Process Mining Supported Process Redesign: Matching Problems with Solutions

Process mining is a widely used technique to understand and analyze business process executions through event data. It offers insights into process problems but leaves analysts barehanded to translate these problems into concrete solutions. Research on business process management discusses both process mining and improvement patterns in isolation. In this paper, we address this research gap. More specifically, we identify six categories of process problems that can be identified with process mining and map them to applicable best practices of business processes. We analyze the relevance of our approach using a thematic analysis of reports that were handed in to the Business Process Intelligence Challenges over recent years, and observe the dire need for better guidance to translate process problems identified by process mining into suitable process designs. Conceptually, we position process mining into the problem and solution space of process redesign and thereby offer a language to describe potentials and limitations of the technique

A Dashboard-based Predictive Process Monitoring Engine

Protsesside jälgimine moodustab keskse osa äriprotsesside juhtimisest. See sisaldab tegevusi, milles kogutakse ja analüüsitakse protsessi täideviimise andmeid, et mõõta protsesside tulemuslikkust, võttes arvesse soorituse eesmärke. Tavaliselt on protsesside jälgimist sooritatud käitluse ajal, võimaldades reaalajalist ülevaadet protsessi sooritusest ja tuvastades protsessi vaidlusküsimused nende tekkimise hetkel. Viimasel ajal logimisvõimetega töövoo juhtimise süsteemide laialdane omaksvõtt on loonud aktiivse andmetest ajendatud ennustava protsesside jälgimise, mis kasutab varasemat protsesside jooksutamise andmestikku, et ennustada käimasolevate äriprotsesside tulevikusuunda. Seega potentsiaalselt hälbiva protsessi kulgu saab ette ennustada ja lahendada. Tüüpiliste protsesside jälgimise probleemidega tegelemiseks on välja pakutud erinevaid lähenemisi, nagu kas parasjagu käiva protsessi instants vastab selle soorituse eesmärkidele või millal instantsiga lõpule jõutakse. Need lähenemised on siiski seni jäänud akadeemilisse valdkonda ning neid pole rakendatud tööstuse sätetesse. Selles lõputöös me disainisime ja teostasime ennustava protsessi jälgimise mootori prototüübi. Arendatud lahendus on konfigureeritav täispinu veebiraamistik, mis võimaldab mitme soorituse indikaatori ennustamist ja mida saab kerge vaevaga laiendada teiste indikaatorite jaoks uute ennustavate mudelitega. Lisaks võimaldab see mitmest äriprotsessist pärinevate sündmusvoogude käsitlemist. Nii ennustuste tulemused kui protsesside täitmise reaalaja statistika kokkuvõtted kuvatakse esipaneelil, mis võimaldab mitut erinevat alternatiivset visualiseerimise valikut. Lahendus on kahte tõsielu äriprotsessi kasutades edukalt valideeritud, arvestades defineeritud funktsionaalseid ja mittefunktsionaalseid nõudeid.Process monitoring forms an integral part of business process management. It involves activities in which process execution data are collected and analyzed to measure the process performance with respect to the performance objectives. Traditionally, process monitoring has been performed at runtime, providing a real-time overview of the process performance and identifying performance issues as they arise. Recently, the rapid adop- tion of workflow management systems with logging capabilities has spawned the active development of data-driven, predictive process monitoring that exploits the historical process execution data to predict the future course of ongoing instances of a business process. Thus, potentially deviant process behavior can be anticipated and proactively addressed.To this end, various approaches have been proposed to tackle typical predictive monitoring problems, such as whether an ongoing process instance will fulfill its per- formance objectives, or when will an instance be completed. However, so far these approaches have largely remained in the academic domain and have not been widely applied in industry settings, mostly due to the lack of software support. In this the- sis, we have designed and implemented a prototype of a predictive process monitor- ing engine. The developed solution, named Nirdizati, is a configurable full-stack web framework that enables the prediction of several performance indicators and is easily extensible with new predictive models for other indicators. In addition, it allows han- dling event streams that originate from multiple business processes. The results of the predictions, as well as the real-time summary statistics about the process execution, are presented in a dashboard that offers multiple alternative visualization options. The dashboard updates periodically based on the arriving stream of events. The solution has been successfully validated with respect to the established functional and non-functional requirements using event streams corresponding to two real-life business processes

Guided Interaction Exploration in Artifact-centric Process Models

Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data we can automatically discover composite state machines representing artifact-centric processes. Moreover, we provide ways of visualizing and quantifying interactions among different artifacts. For example, we are able to highlight strongly correlated behaviours in different artifacts. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data sets, including the personal loan and overdraft process of a Dutch financial institution.Comment: 10 pages, 4 figures, to be published in proceedings of the 19th IEEE Conference on Business Informatics, CBI 201

A Literature Review on Predictive Monitoring of Business Processes

Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field

Discovering Declarative Process Models from Event Logs through Temporal Logic Query Checking

Käesolev magistritöö keskendub protsessile seatud piirangute avastamisele sündmuste logist, mida saab väljendada temporaalloogika abil. Piirangute avastamise meetodina kasutame temporaalloogika päringute kontrollimist sündmuste logi vastu. Temporaalloogika päring on modaalloogika avaldis, mis sisaldab muutujaid, mis võtavad oma väärtuse automaarpropositsioonide hulgast. Temporaalloogika päring käivitatakse vastu olekumasinat, mis on konstrueeritud sündmuste logi järgi. Päringu tulemuseks on kõik temporaalloogika avaldised, kus muutujad on asendatud kõikvõimalike automaarpropositsioonidega, mis muudavad avaldise tõeseks antud olekumasinas. See meetod ei vaja protsessi piirangute avastamiseks negatiivseid näiteid (protsessi juhtumid, mis ei tohi aset leida) sündmuste logis nagu osa avaldatuid meetodeid vajab. See meetod samuti laiendab võimalike avastatavate piirangute hulka võrreldes olemas olevate meetoditega.This thesis will focus on the discovery of temporal logic constraints from an event log. The constraints are the description of the behavior of a business process. We will use Temporal Logic Query Checking for this purpose. A temporal logic query is a type of modal logic expression containing one or more placeholders that are checked against a transition system. The transition system is built from an event log. The result lists all possible activities that can replace the placeholders to satisfy the constraints described by the query in the log. This approach does not require (as many other approaches in the literature) negative examples as (additional) input and it provides the possibility of discovering a wider range of constraints to describe the process with respect to the existing approaches

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included