever-increasing databases and electronic recordings of individual activities and corporate and societal repositories. Cell phones, social media conversations, web browsing, GPS, CCTV cameras and use of credit/debit cards constantly leave traces of the individual. Learn more in: Omni-Channel Retail Information Systems

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It reflects extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: The Impact of Creating a Business Intelligence Platform on Higher Education: The Case of the American University in Cairo

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The large datasets that are enabled by IT systems which support, capture, and disseminate these data. Learn more in: Digital Technology Deployment in Multi-National Enterprises

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Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. There is not a common definition of big data, and big data are generally characterized by some properties such as volume, velocity, variety, and so on. Learn more in: Towards Massive RDF Storage in NoSQL Databases: A Survey

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It refers to a massive volume of structured or unstructured data that is difficult to be processed by traditional data management tools. Learn more in: A Survey on Intelligence Tools for Data Analytics

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The term used for the collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Learn more in: Population Health Management, Emerging Technologies, and the Science of the Individual

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The data with frequency of generation of the size petabytes / terabytes per second and is so huge in volume and not fit to be stored in the existing traditional relational database storage. Learn more in: NoSQL Databases

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Very large data sets generated predominantly by human interaction with digital systems and by digital systems interaction with the environment. Learn more in: Survey Research: Core Principles and Discussion Points

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Set of analytical techniques and methods for large volumes of data with the final obtaining of computing solutions. Learn more in: Data Science Process for Smart Cities

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Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The main characteristics of these datasets are volume, variety, and velocity. Learn more in: Improve Home Energy Management System by Extracting Usage Patterns From Power Usage Big Data of Homes' Appliances

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Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. Learn more in: IT Strategy Follows Digitalization

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Data that is too large to be stored using conventional data storage capabilities. Big data also includes non-traditional types of data such as images, text, weblog searches, and social media data. Learn more in: Cognitive Biases and Data Visualization

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Big data is term for massive datasets having large, more varied complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Learn more in: Big Data Mining Based on Computational Intelligence and Fuzzy Clustering

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A term for data sets that are so large or complex that traditional data processing applications are inadequate. Learn more in: Eight Tips for the Theme, “Data and Forecasts”

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Any data set that is too difficult to be handled by traditional database systems due to its size and complexity. Learn more in: Web Usage Mining and the Challenge of Big Data: A Review of Emerging Tools and Techniques

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Extremely large volume of data—both structured and unstructured—that inundates a business on a day-to-day basis and may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Internet of Things and Big Data-Driven Data Analysis Services for Third Parties: Business Models, New Ventures, and Potential Horizons

325.

A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.” Learn more in: What the 3Vs Acronym Didn't Put Into Perspective?

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The term for collections of data that are too large and complex to process using on-hand database management tools or traditional data processing applications. Learn more in: The Impact of Digital Resources on Scholarship in the Digital Humanities

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Big data is a term for dataset that are huge and complex to process by traditional data processing applications. Learn more in: Investigation on Deep Learning Approach for Big Data: Applications and Challenges

328.

A term that describes a large dataset that grows in size over time. It refers to the size of dataset that exceeds the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has large, more varied, and complex structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with their high-volume, -velocity and –variety information assets. Learn more in: Real-Time Bidding Advertising: Challenges and Opportunities for Advertising Curriculum, Research, and Practice

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Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Learn more in: Omni-Channel Retailing: Enriching Customers' Shopping Experience

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Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications. Learn more in: Big Data, Knowledge, and Business Intelligence

331.

Term used in information technology to refer to the processing of a large set of data that needs to be processed and stored. The initial concept was based on 3 Vs (speed, volume, and variety) and then some more Vs were gradually added. Learn more in: Municipal Solid Waste Management Using an Inclusive Technological Platform

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Huge amount of structured (text fields such as name and phone number) and unstructured (e.g., video, images, gestures, etc.) data in sizes that exceed the capacity of traditional tools to manage them satisfactorily. Learn more in: Integrating Big Data Analytics Into Retail Services Marketing Management: The Case of a Large Shopping Center in London, UK

333.

Refers to the 3Vs (i.e., Volume, Velocity, Variety) of data which is captured for actionable insights. Learn more in: Critical Success Factors of Analytics and Digital Technologies Adoption in Supply Chain

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The size, speed and sorts of data that exceed an organization’s traditional database capacity to access, integrate, store, and analyze for accurate and timely decision making. Learn more in: Integrated Data Architecture for Business

335.

