Temporal mining for interactive workflow data analysis

author: Michele Berlingerio
published: Sept. 14, 2009,   recorded: June 2009,   views: 295
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Description

In the past few years there has been an increasing interest in the analysis of process logs. Several proposed techniques, such as workflow mining, are aimed at automatically deriving the underlying workflow models. However, current approaches only pay little attention on an important piece of information contained in process logs: the timestamps, which are used to define a sequential ordering of the performed tasks. In this work we try to overcome these limitations by explicitly including time in the extracted knowledge, thus making the temporal information a first-class citizen of the analysis process. This makes it possible to discern between apparently identical process executions that are performed with different transition times between consecutive tasks.

This paper proposes a framework for the user-interactive exploration of a condensed representation of groups of executions of a given process. The framework is based on the use of an existing mining paradigm: Temporally-Annotated Sequences (TAS). These are aimed at extracting sequential patterns where each transition between two events is annotated with a typical transition time that emerges from input data. With the extracted TAS, which represent sets of possible frequent executions with their typical transition times, a few factorizing operators are built. These operators condense such executions according to possible parallel or possible mutual exclusive executions. Lastly, such condensed representation is rendered to the user via the exploration graph, namely the Temporally-Annotated Graph (TAG). The user, the domain expert, is allowed to explore the different and alternative factorizations corresponding to different interpretations of the actual executions. According to the user choices, the system discards or retains certain hypotheses on actual executions and shows the consequent scenarios resulting from the coresponding re-aggregation of the actual data.

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