Challenges and Opportunities with Big Data

K. Parimala¹ , G. Rajkumar² , A. Ruba³ , S. Vijayalakshmi⁴

1. Dept. of Computer Applications, N.M.S.S.Vellaichamy Nadar College, Madurai, India.
2. Dept. of Computer Applications, N.M.S.S.Vellaichamy Nadar College, Madurai, India.
3. Dept. of Computer Applications, N.M.S.S.Vellaichamy Nadar College, Madurai, India.
4. Dept. of Computer Applications, N.M.S.S.Vellaichamy Nadar College, Madurai, India.

Correspondence should be addressed to: mdugrk@gmail.com.

Section:Review Paper, Product Type: Isroset-Journal
Vol.5 , Issue.5 , pp.16-20, Oct-2017

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.1620

Online published on Oct 30, 2017

Copyright © K. Parimala, G. Rajkumar, A. Ruba, S. Vijayalakshmi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Data is exploding at a rapid rate. Big data is the term for data sets so large and complicated that it becomes difficult to process using traditional data management tools or processing applications. Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep and what to discard, and how to store what we keep reliably with the right metadata. Much data today is not natively in structured format, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Transforming such content into a structured format for later analysis is a major challenge. The value of data explodes when it can be linked with other data, thus data integration is a major creator of value. Since most data is directly generated in digital format today, we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. Data analysis, organization, retrieval, and modeling are other foundational challenges. Data analysis is a clear bottleneck in many applications, both due to lack of scalability of the underlying algorithms and due to the complexity of the data that needs to be analyzed. Finally, presentation of the results and its interpretation by non-technical domain experts is crucial to extracting actionable knowledge.

Key-Words / Index Term :
Data analysis, Big data, Data Analysis

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