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Big Data brings new challenges to the field of e-Discovery or digital  forensics and these challenges are mostly connected to the various methods  for data processing. Considering that the most important factors are time and  cost in determining success or failure of digital investigation, the  development of a valid indexing method for efficient search should come first  to more quickly and accurately find relevant evidence from Big Data. This  paper, therefore, introduces a Distributed Text Processing System based on  Hadoop called DTPS and explains about the distinctions between DTPS and other  related researches to emphasize the necessity of it. In addition, this paper  describes various experimental results in order to find the best  implementation strategy in using Hadoop MapReduce for the distributed  indexing and to analyze the worth for practical use of DTPS by comparative  evaluation of its performance with similar tools. To be short, the ultimate  purpose of this research is the development of useful search engine specially  aimed at Big Data indexing as a major part for the future e-Discovery cloud  service.

The efficient implementation of distributed indexing with Hadoop for digital investigations on Big Data