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Data stream mining in ubiquitous environments: state-of-the-art and current directions

Gaber, M. M., Gama, J., Krishnaswamy, S., Gomes, J. B. and Stahl, F. (2014) Data stream mining in ubiquitous environments: state-of-the-art and current directions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4 (2). pp. 116-138. ISSN 1942-4795

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To link to this item DOI: 10.1002/widm.1115

Abstract/Summary

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:36423
Publisher:Wiley

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