Big Data Analytics: Applications, Trends, Tools, and Future Research Directions

Big Data Analytics: Applications, Trends, Tools, and Future Research Directions

Nitigya Sambyal (Punjab Engineering College (Deemed), India), Poonam Saini (Punjab Engineering College (Deemed), India) and Rupali Syal (Punjab Engineering College (Deemed), India)
DOI: 10.4018/978-1-5225-8407-0.ch004

Abstract

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.
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Introduction

Data nowadays is available in wide variety of forms ranging from structured which has a high degree of organization; semi-structured which is a form of structured data but does not conform with the formal structure of data models as seen in relational databases; unstructured which lacks any uniform structure and sensor data which is an output obtained from device that detects changes in the physical environment. However due to increase in digitization, the amount of data created every day is huge which in turn increases the complexity to manage and analyse the data. Management of such data essentially includes three main functions:

  • 1.

    Data Acquisition: It involves capturing data from various locations by defining sources of data, type, templates, configurations etc. It can be from texts, documents, tables (relational databases), audio, image, video etc.

  • 2.

    Data Preparation: It deals with data storage, cleaning, enrichment and validation with primary focus on ensuring accountability and trustworthiness.

  • 3.

    Data Distribution: It deals with both sharing and protection of data, ensuring data privacy, security etc.

Big data which is an emerging topic has attracted the attention of many researchers and practitioners from agriculture industry, banking sector, business corporations, healthcare and cybernetics fields. Big Data is characterized by 5 Vs (Kulkarni, Bhartiya, Kishore & Gunturi, 2016) as shown in Figure 1.

  • Variety: Varied type of structured dataphone numbers, address, name etc; unstructured like photos, videos, tweets etc and semi-structured data like HTML, XML, JSON documents etc.

  • Velocity: High speed at which vast amount of data is being generated, transmitted, collected and analysed.

  • Volume: Incredible amount of data generated each second from disparate sources like social media, credit cards, online transactions, sensors, videos, photographs etc.

  • Veracity: This indicates the trustworthiness of data i.e. degree to which data is accurate, precise and reliable.

  • Value: It mainly signifies the worth of the data in terms of cost and benefits of collecting and analysing a particular data.

Figure 1.

5 Vs of big data

Big data analytics aims to computationally analyse large data sets to discover hidden patterns, correlations, market trends, customer preferences, behaviour, opinion, associations and other useful information that can help organizations make better informed business decisions.

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