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

• Type: Another Publication in an International Scientific Journal
• Date: 2014

Authors

Gaber, MM (Author) Other; João Gama (Author) FEP; Krishnaswamy, S (Author) Other; Gomes, JB (Author) Other; Stahl, F (Author) Other

Journal

Title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Vol. 4

Pages: 116-138

ISSN: 1942-4787

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 28 Citations

Scopus - 30 Citations

Other information

Authenticus ID: P-009-6FV

DOI: 10.1002/widm.1115

Abstract (EN): 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. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the .

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23