@inproceedings{12b2dda60ae04174b6a5de769eae991c,
title = "Improving the efficiency of big forensic data analysis using NoSQL",
abstract = "The rapid growth of Internet of Things (IoT) makes the task for digital forensic more difficult. At the same time, the data analyzing technology is also developing in a feasible pace. Where traditional Structured Query Language (SQL) is not adequate to analyze the data in an unstructured and semi-structured format, Not only Standard Query Language (NoSQL) unfastens the access to analyzing the data of all format. The large volume of data of IoTs turns into Big Data which just do not enhance the probability of attaining of evidence of an incident but make the investigation process more complex. This paper aims to analyze Big Data for Digital Forensic (DF) investigation using NoSQL. MongoDB has been used to analyze Big Forensic Data in the form of document-oriented database. The proposed solution is capable of analyzing Big Forensic Data in the form of NoSQL more specifically document oriented data in a cost-effective, efficient way as all the tools is being used are open source.",
keywords = "Autopsy, Big Data, Big Data Forensic, Digital Forensic (DF), Document-oriented Database, Internet of Things (IoT), MongoDB, NoSQL",
author = "{Al Sadi}, {Md Baitul} and Hayden Wimmer and Lei Chen and Kai Wang",
note = "Publisher Copyright: Copyright {\textcopyright} 2017 EAI.; 10th EAI International Conference on Mobile Multimedia Communications, MOBIMEDIA 2017 ; Conference date: 13-07-2017 Through 14-07-2017",
year = "2017",
doi = "10.4108/eai.13-7-2017.2270344",
language = "English",
isbn = "9781631901560",
series = "International Conference on Mobile Multimedia Communications (MobiMedia)",
publisher = "ICST",
pages = "240--248",
editor = "Honggang Wang and Qianbin Chen and Yanbing Liu and Dapeng Wu and Nirwan Ansari and Lei Chen and Dalei Wu",
booktitle = "MOBIMEDIA 2017 - 10th EAI International Conference on Mobile Multimedia Communications",
}