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Exploring Calendar-Based Pattern Mining in Data Streams

Exploring Calendar-Based Pattern Mining in Data Streams

Rodrigo Salvador Monteiro, Geraldo Zimbrão, Holger Schwarz, Bernhard Mitschang, Jano Moreira de Souza
Copyright: © 2010 |Pages: 19
ISBN13: 9781605667485|ISBN10: 160566748X|ISBN13 Softcover: 9781616924522|EISBN13: 9781605667492
DOI: 10.4018/978-1-60566-748-5.ch016
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MLA

Monteiro, Rodrigo Salvador, et al. "Exploring Calendar-Based Pattern Mining in Data Streams." Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, IGI Global, 2010, pp. 342-360. https://doi.org/10.4018/978-1-60566-748-5.ch016

APA

Monteiro, R. S., Zimbrão, G., Schwarz, H., Mitschang, B., & Moreira de Souza, J. (2010). Exploring Calendar-Based Pattern Mining in Data Streams. In T. Nguyen (Ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications (pp. 342-360). IGI Global. https://doi.org/10.4018/978-1-60566-748-5.ch016

Chicago

Monteiro, Rodrigo Salvador, et al. "Exploring Calendar-Based Pattern Mining in Data Streams." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, 342-360. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-748-5.ch016

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Abstract

Calendar-based pattern mining aims at identifying patterns on specific calendar partitions. Potential calendar partitions are for example: every Monday, every first working day of each month, every holiday. Providing flexible mining capabilities for calendar-based partitions is especially challenging in a data stream scenario. The calendar partitions of interest are not known a priori and at each point in time only a subset of the detailed data is available. The authors show how a data warehouse approach can be applied to this problem. The data warehouse that keeps track of frequent itemsets holding on different partitions of the original stream has low storage requirements. Nevertheless, it allows to derive sets of patterns that are complete and precise. Furthermore, the authors demonstrate the effectiveness of their approach by a series of experiments.

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