Abstract
Discrete event sequences have been modeled with two types of representation: snapshots and overviews. Snapshot models describe the process as a collection of relatively short sequences. Overview models collect key relationships into a single structure, providing an integrated but abstract view. This paper describes a new algorithm for constructing one type of overview model: state transition diagrams. The algorithm, called State Transition Dependency Detection (STDD), is the latest in a family of statistics based algorithms for modeling event sequences called Dependency Detection. We present accuracy results for the algorithm on synthetic data and data from the execution of two AI systems.
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© 1997 Springer-Verlag
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Howe, A.E., Somlo, G. (1997). Modelling discrete event sequences as state transition diagrams. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052872
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DOI: https://doi.org/10.1007/BFb0052872
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