Big Data Issues and Challenges

Big Data Issues and Challenges

Shweta Kaushik (ABES Engineering College, India)
Copyright: © 2021 |Pages: 21
DOI: 10.4018/978-1-7998-3049-8.ch001
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The library plays a vital role for the students, researchers, and academician as a central data storage which is utilized for accessing any required data within less time and effort. Academic libraries are rich in primary and secondary data with lots of content, which may include data from other resources also such as internet and other media. This large amount of data must provide a valuable information to the user, but it may not be same format. Librarians need to transform and analyse all the available data to the same format so that it becomes easier for the user to facilitate the required knowledge. For example, they need to create a dataset in a manner that is easy to visualize and accessible. In this regard, big data analytics tools such as information visualisation tools help the user in mining the intended information. In any case, it is assumed that the confinements and conceivable outcomes of Big data innovation are being considered and that relationships are acknowledged as precise. This chapter focus on all the possibilities of various issues and challenges that may arise while using big data with library.
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I. Introduction

Big Data

In today digital era, the data is generated enormously day by day from multiple resources such as social networking data, cloud computing data, online trading data etc. responsible for generating a large amount of data. The usage of the latest digital technologies and information system such as cloud computing, mobile computing, IoT etc. is also responsible for the generation of large amount of data. This data generated by various technology is not always in the same format i.e, data may be structured, unstructured and semi-structured. This inconsistency of data raises many issues and challenges in front of the data management authority as they all need to deal with these numerous types of data. Previously, data warehouses are responsible for handling the large data storage and maintenance. User will acquire their required data by applying any of the data mining technique.

The primary requirement here was that all the data is stored in a predefined format which increase data warehouse efficiency and also reduce the time for searching any valuable data by data mining. Since, now data is not always stored in the same format and also due to its large volume it become infeasible to apply all these previously data mining technique. Usage of big data have their own technology and method to handle this issue, but still facing security issues and challenges regarding the data storage and finding the useful or required data for decision making purpose in less time. This chapter focus on these issues and challenges comes in front of big data technology.

Big Data in Library

There are few reasons for the adoption of big data technology in digital libraries for personalized services as:

  • The consistent creation of huge measures of information makes acquiring successful data progressively troublesome. The data over-burden issue is ending up progressively visible comparative with restricted client data adequacy and time costs. In this manner, finding content that clients are really keen on from enormous scale information assets and separating unessential data to minimize the meaningless data screening expenses has turned out to be vital to improving client accomplishment in computerized libraries.

  • The regularly expanding measure of information prompts consistently expanding information associations. Such associations cannot just improve our comprehension of information and encourage approaches to discover target information all the more successfully what's more, proficiently, yet additionally give the essential and fundamental conditions for further investigation and examination of concealed qualities which customary single-information assets can't give. In huge measures of information, there are an extraordinary number of relationships among the information, for example, the relationship among client social information, relationship among clients and clients, relationship among clients and assets, what's more, relationship among various assets. Such associations permit clients to get the necessary help content all the more effectively and rapidly. Moreover, such associations can create new client data prerequisites and can be utilized to make new sorts of data benefits by joining existing client intrigue designs.

  • Users get and break down information to acquire learning identified with a specific application. The comprehension and use of the learning substance are dictated by the information and furthermore relies upon the particular application condition and current data prerequisites. Connections, cooperation, and incorporation of semantic and application connections will significantly affect client understanding of the got information (Zhang, 2005).


Ii. Challeneges In Big Data

The data generation by the various organization is increasing at the very fast speed from 40-60% per year. Also, all this generated data is not useful for organization. This generation of enormous large volumetric data brought many challenges related to information security, computational complexity, data storage and scalability etc. There are many computational techniques as well as statistical methods which works well for small data but do not perform well in case of big data. Thus, it become a challenge in front of big data to handle all these issues. The various challenges faced by big data are, as shown in figure 1:

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