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

Databases;Machine Learning