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Evidence Data Preprocessing for Forensic and Legal Analytics
Sundar Krishnan, Narasimha Shashidhar, Cihan Varol, ABM Rezbaul Islam
Pages - 24 - 34     |    Revised - 31-05-2021     |    Published - 30-06-2021
Volume - 12   Issue - 2    |    Publication Date - June 2021  Table of Contents
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KEYWORDS
eDiscovery, Electronic Stored Information, Digital Evidence, Digital Forensics, Digital Forensic Analytics, Legal Analytics, Machine Learning, Preprocessing, Natural Language Processing.
ABSTRACT
Electronic evidential data pertaining to a legal case, or a digital forensic investigation can be enormous given the extensive electronic data generation mechanisms of companies and users coupled with cheap storage alternatives. Working with such volumes of data can be tasking, sometimes requiring matured analytical processes and a degree of automation. Once electronic data is collected post eDiscovery hold or post forensic acquisition, it can be framed into datasets for analytical research. This paper focuses on data preprocessing of such evidentiary datasets outlining best practices and potential pitfalls prior to undertaking analytical experiments.
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Mr. Sundar Krishnan
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
skrishnan@shsu.edu
Dr. Narasimha Shashidhar
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Dr. Cihan Varol
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Dr. ABM Rezbaul Islam
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America