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MILPRIT*: A Constraint-Based Algorithm for Mining Temporal Relational Patterns

MILPRIT*: A Constraint-Based Algorithm for Mining Temporal Relational Patterns

Sandra de Amo, Waldecir P. Junior, Arnaud Giacometti
Copyright: © 2008 |Volume: 4 |Issue: 4 |Pages: 20
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202027|EISSN: 1548-3924|DOI: 10.4018/jdwm.2008100103
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MLA

de Amo, Sandra, et al. "MILPRIT*: A Constraint-Based Algorithm for Mining Temporal Relational Patterns." IJDWM vol.4, no.4 2008: pp.42-61. http://doi.org/10.4018/jdwm.2008100103

APA

de Amo, S., Junior, W. P., & Giacometti, A. (2008). MILPRIT*: A Constraint-Based Algorithm for Mining Temporal Relational Patterns. International Journal of Data Warehousing and Mining (IJDWM), 4(4), 42-61. http://doi.org/10.4018/jdwm.2008100103

Chicago

de Amo, Sandra, Waldecir P. Junior, and Arnaud Giacometti. "MILPRIT*: A Constraint-Based Algorithm for Mining Temporal Relational Patterns," International Journal of Data Warehousing and Mining (IJDWM) 4, no.4: 42-61. http://doi.org/10.4018/jdwm.2008100103

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Abstract

In this article, we consider a new kind of temporal pattern where both interval and punctual time representation are considered. These patterns, which we call temporal point-interval patterns, aim at capturing how events taking place during different time periods or at different time instants relate to each other. The datasets where these kinds of patterns may appear are temporal relational databases whose relations contain point or interval timestamps. We use a simple extension of Allen’s Temporal Interval Logic as a formalism for specifying these temporal patterns. We also present the algorithm MILPRIT* for mining temporal point-interval patterns, which uses variants of the classical levelwise search algorithms. In addition, MILPRIT* allows a broad spectrum of constraints to be incorporated into the mining process. An extensive set of experiments of MILPRIT* executed over synthetic and real data is presented, showing its effectiveness for mining temporal relational patterns.

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