Special Issue "Process Mining and Emerging Applications"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Databases, and Data Structures".

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editor

Dr. Antonella Guzzo
Guest Editor
Computer Engineering, DIMES-Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende 87036, Italy
Interests: process mining; data mining

Special Issue Information

Dear Colleagues,

Process mining is a research field aimed at developing algorithms and methodologies to extract useful knowledge from event data. Process mining methods have been successfully applied to logs of business process execution recorded by transactional IT systems, with the ultimate goal of analyzing and improving organizational productivity along performance dimensions such as efficiency, quality, compliance, and risk. Moreover, such methods are being increasingly used—with an interdisciplinary perspective—in other application domains beyond those related to business processes, such as in the context of distributed ledger technologies (DLT), robotic process automation (RPA), and Internet-of-Things (IoT). This Special Issue aims at providing a high-quality forum for interdisciplinary researchers and practitioners to exchange research findings and ideas on process mining and its applications.

We invite you to submit to this Special Issue on “Process Mining and Emerging Applications”, with subjects covering the whole range from theory to applications. The following is a (non-exhaustive) list of topics of interests:

Process Mining techniques:

  • Automated discovery of process models
  • Conformance/compliance analysis
  • Multiperspective process mining
  • Predictive process analytics
  • Prescriptive process analytics and recommender systems
  • Privacy-preserving process mining
  • Visual process analytics
  • Mining from non-process-aware systems/event streams

We welcome applications and case studies in:

  • Distributed ledger technologies (DLT)
  • (Cyber)security and privacy
  • Risk management
  • Robotic process automation (RPA)
  • Sensors, Internet-of-Things (IoT), and wearable devices
  • Specific domains such as accounting, finance, government, healthcare, and manufacturing

Dr. Antonella Guzzo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Process Mining Algorithms
  • Conformance/compliance analysis
  • Process analytics
  • Event logs analysis

Published Papers (1 paper)

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Open AccessArticle
CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining
Algorithms 2020, 13(7), 161; https://doi.org/10.3390/a13070161 - 03 Jul 2020
Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed [...] Read more.
Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONcept Drift Analysis in Process Mining Framework. A four-staged framework providing guidance on the fundamental components of a concept drift analysis approach in the context of process mining (CONDA-PM,)) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (Systematic Literature Review. A survey of a topic conducted according to systematic steps and adopts a certain format (SLR,)) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts. Full article
(This article belongs to the Special Issue Process Mining and Emerging Applications)
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