Discovering Entities in Process Execution Logs

Töö on kirjutatud protsessikaeve valdkonnas Artefaktikeskse teenuste koosvõime projekti (ACSI) raames. Töö eesmärgiks oli luua meetod sündmuste logidest olemite avastamiseks ja seda meetodit rakendada. Loodud meetod on kirjutatud Javas ning kujutab endast pluginat ProM raamistikule. ProM on geneeriline avatud lähtekoodiga Java raamistik protsessikaeve algoritmide rakendamiseks pluginatena. Olemite leidmise protsessi saab jaotada järgmisteks sammudeks: 1. Integreerimine ProM-iga. 2. Sisendandmetest (XES formaadis logifailidest) sündmuste tüüpide relatsioonide koostamine. 3. Funktsionaalsete sõltuvuste leidmine sündmuste logide relatsioonilisest esitusest. Funktsionaalsete sõltuvuste leidmiseks kasutatakse algoritmi TANE. 4. Funktsionaalsete sõltuvuste alusel kandidaatvõtmete leidmine. Kui relatsioonil on mitu kandidaatvõtit, palutakse kasutajal valida neist üks primaarseks võtmeks. 5. Sama primaarse võtmega sündmustest moodustatakse üks olem. 6. Kasutajale esitatakse töö käigus moodustatud olemid väljundina või saadetakse need järgmisele algoritmile töötlemiseks. Meetodit testiti kahe logifaili puhul, milles olid andmed CD-poe näitel. Meetod töötas mõlema logifaili puhul korrektselt.The thesis is written in the field of process mining and in the frames of Artifact-Centric Service Interoperation (ACSI) project. The goal of the thesis was to create a method for discovering entities in process execution logs and to implement this method. The method is implemented as plugin for ProM open source process mining framework and is written in Java. This implementation can be divided into the following steps: 1. Integration with ProM. 2. Extracting the event type tables from the raw log input. 3. Finding functional dependencies from relational representation of event logs. The functional dependencies are found using an algorithm called TANE. 4. Finding the candidate keys from the functional dependencies. In case a relation has multiple candidate keys, the user is prompted to select one as primary key. 5. Grouping together the event types that have the same primary keys and integrating them into one entity. 6. The output is shown to the user or the entities are sent to another algorithm. Two different event log files were used to test this method. Both of these logs are based on the example of online CD-shop. The method was working correclty for the both event logs

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

Ontology-Driven Extraction of Event Logs from Relational Databases

\u3cp\u3eProcess mining is an emerging discipline whose aim is to discover, monitor and improve real processes by extracting knowledge from event logs representing actual process executions in a given organizational setting. In this light, it can be applied only if faithful event logs, adhering to accepted standards (such as XES), are available. In many real-world settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology. In this work, we devise a novel framework that supports domain experts in the extraction of XES event log information from legacy relational databases, and consequently enables the application of standard process mining tools on such data. Differently from previous work, the extraction is driven by a conceptual representation of the domain of interest in terms of an ontology. On the one hand, this ontology is linked to the underlying legacy data leveraging the well-established ontology-based data access (OBDA) paradigm. On the other hand, our framework allows one to enrich the ontology through user-oriented log extraction annotations, which can be flexibly used to provide different log-oriented views over the data. Different data access modes are then devised so as to view the legacy data through the lens of XES.\u3c/p\u3

Discovering mapping between artifact-centric business process models and execution logs

Klassikaliselt on kirjeldatud töövoogusi protsessidele orienteeritud kujul, kus keskendutakse tervele töövoole ja tegevustele selles. Hiljuti on esile kerkinud uudne, artefakti keskne modelleerimine, kus on oluliseks just äriobjektid ning nende vahelised seosed. Artefakti põhised meetodid nõuavad ka muudatusi protsessianalüüsi tehnikates. Üks võimalik protsesside analüüsi meetod on käivituslogide vastavuse kontrollimine protsessi mudeliga, mille abil saab tuvastada kas süsteem käitub nii nagu planeeritud. Mudeli ja logide vastavuse kontrollimiseks on vaja teada, millised sündmused logides vastavad millistele tegevustele mudelis. Töö eemärgiks on automaatselt tuvastada seosed artefakti põhiste protsessimudelites olevate tegevuste ja töövoosüsteemi logides olevate sündmuste vahel. Selline seose tuvastamine pole triviaalne, kuna võib esineda, et sündmuste nimed logides ja tegevuste nimed mudelis ei ole vastavuses. Näiteks ei jälgita samasid standardeid nimetamisel. Samuti on vaja seoste automaatne tuletamine, kui on teada, et logide ja mudeli vahel on mittesobivused ning kõiki sündmuseid ja tegevusi ei saagi vastavusse viia. Automaatne tuvastamine aitab lihtsustada kasutaja tööd. Lahenduseks pakutud meetod kasutab sisendina Procleti põhist mudelit ja käivituslogi süsteemist. Et leida seos mudeli ja logide vahel, viiakse mõlemad graafi kujule. Seosed leitakse iga artefakti kohta eraldi ning ei kasutata infot nende omavahelise suhtluse kohta. Iga artefakti kohta eraldatakse nende Petri võrk ning koostatakse käitumisrelatsioonid, mis väljendavad kuidas on tegevused antud artefaktis omavahel seotud. Sellest koostatakse graaf, mille tippudeks saavad tegevused ning kaarteks tippude vahel käitumisseosed nende vahel. Analoogselt koostatakse graaf iga logis esinenud olemi kohta. Kasutaja poolt sisestatud olemite ja artefaktide tüüpide vahelise seoste abil leitakse iga vastava olemi ja artefakti isendi tegevuste ja sündmuste vahelised seosed. Seoste leidmine taandub kahe graafi vaheliste tippude kujutuse leidmisele. Seoste leidmiseks esmalt arvutatakse sarnasused tegevuste nimede vahel ning selle põhjal leitakse kujutus, mis minimiseeriks teisenduskaugust graafide vahel antud kujutuse põhjal. Kujutuse leimiseks kasutatakse ahnet algoritmi. Praktilise eksperimendina testiti meetodit erinevate mudelite ja logide kombinatsioonidel. Tulemused näitavad, et meetod on võimeline seoseid leidma, kuid tulemuste kvaliteet sõltub palju tegevuste ja sündmuste nimede sarnasusest ja vähem struktuurilisest sarnasustest

Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review

Recent years have seen the emergence of object-centric process mining techniques. Born as a response to the limitations of traditional process mining in analyzing event data from prevalent information systems like CRM and ERP, these techniques aim to tackle the deficiency, convergence, and divergence issues seen in traditional event logs. Despite the promise, the adoption in real-world process mining analyses remains limited. This paper embarks on a comprehensive literature review of object-centric process mining, providing insights into the current status of the discipline and its historical trajectory

Discovering interacting artifacts from ERP systems (extended version)

The omnipresence of using Enterprise Resource Planning (ERP) systems to support business processes has enabled recording a great amount of (relational) data which contains information about the behaviors of these processes. Various process mining techniques have been proposed to analyze recorded information about process executions. However, classic process mining techniques generally require a linear event log as input and not a multi-dimensional relational database used by ERP systems. Much research has been conducted into converting a relational data source into an event log. Most conversion approaches found in literature usually assume a clear notion of a case and a unique case identifier in an isolated process. This assumption does not hold in ERP systems where processes comprise the life-cycles of various interrelated data objects, instead of a single process. In this paper, a new semi-automatic approach is presented to discover from the plain database of an ERP system the various objects supporting the system. More precisely, we identify an artifact-centric process model describing the system’s objects, their life-cycles, and detailed information about how the various objects synchronize along their life-cycles, called interactions. In addition, our artifact-centric approach helps to eliminate ambiguous dependencies in discovered models caused by the data divergence and convergence problems and to identify the exact "abnormal flows". The presented approach is implemented and evaluated on two processes of ERP systems through case studies

Объектно-ориентированная проверка соответствия модели на основе воспроизведения журнала событий: выявление желаемого поведения и локальных отклонений

Conformance checking methods diagnose to which extent a real system, whose behavior is recorded in an event log, complies with its specification model, e.g., a Petri net. Nonetheless, the majority of these methods focus on checking isolated process instances, neglecting interaction between instances in a system. Addressing this limitation, a series of object-centric approaches have been proposed in the field of process mining. These approaches are based on the holistic analysis of the multiple process instances interacting in a system, where each instance is centered on the handling of an object. Inspired by the object-centric paradigm, this paper presents a replay-based conformance checking method which uses a class of colored Petri nets (CPNs) -- a Petri net extension where tokens in the model carry values of some types (colors). Particularly, we consider conservative workflow CPNs which allow to describe the expected behavior of a system whose components are centered on the end-to-end processing of distinguishable objects. For describing a system’s real behavior, we consider event logs whose events have sets of objects involved in the execution of activities. For replay, we consider a jump strategy where tokens absent from input places of a transition to fire move from their current place of the model to the requested places. Token jumps allow to identify desire lines, i.e., object paths unforeseen in the specification. Also, we introduce local diagnostics based on the proportion of jumps in specific model components. The metrics allow to inform the severity of deviations in precise system parts. Finally, we report experiments supported by a prototype of our method. To show the practical value of our method, we employ a case study on trading systems, where orders from users are matched to trade.Методы проверки соответствия позволяют установить, в какой степени реальная система, поведение которой регистрируется в журнале событий, соответствует ее модели, например, в виде сети Петри. Большинство таких методов направлены на проверку изолированных экземпляров процесса и игнорируют взаимодействие между экземплярами в системе. Для преодоления этого ограничения в области интеллектуального анализа данных был предложен ряд объектно-ориентированных подходов. Эти подходы основаны на целостном анализе нескольких экземпляров процесса, взаимодействующих в системе, где каждый экземпляр соответствует некоторому объекту. В этой статье объектно-ориентированный подход применяется к методу проверки соответствия между журналами событий и цветными сетями Петри (CPN) -- расширением сетей Петри, в котором фишки в модели представляют собой значения некоторых типов (цветов). В частности, мы рассматриваем консервативные CPN потоков работ, которые позволяют описывать ожидаемое поведение системы, в которой компоненты соответствуют обработке различных объектов. Реальное поведение системы описано в журнале событий, в котором события атрибутированы участвующими в них объектами. Для воспроизведения журнала событий мы используем стратегию прыжков, когда фишки, необходимые для срабатывания перехода, перемещаются из своих текущих позиций во входные позиции этого перехода. Прыжки фишек позволяют идентифицировать линии желаний, то есть поведения объектов, не предусмотренные в спецификации. Также мы представляем локальную диагностику, основанную на доле прыжков в поведении конкретных компонент модели. Эти метрики позволяют судить о серьезности отклонений в тех или иных частях системы. Наконец, мы приводим эксперименты, выполненные с помощью программного прототипа. Практическая ценность нашего метода показана на примере моделирования торговых систем, при котором устанавливаются соответствия между заявками пользователей и сделками