Abstract
Distributed online data analytics has attracted significant research interest in recent years with the advent of Fog and Cloud computing. The popularity of novel distributed applications such as crowdsourcing and crowdsensing have fostered the need for scalable energy-efficient platforms that can enable distributed data analytics. In this paper, we propose CARDAP, a (C)ontext (A)ware (R)eal-time (D)ata (A)nalytics (P)latform. CARDAP is a generic, flexible and extensible, component- based platform that can be deployed in complex distributed mobile analytics applications e.g. sensing activity of citizens in smart cities. CARDAP incorporates a number of energy efficient data delivery strategies using real-time mobile data stream mining for data reduction and thus less data transmission. Extensive experimental evaluations indicate the CARDAP platform can deliver significant benefits in energy efficiency over naive approaches. Lessons learnt and future work conclude the paper.
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Jayaraman, P.P., Gomes, J.B., Nguyen, H.L., Abdallah, Z.S., Krishnaswamy, S., Zaslavsky, A. (2014). CARDAP: A Scalable Energy-Efficient Context Aware Distributed Mobile Data Analytics Platform for the Fog. In: Manolopoulos, Y., Trajcevski, G., Kon-Popovska, M. (eds) Advances in Databases and Information Systems. ADBIS 2014. Lecture Notes in Computer Science, vol 8716. Springer, Cham. https://doi.org/10.1007/978-3-319-10933-6_15
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DOI: https://doi.org/10.1007/978-3-319-10933-6_15
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