1,529 research outputs found
Conformance Checking Based on Multi-Perspective Declarative Process Models
Process mining is a family of techniques that aim at analyzing business
process execution data recorded in event logs. Conformance checking is a branch
of this discipline embracing approaches for verifying whether the behavior of a
process, as recorded in a log, is in line with some expected behaviors provided
in the form of a process model. The majority of these approaches require the
input process model to be procedural (e.g., a Petri net). However, in turbulent
environments, characterized by high variability, the process behavior is less
stable and predictable. In these environments, procedural process models are
less suitable to describe a business process. Declarative specifications,
working in an open world assumption, allow the modeler to express several
possible execution paths as a compact set of constraints. Any process execution
that does not contradict these constraints is allowed. One of the open
challenges in the context of conformance checking with declarative models is
the capability of supporting multi-perspective specifications. In this paper,
we close this gap by providing a framework for conformance checking based on
MP-Declare, a multi-perspective version of the declarative process modeling
language Declare. The approach has been implemented in the process mining tool
ProM and has been experimented in three real life case studies
Data-aware Synthetic Log Generation for Declarative Process Models
Äriprotsesside juhtimises on protsessikaeve klass meetodeid, mida kasutatakse protsessi struktuuri õppimiseks täitmislogist. Selle struktuur on esindatud kui protsessi mudel: kas menetluslik või deklaratiivne. Näited deklaratiivsetest keeltest on Declare, DPIL ja DCR Graphs. Selleks, et testida ja parandada protsessi kaevandamise algoritme on vaja palju logisid erinevate parameetritega ja alati ei ole võimalik saada piisavalt reaalseid logisid. See on koht, kus tehislikud logid tulevad kasuks. On olemas meetodeid logi genereerimiseks DPIL-ist ja deklaratiivsetest mudelitest, kuid puuduvad vahendid logi genereerimiseks MPDeclare-ist, mis on multiperspektiivne versioon Declare-ist andmete toega. Käesolev magistritöö käsitleb MP-Declare mudelitest logide genereerimist kasutades kaht erinevat mudelite kontrollijat: Alloy ja NuSMV. Selleks, et parandada jõudlust, optimeerisime kirjanduses saadaval olevaid baaslähenemisi. Kõik käsitletud tehnikad implementeeritakse ja testitakse kasutades saadaval olevat sobivuse testimise tööriistu ja meie enda väljatöötatud teste. Meie generaatorite hindamiseks ja võrdluseks olemasolevate lahendustega mõõtsime me logide genereerimise aega ja seda, kuidas see muutub erinevate parameetrite ja mudelitega. Me töötasime välja erinevad mõõdupuud logide varieeruvuse arvutamiseks ja rakendasime neid uuritavatele generaatoritele.In Business Process Management, process mining is a class of techniques for learning process structure from an execution log. This structure is represented as a process model: either procedural or declarative. Examples of declarative languages are Declare, DPIL and DCR Graphs. In order to test and improve process mining algorithms a lot of logs with different parameters are required, and it is not always possible to get enough real logs. And this is where artificial logs are useful. There exist techniques for log generation from DPIL and declare-based models. But there are no tools for generating logs from MP-Declare – multiperspective version of Declare with data support. This thesis introduces an approach to log generation from MP-Declare models using two different model checkers: Alloy and NuSMV. In order to improve performance, we applied optimization to baseline approaches available in the literature. All of the discussed techniques are implemented and tested using existing conformance checking tools and our tests. To evaluate performance of our generators and compare them with existing ones, we measured time required for generating log and how it changes with different parameters and models. We also designed several metrics for computing log variability, and applied them to reviewed generators
Conformance checking: A state-of-the-art literature review
Conformance checking is a set of process mining functions that compare
process instances with a given process model. It identifies deviations between
the process instances' actual behaviour ("as-is") and its modelled behaviour
("to-be"). Especially in the context of analyzing compliance in organizations,
it is currently gaining momentum -- e.g. for auditors. Researchers have
proposed a variety of conformance checking techniques that are geared towards
certain process model notations or specific applications such as process model
evaluation. This article reviews a set of conformance checking techniques
