Since a past decade, social media networking has become an essential part of everyone’s life affecting cultural, economic and social life of the people. According to internetlivestats.com, in March 2019 the Internet users reached 4 168 461 500, i.e., 50.08 % penetration of world population. According to Statista, in 2019 there are 2.22 billion social media networking users worldwide, i.e., 31 % of global social media networking penetration and it is expected that in 2021 this number will reach 3.02 billion. These social networking sites are attracting users from all walks of life and keeping these users’ data in the cloud. Today’s big challenge is related to an increase in volume, velocity, variety and veracity of data in social media networking, and this leads to creating several concerns, including privacy and security; on the other hand, it also proves as a tool to prevent and investigate cybercrime, if intelligently and smartly handled. The law enforcement agencies are putting their utmost efforts to prevent cybercrime by monitoring communications activities over the Internet. In this paper, the authors discuss recommendations and techniques for preventing cybercrime.
Saadia Karim, Tariq Rahim Soomro and S. M. Aqil Burney
Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Frameworks have been compared on the basis of architectural characteristics and feature attributes as well. These two categories project a significant effect on the execution of spatiotemporal data in big data. Frameworks are able to solve the real-time problems in less time of cycle. This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.