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Sachin Arun Thanekar
KL University

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A Study on MapReduce: Challenges and Trends Sachin Arun Thanekar; K. Subrahmanyam; A. B. Bagwan
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 1: October 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i1.pp176-183

Abstract

Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data. The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement. 
A Study on Digital Forensics in Hadoop Sachin Arun Thanekar; K. Subrahmanyam; A.B. Bagwan
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 2: November 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i2.pp473-478

Abstract

Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Hadoop is a framework which can be used for tremendous data storage and faster processing. It is freely available, easy to use and implement. Big data forensic is one of the challenges of big data. For this it is very important to know the internal details of the Hadoop. Different files are generated by Hadoop during its process. Same can be used for forensics. In our paper our focus is on digital forensics and different files generated during different processes. We have given the short description on different files generated in Hadoop. With the help of an open source tool ‘Autopsy’ we demonstrated that how we can perform digital forensics using automated tool and thus big data forensics can be done efficiently.
Co-Authors A. B. Bagwan A.B. Bagwan K. Subrahmanyam