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The omnipresence of using Enterprise Resource Planning (ERP) systems to support business processes has enabled recording a great amount of (relational) data which contains information about the behaviors of these processes. Various process mining techniques have been proposed to analyze recorded information about process executions. However, classic process mining techniques generally require a linear event log as input and not a multi-dimensional relational database used by ERP systems. Much research has been conducted into converting a relational data source into an event log. Most conversion approaches found in literature usually assume a clear notion of a case and a unique case identifier in an isolated process. This assumption does not hold in ERP systems where processes comprise the life-cycles of various interrelated data objects, instead of a single process. In this paper, a new semi-automatic approach is presented to discover from the plain database of an ERP system the various objects supporting the system. More precisely, we identify an artifact-centric process model describing the system’s objects, their life-cycles, and detailed information about how the various objects synchronize along their life-cycles, called interactions. In addition, our artifact-centric approach helps to eliminate ambiguous dependencies in discovered models caused by the data divergence and convergence problems and to identify the exact "abnormal flows". The presented approach is implemented and evaluated on two processes of ERP systems through case studies.

One of the main challenges in process mining is to discover a process model describing observed behaviour in the best possible manner. Since event logs only contain example behaviour and one cannot assume to have seen all possible process executions, process discovery techniques need to be able to handle incompleteness. In this paper, we study the effects of such incomplete logs on process discovery. We analyse the impact of incompleteness of logs on behavioural relations, which are an abstraction often used by process discovery techniques. We introduce probabilistic behavioural relations that are less sensitive to incompleteness, and exploit these relations to provide a more robust process discovery algorithm. We prove this algorithm to be able to rediscover a model of the original system. Furthermore, we show in experiments that our approach even rediscovers models from incomplete event logs that are much smaller than required by other process discovery algorithms. Keywords: process discovery, block-structured process models, rediscoverability, process trees

Process choreographies - communication between different organizations to exchange information - is part of daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe the data perspective for a successful process choreography. In this paper, we describe an entirely model-driven approach for BPMN, the industry standard, to include the data perspective while maintaining control flow aspects by utilizing a recent concept to enact data dependencies in internal processes. This work provides a modeling guideline with the require artifacts and their operational semantics to allow automatic choreography enactment covering data retrieval, transformation, and correlation. We show applicability of our approach by an implementation for the Camunda BPM platform, a java-based process engine, and validate it with the service interaction patterns. Keywords: Process Modeling, Data Modeling, Process Choreographies, Process Enactment, BPMN, SQL