Journal ArticleDOI
Dealing With Concept Drifts in Process Mining
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TLDR
A generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed are presented and used to discover differences between successive populations.Abstract:
Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality.read more
Citations
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Journal ArticleDOI
A survey on concept drift adaptation
TL;DR: The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
Book ChapterDOI
An Overview of Concept Drift Applications
TL;DR: This chapter provides an application oriented view towards concept drift research, with a focus on supervised learning tasks, and constructs a reference framework for positioning application tasks within a spectrum of problems related to concept drift.
Journal ArticleDOI
Process mining techniques and applications – A systematic mapping study
Cleiton dos Santos Garcia,Alex Meincheim,Elio Ribeiro Faria Junior,Marcelo Rosano Dallagassa,Denise Maria Vecino Sato,Denise Maria Vecino Sato,Deborah Ribeiro Carvalho,Eduardo Alves Portela Santos,Edson Emílio Scalabrin +8 more
TL;DR: It is possible to observe that the most active research topics are associated with the process discovery algorithms, followed by conformance checking, and architecture and tools improvements, and finally application domains among different business segments are reported on.
Journal ArticleDOI
A New Method for Data Stream Mining Based on the Misclassification Error
TL;DR: A theorem is proven showing that the best attribute computed in considered node according to the available data sample is the same, with some high probability, as the attribute derived from the whole infinite data stream.
Replaying history on process models for conformance checking and performance analysis
TL;DR: The importance of maintaining a proper alignment between event log and process model is elaborated on and their application to conformance checking and performance analysis is elaborated.
References
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Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Book
Process Mining: Discovery, Conformance and Enhancement of Business Processes
TL;DR: This book provides real-world techniques for monitoring and analyzing processes in real time and is a powerful new tool destined to play a key role in business process management.
Book
Workflow Management: Models, Methods, and Systems
Wil vanderAalst,Kees M. van Hee +1 more
TL;DR: This book provides a basic overview of workflow terminology and organization, as well as detailed coverage of workflow modeling with Petri nets, to provide a comprehensive introduction to workflow management.
Journal ArticleDOI
Handbook of Parametric and Nonparametric Statistical Procedures
TL;DR: Data Analysis by Resampling is a useful and clear introduction to resampling that would make an ambitious second course in statistics or a good third or later course and is quite well suited for self-study by an individual with just a few previous statistics courses.
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