1,721,011 research outputs found
A probabilistic unified framework for event abstraction and process detection from log data
No full textWe consider the scenario where the executions of different business processes are traced into a log, where each trace describes a process instance as a sequence of low-level events (representing basic kinds of operations). In this context, we address a novel problem: given a description of the processes’ behaviors in terms of high-level activities (instead of low-level events), and in the presence of uncertainty in the mapping between events and activities, find all the interpretations of each trace Φ. Specifically, an interpretation is a pair (σ,W) that provides a two-level “explanation” for Φ: σ is a sequence of activities that may have triggered the events in Φ, and W is a process whose model admits σ. To solve this problem, we propose a probabilistic framework representing “consistent” Φ’s interpretations, where each interpretation is associated with a probability score
A framework supporting the analysis of process logs stored in either relational or NoSQL DBMSS
No full textThe issue of devising efficient and effective solutions for supporting the analysis of process logs has recently received great attention from the research community, as effectively accomplishing any business process management task requires understanding the behavior of the processes. In this paper, we propose a new framework supporting the analysis of process logs, exhibiting two main features: a flexible data model (enabling an exhaustive representation of the facets of the business processes that are typically of interest for the analysis) and a graphical query language, providing a user-friendly tool for easily expressing both selection and aggregate queries over the business processes and the activities they are composed of. The framework can be easily and efficiently implemented by leveraging either “traditional” relational DBMSs or “innovative” NoSQL DBMSs, such as Neo4J
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