Process Mining Manifesto
This manifesto is written by members and supporters of the IEEE Task Force on Process Mining. The goal of this task force is to promote the research, development, education, implementation, evolution, and understanding of process mining.
IEEE CIS Task Force on Process Mining The IEEE has established a Task Force on Process Mining. The goal of Task Force is to promote the research, development, education and understanding of process mining.
Introduction to Event Log Mining with R. Content: the scenarios and benefits of event log mining; The minimum data required for event log mining; Ingesting and analyzing event log data using R; Process Mining with ProM.
Process Mining: Data Science in Action. This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics.
A Primer on Process Mining.Practical Skills with Python and Graphviz.The main goal of this book is to explain the core ideas of process mining, and to demonstrate how they can be implemented using just some basic tools that are available to any computer scientist or data scientist. It describes how to analyze event logs in order to discover the behavior of real-world business processes. The end result can often be visualized as a graph, and the book explains how to use Python and Graphviz to render these graphs intuitively. Overall, it enables the reader to implement process mining techniques on his or her own, independently of any specific process mining tool. An introduction to two popular process mining tools, namely Disco and ProM, is also provided + github
PM4PY is a python library that supports (state-of-the-art) process mining algorithms in python. It is completely open source and intended to be used in both academia and industry projects.
ProM is an extensible framework that supports a wide variety of process mining techniques in the form of plug-ins. It is platform independent as it is implemented in Java, and can be downloaded free of charge. We welcome and support practical applications of ProM, and we invite researchers and developers to contribute in the form of new plug-ins.
ProcessExplorer is an academic process mining tool which guides analysts through event logs by suggesting subset and insights recommendations.
bupaR is an open-source, integrated suite of R-packages for the handling and analysis of business process data. It currently consists of 8 packages, including the central package, supporting different stages of a process mining workflow.
XESame is an application that supports in the extraction of an event log from non-event log data sources.
Uma is a Java-library for analyzing and synthesizing Petri net models using unfolding-based techniques.
MXMLib is a Java library, used by ProMimport among others, when converting logs to the MXML format.
CPN Tools. A tool for editing, simulating, and analyzing Colored Petri nets.
visual Miner is a free and open source process mining tool, which makes it easy to explore an event log.
Process mining techniques and applications – A systematic mapping study. The objective of this article is to map the active research topics of process mining and their main publishers by country, periodicals, and conferences. Also extract the reported application studies and classify these by exploration domains or industry segments that are taking advantage of this technique. The applied research method was systematic mapping, which began with 3713 articles. After applying the exclusion criteria, 1278 articles were selected for review.
Process Mining: A DATABASE OF APPLICATIONS. The idea of creating the present database of applications came up within HSPI in 2016, during an informal meeting about process mining technology and its spread over several business – and not only – situations. The need to collect, to put into an ordered system all the historical information about process mining techniques implementations has led the creation of the very first version of " Process Mining: A Database of Applications".
Automated Discovery of Process Models from Event Logs: Review and Benchmark. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
Implementation (github)
Predictive Performance Monitoring of Material Handling Systems Using the Performance Spectrum". The Performance Spectrum Miner (PSM) is a visual analytics tool for event data. It takes as input an event log (of events, timestamps, and case identifier) of past process or system executions in CSV or XES format. The PSM visualizes the flow of all cases over all process over time, and gives detailed insights performance characteristics.
MINERful. MINERful is a fast process mining tool for discovering declarative process models out of event logs. Event logs can be either real or synthetic, stored as XES, MXML, or text files (a collection of strings, in which every character is considered as an event, every line as a trace). Among the other things, MINERful can also create synthetic logs and export them as XES or MXML files, simplify existing Declare models, and import/export models written in JSON or in the ConDec native language.
Comprehensive Process Drift Detection with Visual Analytics (VDD technique). This technique supports the discovery of process drifts in the processes from event logs. Load your timestamp sorted csv and xes files to the tool to discover Drfit Maps, Drift charts, and Declare relations that see which part of the log is changing.
ProcessCubeExplorer Process Mining extracts implicit knowledge from event logs and generates process models to visualize the underlying process. The Process Cube Explorer, realized by C#, can connect to various databases and load multidimensional event logs in which the characteristics of the process-instance are modelled as dimensions in a datawarehouse-like schema.
Implementation (ProM)
Inter-Level Replayer plug-in for the Security package. Paper. Dataset.
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