Abstract
A business process or workflow is an assembly of tasks that accomplishes a business goal. Business process management is the study of the design, configuration/implementation, enactment and monitoring, analysis, and re-design of workflows. The traditional methodology for the re-design and improvement of workflows relies on the well-known sequence of extract, transform, and load (ETL), data/process warehousing, and online analytical processing (OLAP) tools. In this paper, we study the ad hoc queryiny of process enactments for (data-centric) business processes, bypassing the traditional methodology for more flexibility in querying. We develop an algebraic query language based on “incident patterns” with four operators inspired from Business Process Model and Notation (BPMN) representation, allowing the user to formulate ad hoc queries directly over workflow logs. A formal semantics of this query language, a preliminary query evaluation algorithm, and a group of elementary properties of the operators are provided.
Highlights
According to Gartner, business process (BP) improvement is the top business strategy in enterprise systems [1]
The design and adjustment of workflows relies on the analysis of past workflow executions, and the ability to query characteristics of past executions is key for BP improvement
An incident tree is a binary tree with two types of nodes—namely operator and activity nodes—such that:
Summary
According to Gartner, business process (BP) improvement is the top business strategy in enterprise systems [1]. This paper initiates the development of a query language for ad hoc exploration of BP execution logs. The state-of-the-art design/development methodology for BP/workflow management systems places the data needed for analysis in process logs, activity logs, data stores, process models, and even execution engines [2,3]. ETL aims at specific types of analysis, often with queries centered around summaries over data cubes [6] This is effective only when relevant data are extracted. This paper develops an algebraic query language based on “incident patterns” for process analytics. An incident pattern describes a temporally related set of activity executions within a single workflow instance, allowing the user to reason about temporal relationships between the activity executions.
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