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Organizations require a unified view of business processes and organizational data for the improvement of their daily operations. However, it is infrequent for both kinds of data to be consistently unified. Organizational data (e.g., clients, orders, and payments) is usually stored in many different data sources. Process data (e.g., cases, activity in- stances, and variables) is generally handled manually or implicit in information systems and coupled with organizational data without clear separation. It impairs the combined application of process mining and data mining techniques for a complete evaluation of their business process execution. In this paper, we deal with the integration of both kinds of data into a unified view. First, we analyze data integration scenarios and data matching problems considering intra-organizational and inter-organizational collaborative business processes. We also propose a model-driven approach to integrate several data sources, generating a unified model for evidence-based business intelligence. |