Can be described large volume of data that are structural, semi-structural, and non-structural, and it provides valuable information for lots of research areas. It is often characterized by the 3V (volume, variety, and velocity), but it has continued to grow up with other characteristic components. Learn more in: Intelligent Big Data Analytics in Health

336.

A type of digital disruptive innovation that enhances decision making by using a machine with the capacity of analyzing extremely large data sets in order to find behavioral patterns, trends, etc. It can predict human behavior and its interactions. Learn more in: Disruptive Unicorn of Digital Innovations: A Challenge for University Professors

337.

Big data has four distinct characteristics (so called 4V features): the data volume is huge (volume); the data type is diverse meaning a mixture of structured data, semi-structured data and unstructured data (variety); the streaming data is very high (velocity); and uncertain or imprecise data (veracity). Learn more in: The Role of Big Data and Business Analytics in Decision Making

338.

A term that defines a large dataset that produces in the size of collected data over time. It refers to the size of dataset that surpasses the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has huge, more diverse, and multifaceted structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with the commonly known attributes: high-volume, -velocity, and –variety information assets. Learn more in: Optimization Models in Google Ads Campaigns

339.

Extremely large set of data which is used to extract some meaningful information. Learn more in: An Overview of Biomedical Image Analysis From the Deep Learning Perspective

340.

Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing the way businesses operate in many industries (Lee, 2017 AU147: The in-text citation "Lee, 2017" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). Learn more in: Big Data Analytics and Visualization of Performance of Stock Exchange Companies Based on Balanced Scorecard Indicators

341.

Datasets of various types and formats with various non-standard properties, such as large volume, enormous complexity, or diverse quality, usually obtained in real time. Learn more in: Managerial Decision Support in the Post-COVID-19 Era: Towards Information-Based Management

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Relating to the massive amount of data transacted online by all users in an undifferentiated way, through the use of any Internet-connected hardware, from any application or software. Learn more in: Open and Big Data as a Resource to the Development of Social Innovation Initiatives and Smart Territories

343.

A very large data set; used in a variety of fields to reach decisions. Learn more in: Algorithms and Bias

344.

Big data is a new concept that identifies large amounts of heterogeneous data that cannot be processed using conventional database techniques. Big data characteristics include volume, variety, and velocity of data. Big data is generated by many sources. Some of the most known are smart sensors, smartwatches, wristbands, medical equipment/devices, tablets, phones, web usage records, social media, and simulation. Learn more in: Technologies for Connected Government Implementation: Success Factors and Best Practices

345.

Is a field that deals with analysis, information extraction and visualization from very large data sets that are too complex to be processed by traditional systems. Learn more in: Industry 4.0-Based Large-Scale Symbiotic Systems for Sustainable Food Security in Namibia

346.

Big data is a conventional term used to describe the exponential increase and accessibility of structured and unstructured data. Learn more in: Challenges for Big Data Security and Privacy

347.

It is a massive volume of both structured and unstructured data that is difficult to process using traditional techniques. Learn more in: Analysis of Cutting-Edge Regression Algorithms Used for Data Analysis

348.

The term refers to the dataset that has large, more varied, and complex structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with their high-volume, -velocity and –variety information assets. Learn more in: Employing a Mixed Method to Explore Mobile Social Media Users' Big Data Privacy Concerns

349.

It is a term widely used for the unstructured data. People generally confused that what is big data, in simple term when we have lot of data of different kind of some particular problem we can say that its big data for example: crop data of rice plant it include all the aspect of rice like amount of water needed, time period, fertilizer requirement, height, width, etc. Learn more in: Smart Agriculture Services Using Deep Learning, Big Data, and IoT (Internet of Things)

350.

Data that cannot be processed using traditional data analytics software and infrastructure on a personal computer or on a data analytics server. Compared with traditional data, big data have greater volume, variety, and/or velocity than traditional data. Learn more in: Big Data Governance in Agile and Data-Driven Software Development: A Market Entry Case in the Educational Game Industry

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Applies to information that cannot be processed or analyzed using traditional processes or tools. Learn more in: The New “ABC” of ICTs (Analytics + Big Data + Cloud Computing): A Complex Trade-Off between IT and CT Costs

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The large volumes of data-sets with a higher complexity level. Learn more in: Understanding Big Data

353.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Learn more in: An Agent and Pattern-Oriented Approach to Data Visualization

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It refers to datasets that are large, complex, and more than main memory, internal and external disk that it is difficult or impossible to process using traditional methods. Learn more in: What Is Computational Communication Research?

