Extracting and Pre-Processing Event Logs

Event data is the basis for all process mining analysis. Most process mining techniques assume their input to be an event log. However, event data is rarely recorded in an event log format, but has to be extracted from raw data. Event log extraction itself is an act of modeling as the analyst has to consciously choose which features of the raw data are used for describing which behavior of which entities. Being aware of these choices and subtle but important differences in concepts such as trace, case, activity, event, table, and log is crucial for mastering advanced process mining analyses. This text provides fundamental concepts and formalizations and discusses design decisions in event log extraction from a raw event table and for event log pre-processing. It is intended as study material for an advanced lecture in a process mining course.Comment: This text is intended as study material for an advanced lecture in a process mining cours

Artifact Lifecycle Discovery

Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whose lifecycles define the overall business process. While artifact-centric modeling shows significant advantages, the overwhelming majority of existing process mining methods cannot be applied (directly) as they are tailored to discover monolithic process models. This paper addresses the problem by proposing a chain of methods that can be applied to discover artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) process discovery and analysis methods can be reused in a flexible manner. The methods presented in this paper are implemented as software plug-ins for ProM, a generic open-source framework and architecture for implementing process mining tools

Discover Context-Rich Local Process Models (Extended Abstract)

We introduce a new ProM plugin called Discover Context-Rich LPMs which mines a log for large local process models (LPMs) based on supported words. The main advantage of this plugin is that it produces much larger and much fewer LPMs than other tools. The plugin is packaged with an additional plugin called Generate HTML coverage report which calculates the coverage of LPMs along with several other quality measures. This extra plugin is useful to select and improve a set of LPMs.</p

Exploring Task Execution Patterns in Event Graphs

Classical process mining aims to capture the behavior of a process based on a single dimension: the sequence of activities grouped by process cases. This viewpoint fails to capture how individual actors are organizing their work across multiple cases. We present a tool that uses the graph database Neo4j to model actor behavior over different cases as an event graph. We then use Neo4j queries to detect task execution patterns in the graph describing how multiple actors collaborate across multiple cases. Exploring and visualizing these patterns enables the data driven analysis of tasks, routines, and habits as studied in organizations research.</p

Mining Branching-Time Scenarios

Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces