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ConChi: Pattern Change Mining from Mobile Context-Aware Data

ConChi: Pattern Change Mining from Mobile Context-Aware Data

Luca Cagliero
Copyright: © 2016 |Pages: 23
ISBN13: 9781466695627|ISBN10: 1466695625|EISBN13: 9781466695634
DOI: 10.4018/978-1-4666-9562-7.ch042
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MLA

Cagliero, Luca. "ConChi: Pattern Change Mining from Mobile Context-Aware Data." Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 798-820. https://doi.org/10.4018/978-1-4666-9562-7.ch042

APA

Cagliero, L. (2016). ConChi: Pattern Change Mining from Mobile Context-Aware Data. In I. Management Association (Ed.), Business Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 798-820). IGI Global. https://doi.org/10.4018/978-1-4666-9562-7.ch042

Chicago

Cagliero, Luca. "ConChi: Pattern Change Mining from Mobile Context-Aware Data." In Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 798-820. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9562-7.ch042

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Abstract

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users' requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

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