Process Mining Manifesto
- Wil van der Aalst
- Arya Adriansyah
- Ana Karla Alves de Medeiros
- Franco Arcieri
- Thomas Baier
- Tobias Blickle
- Jagadeesh Chandra Bose
- Peter van den Brand
- Ronald Brandtjen
- Joos Buijs
- Andrea Burattin
- Josep Carmona
- Malu Castellanos
- Jan Claes
- Jonathan Cook
- Nicola Costantini
- Francisco Curbera
- Ernesto Damiani
- Massimiliano de Leoni
- Pavlos Delias
- Boudewijn F. van Dongen
- Marlon Dumas
- Schahram Dustdar
- Dirk Fahland
- Diogo R. Ferreira
- Walid Gaaloul
- Frank van Geffen
- Sukriti Goel
- Christian Günther
- Antonella Guzzo
- Paul Harmon
- Arthur ter Hofstede
- John Hoogland
- Jon Espen Ingvaldsen
- Koki Kato
- Rudolf Kuhn
- Akhil Kumar
- Marcello La Rosa
- Fabrizio Maggi
- Donato Malerba
- Ronny S. Mans
- Alberto Manuel
- Martin McCreesh
- Paola Mello
- Jan Mendling
- Marco Montali
- Hamid R. Motahari-Nezhad
- Michael zur Muehlen
- Jorge Munoz-Gama
- Luigi Pontieri
- Joel Ribeiro
- Anne Rozinat
- Hugo Seguel Pérez
- Ricardo Seguel Pérez
- Marcos Sepúlveda
- Jim Sinur
- Pnina Soffer
- Minseok Song
- Alessandro Sperduti
- Giovanni Stilo
- Casper Stoel
- Keith Swenson
- Maurizio Talamo
- Wei Tan
- Chris Turner
- Jan Vanthienen
- George Varvaressos
- Eric Verbeek
- Marc Verdonk
- Roberto Vigo
- Jianmin Wang
- Barbara Weber
- Matthias Weidlich
- Ton Weijters
- Lijie Wen
- Michael Westergaard
- Moe Wynn
Abstract
Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.
Keywords
Process Mining Business Process Management Business Intelligence Concept Drift Process Instance
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
1.
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)
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