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In modern enterprises, Business Processes (BPs) are realized over a mix ofworkflows, IT systems, Web services and direct collaborations of people.Accordingly, process data (i.e., BP execution data such as logs containingevents, interaction messages and other process artifacts) is scattered acrossseveral systems and data sources, and increasingly show all typical propertiesof the Big Data. Understanding the execution of process data is challenging askey business insights remain hidden in the interactions among process entities:most objects are interconnected, forming complex, heterogeneous but oftensemi-structured networks. In the context of business processes, we consider theBig Data problem as a massive number of interconnected data islands frompersonal, shared and business data. We present a framework to model processdata as graphs, i.e., Process Graph, and present abstractions to summarize theprocess graph and to discover concept hierarchies for entities based on bothdata objects and their interactions in process graphs. We present a language,namely BP-SPARQL, for the explorative querying and understanding of processgraphs from various user perspectives. We have implemented a scalablearchitecture for querying, exploration and analysis of process graphs. Wereport on experiments performed on both synthetic and real-world datasets thatshow the viability and efficiency of the approach.

A Query Language for Summarizing and Analyzing Business Process Data