Towards next-generation heterogeneous mobile data stream mining applications: Opportunities, challenges, and future research directions. (1st February 2017) - Rehman, Muhammad Habib ur; Liew, Chee Sun; Wah, Teh Ying; Khan, Muhammad Khurram
Abstract: The convergence of Internet of Things (IoTs), mobile computing, cloud computing, edge computing and big data has brought a paradigm shift in computing technologies. New computing systems, application models, and application areas are emerging to handle the massive growth of streaming data in mobile environments such as smartphones, IoTs, body sensor networks, and wearable devices, to name a few. However, the challenge arises about how and where to process the data streams in order to perform analytic operations and uncover useful knowledge patterns. The mobile data stream mining (MDSM) applications involve a number of operations for, 1) data acquisition from heterogeneous data sources, 2) data preprocessing, 3) data fusion, 4) data mining, and 5) knowledge management. This article presents a thorough review of execution platforms for MDSM applications. In addition, a detailed taxonomic discussion of heterogeneous MDSM applications is presented. Moreover, the article presents detailed literature review of methods that are used to handle heterogeneity at application and platform levels. Finally, the gap analysis is articulated and future research directions are presented to develop next-generation MDSM applications. Abstract : Highlights: Presenting abibliometric analysis of existing literature related to MDSM. Highlighting opportunities and challenges to design next-generation MDSM platforms. Presenting the detailed taxonomic discussion of heterogeneous MDSM Applications. Performing thorough literature review and comparing selected MDSM platforms. Articulating the gap analysis and presenting future research directions.