Introduction to Big Data Analysis

We are living in an era of big data, where the process of generating data is continuously been taking place with each coming second. Data that is more varied and extremely complex in structure (unstructured/semi-structured) with problems of indexing, sorting, searching, analyzing and visualizing are major challenges of today's organizations. Big data is always defined by its 5-v characteristics which are Volume, Velocity, Veracity, Variety, and Value. Almost each data model comprising big data is dependent on these 5-v characteristics. A large number of researches have been done on velocity and volume, but the complete and efficient solution for the variety is still not available in the markets. Traditional solutions provided by DBMS generally use multidimensional data type. However, many new data types cannot be compatible with these traditional systems. Big Data is a general problem affecting different fields, whether it is business, economic, social security or scientific research. To analyze huge data sets in order to get insights and find patterns in data is called big data analytics. Big data analytics is the need of every corporate and state of the art organization to look forward and make useful decisions. This paper comprises of discussion on current issues, opportunities, trends, and challenges of big data aimed to discuss variety in more detail. An efficient solution for the big data variety problem will be discussed.

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Trends and Future Perspective Challenges in Big Data

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Smart Innovation, Systems and Technologies

142

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Introduction to Big Data Analysis

Cited by 3 publications

References 5 publications

Trends and Future Perspective Challenges in Big Data

Trends and Future Perspective Challenges in Big Data

Distributed Storage Infrastructure: Foundations, Analytics, Tools, and Applications

A Structural Health Monitoring Technique for the Analysis of Big Data of Bridges

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