DWFIST: leveraging calendar-based pattern mining in data streams

Authors:
Rodrigo Salvador Monteiro;Geraldo Zimbrão;Holger Schwarz;Bernhard Mitschang;Jano Moreira de Souza
Affiliations:
Computer Science Department, Graduate School of Engineering, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil and Institute f. Parallel & Distributed Systems, University of Stuttga ...;Computer Science Department, Graduate School of Engineering and Institute of Mathematics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil;Institute f. Parallel & Distributed Systems, University of Stuttgart, Stuttgart, Germany;Institute f. Parallel & Distributed Systems, University of Stuttgart, Stuttgart, Germany;Computer Science Department, Graduate School of Engineering and Institute of Mathematics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Venue:
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Year:
2007

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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. We 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. This work demonstrates the effectiveness of our approach by a series of experiments.