described in 37 scholarly publications. It classifies the techniques along the
dimensions "modelling language", "algorithm type", "quality metric", and
"perspective" using a concept matrix so that the techniques can be better
accessed by practitioners and researchers. The matrix highlights the dimensions
where extant research concentrates and where blind spots exist. For instance,
process miners use declarative process modelling languages often, but
applications in conformance checking are rare. Likewise, process mining can
investigate process roles or process metrics such as duration, but conformance
checking techniques narrow on analyzing control-flow. Future research may
construct techniques that support these neglected approaches to conformance
checking
Leveraging Multi-Perspective A priori Knowledge in Predictive Business Process Monitoring
Äriprotsesside ennestusseire on valdkond, mis on pühendunud käimasolevate äriprotsesside tuleviku ennustamisele kasutades selleks minevikus sooritatud äriprotsesside kohta käivaid andmeid. Valdav osa uurimustööst selles valdkonnas keskendub ainult seda tüüpi andmetele, jättes tähelepanuta täiendavad teadmised (a priori teadmised) protsessi teostumise kohta tulevikus. Hiljuti pakuti välja lähenemine, mis võimaldab a priori teadmisi kasutada LTL-reeglite näol. Kuid tõsiasjana on antud tehnika limiteeritud äriprotsessi kontroll-voole, jättes välja võimaluse väljendada a priori teadmisi, mis puudutavad lisaks kontrollvoole ka informatsiooni protsessis leiduvate atribuutide kohta (multiperspektiivsed a priori teadmised). Me pakume välja lahenduse, mis võimaldab seda tüüpi teadmiste kasutuse, tehes multiperspektiivseid ennustusi käimasoleva äriprotsessi kohta. Tulemused, milleni jõuti rakendades väljapakutud tehnikat 20-le tehisärilogile ning ühele elulisele ärilogile, näitavad, et meie lähenemine suudab pakkuda konkurentsivõimelisi ennustusi.Predictive business process monitoring is an area dedicated to exploiting past process execution data in order to predict the future unfolding of a currently executed business process instance. Most of the research done in this domain focuses on exploiting the past process execution data only, leaving neglected additional a priori knowledge that might become available at runtime. Recently, an approach was proposed, which allows to leverage a priori knowledge on the control flow in the form of LTL-rules. However, cases exist in which more granular a priori knowledge becomes available about perspectives that go be-yond the pure control flow like data, time and resources (multiperspective a priori knowledge). In this thesis, we propose a technique that enables to leverage multi-perspective a priori knowledge when making predictions of complex sequences, i.e., sequences of events with a subset of the data attributes attached to them. The results, obtained by applying the proposed technique to 20 synthetic logs and 1 real life log, show that the proposed technique is able to overcome state-of-the-art approaches by successfully leveraging multiperspective a priori knowledge
Process Mining Handbook
This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing
Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)
This technical report describes the intersection of process mining and large
language models (LLMs), specifically focusing on the abstraction of traditional
and object-centric process mining artifacts into textual format. We introduce
and explore various prompting strategies: direct answering, where the large
language model directly addresses user queries; multi-prompt answering, which
allows the model to incrementally build on the knowledge obtained through a
series of prompts; and the generation of database queries, facilitating the
validation of hypotheses against the original event log.
Our assessment considers two large language models, GPT-4 and Google's Bard,
under various contextual scenarios across all prompting strategies. Results
indicate that these models exhibit a robust understanding of key process mining
abstractions, with notable proficiency in interpreting both declarative and
procedural process models.
In addition, we find that both models demonstrate strong performance in the
object-centric setting, which could significantly propel the advancement of the
object-centric process mining discipline.
Additionally, these models display a noteworthy capacity to evaluate various
concepts of fairness in process mining. This opens the door to more rapid and
efficient assessments of the fairness of process mining event logs, which has
significant implications for the field.
The integration of these large language models into process mining
applications may open new avenues for exploration, innovation, and insight
generation in the field
Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis
Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools
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