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Big Data is high-volume, high-variety and high-velocity information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, process automation and decision making. Learn more in: Architecture for Big Data Storage in Different Cloud Deployment Models

356.

Large volumes of data which has more diversity and has grown rapidly. Learn more in: Awareness and Utilization of Emerging Technologies in E-Businesses: A Survey From Turkey

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Set of technologies that tackle huge amounts of data, usually at Web scale. Learn more in: Semantic Web Platforms for Bioinformatics and Life Sciences

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Is a collection of data from polluted gasses monitoring stations in each country that is huge in volume, globally these data growing exponentially with time, the complexity of these data require especial methods for analysis and management. Learn more in: Data Mining for Visualizing Polluted Gases

359.

It is a collection of data with huge volumes and extreme complexity. Learn more in: Big Data and Public Health: Avenue for Multidisciplinary Collaborative Research

360.

Data rapidly generated, in large scale and from multiple sources, mainly characterized by the 3 Vs: Volume, Velocity, and Variety. Learn more in: A Concrete Way to Develop Clinical Research in a Fair Way to the Users/Patients Using Blockchain Technology

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They are data sets that are so voluminous and complex that traditional data-processing application software is inadequate to deal with them. The technology of processing big data sets is often called as big data as well. Learn more in: The Link Between Innovation and Prosperity: How to Manage Knowledge for the Individual's and Society's Benefit From Big Data Governance?

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Massive data whose process complexity requires computer systems. Learn more in: Netflix in Spain, Spain in Netflix

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It refers to data repository accumulated through sensors and individual entries. Learn more in: The Role of Social Media Use in Health Communication: Digitized Health Communication During COVID-19 Pandemic

364.

The possibility of analysing and systematically extracting information from data sets that are too large or complex to be managed with by traditional data-processing techniques. Learn more in: Emerging Technologies to Increase Energy Efficiency and Decrease Indoor Pollution in University Campuses

365.

Is a diverse, complex, and voluminous sets of data produced at a very high velocity rate used by organizations to help sound, accurate, and fast decision to be made. Learn more in: Digital Transformation: Opportunities and Challenges

366.

Large amount of data, procedures, and computer applications, processed at high speed and stored for analysis. Learn more in: Learning Analytics for Smart Classrooms

367.

It refers to the possibility of analyzing and systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Learn more in: Can Artificial Intelligence and Big Data Improve Gamified Healthcare Services and Devices?

368.

Through various processes exercised on extremely large datasets, patterns and predictive analytics can be made to help the enterprise make better and more informed decisions. Learn more in: Vendor-Managed Inventory and Its Support Technologies: An Inside Look at Supplier and Customer Relationships

369.

Data that exceeds the capacity of commonly used hardware and software tools to capture, store and analyze within a tolerable elapsed time is considered as big data. The three main characteristics of big data are volume, variety, and velocity. Learn more in: What Is Open Source Software (OSS) and What Is Big Data?

370.

Big Data is the data with huge volume, variety and velocity, traditional tools are not able to handle such a huge datasets. Learn more in: Taxonomy Outline of Big Data Analytics Literature

371.

A field that addresses ways to analyze, extract, and manage data sets that are too large or complex to be dealt with by traditional data-processing application software. Learn more in: New Personal Learning Ecosystems: A Decade of Research in Review

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Big data is collection of datasets that could not be acquired, processed, perceived, and managed by traditional hardware and software tools within a specific time. Learn more in: Bootstrapping Urban Planning: Addressing Big Data Issues in Smart Cities

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Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. Learn more in: A Study of Big Data Analytical Frameworks in Research Data Management Using Data Mining Techniques

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Big data refers to large and complex information that is growing at an exponential rate and becomes difficult to handle through traditional methods. Learn more in: Leveraging Big Data Analytics in the Healthcare Sector: A Look at Applications and Challenges

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A term that generally describes a large amount of data. Learn more in: Why Is Data So Hard?: Challenges, Solutions, and Future Directions

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This is personalized data that is coming from people who are actively and voluntarily contributing to the compilation of these data sets. Much of the attention on big data has focused on the two key components: (1) gathering the large amounts of data and (2) quantitatively analyzing the data to obtain both personal-individualized information as well as information about different groups of people. Learn more in: Managing and Visualizing Unstructured Big Data

377.

Area of knowledge that studies how to treat, analyze and obtain information from data sets that are too large to be analyzed by traditional systems. Learn more in: Implications of 5G Technology in Marketing: A Systematic Review of the Bibliometric Literature

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Huge in volume or too large data and complex, in nature. Learn more in: Data Science in Economics and Business: Roots and Applications