Special Issue "Process Mining and Emerging Applications"

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

Deadline for manuscript submissions: closed (15 December 2020).

Special Issue Editor

Dr. Antonella Guzzo
E-Mail Website
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 1400 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.

Keywords

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

Published Papers (6 papers)

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Editorial

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Open AccessEditorial
Special Issue on Process Mining and Emerging Applications
Algorithms 2021, 14(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/a14010013 - 05 Jan 2021
Viewed by 509
Abstract
This article is the editorial of the “Process Mining and Emerging Applications” (https://www [...] Full article
(This article belongs to the Special Issue Process Mining and Emerging Applications)

Research

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Open AccessArticle
Efficient Time and Space Representation of Uncertain Event Data
Algorithms 2020, 13(11), 285; https://0-doi-org.brum.beds.ac.uk/10.3390/a13110285 - 09 Nov 2020
Cited by 1 | Viewed by 686
Abstract
Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches [...] Read more.
Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately captured behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction. Full article
(This article belongs to the Special Issue Process Mining and Emerging Applications)
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Open AccessArticle
Translating Workflow Nets to Process Trees: An Algorithmic Approach
Algorithms 2020, 13(11), 279; https://0-doi-org.brum.beds.ac.uk/10.3390/a13110279 - 02 Nov 2020
Cited by 1 | Viewed by 765
Abstract
Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. [...] Read more.
Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound workflow net is trivial. However, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability. Full article
(This article belongs to the Special Issue Process Mining and Emerging Applications)
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Open AccessArticle
Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach
Algorithms 2020, 13(11), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/a13110267 - 22 Oct 2020
Cited by 1 | Viewed by 594
Abstract
Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety [...] Read more.
Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches. Full article
(This article belongs to the Special Issue Process Mining and Emerging Applications)
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Open AccessArticle
Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework
Algorithms 2020, 13(10), 244; https://0-doi-org.brum.beds.ac.uk/10.3390/a13100244 - 27 Sep 2020
Cited by 1 | Viewed by 829
Abstract
Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business [...] Read more.
Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK’s renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log. Full article
(This article belongs to the Special Issue Process Mining and Emerging Applications)
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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://0-doi-org.brum.beds.ac.uk/10.3390/a13070161 - 03 Jul 2020
Cited by 1 | Viewed by 1503
Abstract
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 (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (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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