Improving the Efficiency of Big Forensic Data Analysis Using NoSQL
Contribution to Book
MOBIMEDIA 2017: Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications
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.
Al Sadi, Md Baitul, Hayden Wimmer, Lei Chen, Kai Wang.
"Improving the Efficiency of Big Forensic Data Analysis Using NoSQL."
MOBIMEDIA 2017: Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications: 240-248 Brussels, Belgium: Institute for Computer Sciences, Social-Informatics, and Telecommunications Engineering.
doi: 10.4108/eai.13-7-2017.2270344 source: https://eudl.eu/doi/10.4108/eai.13-7-2017.2270344 isbn: 978-1-63190-156-0