Affiliations: [a] Center for Distributed Systems and Software Engineering, Monash University, Australia | [b] Department of Computer Science and Computer Engineering, La Trobe University, Australia
Correspondence:
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Corresponding author: Pari Delir Haghighi, PO Box 7074, Faculty of Information Technology, Monash University, 900 Dandenong Rd, Caulfield East, VIC – 3145, Australia. Tel.: +613 9903 1402; Fax: +613 99031077; E-mail: Pari.DelirHaghighi@infotech.monash.edu.au.
Abstract: In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adaptation, there is a need to go beyond mere computational and device capabilities to encompass the full spectrum of context-awareness. This paper presents a general approach for context-aware adaptive mining of data streams that aims to dynamically and autonomously adjust data stream mining parameters according to changes in context and situations. We perform intelligent and real-time analysis of data streams generated from sensors that is under-pinned using context-aware adaptation. A prototype of the proposed architecture is implemented and evaluated in the paper through a real-world scenario in the area of healthcare monitoring.
