Knowledge discovery from series of interval events

    Authors

    R. Villafane; K. A. Hua; D. Tran;B. Maulik

    Comments

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    Abbreviated Journal Title

    J. Intell. Inf. Syst.

    Keywords

    data mining; knowledge discovery; time series; event sequence; temporal; SEQUENCES; Computer Science, Artificial Intelligence; Computer Science, Information; Systems

    Abstract

    Knowledge discovery from data sets can be extensively automated by using data mining software tools. Techniques for mining series of interval events, however, have not been considered. Such time series are common in many applications. In this paper, we propose mining techniques to discover temporal containment relationships in such series. Specifically, an item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships provides insight about temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.

    Journal Title

    Journal of Intelligent Information Systems

    Volume

    15

    Issue/Number

    1

    Publication Date

    1-1-2000

    Document Type

    Article; Proceedings Paper

    Language

    English

    First Page

    71

    Last Page

    89

    WOS Identifier

    WOS:000087869400005

    ISSN

    0925-9902

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