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

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

Artifact-centric business process models in UML : specification and reasoning

Business processes are directly involved in the achievement of an organization's goals, and for this reason they should be performed in the best possible way. Modeling business processes can help to achieve this as, for instance, models can facilitate the communication between the people involved in the process, they provide a basis for process improvement and they can help perform process management. Processes can be modeled from many different perspectives. Traditional process modeling has followed the process-centric (or activity-centric) perspective, where the focus is on the sequencing of activities (i.e. the control flow), largely ignoring or underspecifying the data required by these tasks. In contrast, the artifact-centric (or data-centric) approach to process modeling focuses on defining the data required by the tasks and the details of the tasks themselves in terms of the changes they make to the data. The BALSA framework defines four dimensions which should be represented in any artifact-centric business process model: business artifacts, lifecycle, services (i.e. tasks) and associations. Using different types of models to represent these dimensions will result in distinct representations, whose differing characteristics (e.g. the degree of formality or understandability) will make them more appropriate for one purpose or another. Considering this, in the first part of this thesis we propose a framework, BAUML, for modeling business processes following an artifact-centric perspective. This framework is based on using a combination of UML and OCL models, and its goal is to have a final representation of the process which is both understandable and formal, to avoid ambiguities and errors. However, once a process model has been defined, it is important to ensure its quality. This will avoid the propagation of errors to the process's implementation. Although there are many different quality criteria, we focus on the semantic correctness of the model, answering questions such as "does it represent reality correctly?" or "are there any errors and contradictions in it?". Therefore, the second part of this thesis is concerned with finding a way to determine the semantic correctness of our BAUML models. We are interested in considering the BAUML model as a whole, including the meaning of the tasks. To do so, we first translate our models into a well-known framework, a DCDS (Data-centric Dynamic System) to which then modelchecking techniques can be applied. However, DCDSs have been defined theoretically and there is no tool that implements them. For this reason, we also created a prototype tool, AuRUS-BAUML, which is able to translate our BAUML models into logic and to reason on their semantic correctness using an existing tool, SVTe. The integration between AuRUS-BAUML and SVTe is transparent to the user. Logically, the thesis also presents the logic translation which is performed by the tool.Els processos de negoci estan directament relacionats amb els objectius de negoci, i per tant és important que aquests processos es duguin a terme de la millor manera possible. Optar per modelar-los pot ajudar a aconseguir-ho, ja que els models proporcionen nombrosos avantatges. Per exemple: faciliten la comunicació entre les parts involucrades en el procés, proporcionen una base a partir del qual millorar-lo, i poden ajudar a gestionar-lo. Els processos es poden modelar des de diferents perspectives. El modelat tradicional de processos s'ha basat molt en la perspectiva anomenada "process-centric" (centrada en processos) o "activity-centric" (centrada en activitats), que posa l'èmfasi en la seqüència d'activitats o tasques que s'han d'executar, ignorant en gran mesura les dades necessàries per dur a terme aquestes tasques. Per altra banda, la perspectiva "artifact-centric" (centrada en artefactes) o "data-centric" es basa en definir les dades que necessiten les tasques i els detalls de les tasques en si, representant els canvis que aquestes fan a les dades. El framework BALSA defineix quatre dimensions que haurien de representar-se en qualsevol model artifact-centric: els artefactes de negoci (business artifacts), els cicles de vida (lifecycles), els serveis (services) i les associacions (associations). Utilitzant diferents tipus de models per representar aquestes dimensions porta a obtenir diverses representacions amb característiques diferents. Aquesta varietat de característiques farà que els models resultants siguin més apropiats per un propòsit o per un altre. Considerant això, en la primera part d'aquesta tesi proposem un framework, BAUML, per modelar processos de negoci seguint una perspectiva artifact-centric. El framework es basa en utilitzar una combinació de models UML i OCL, i el seu objectiu és obtenir una representació final del procés que sigui a la vegada comprensible i formal, per tal d'evitar ambigüitats i errors. Un cop definit el procés, és important assegurar-ne la qualitat. Això evitarà la propagació d'errors a la implementació final del procés. Malgrat que hi ha molts criteris de qualitat diferents, ens centrarem en la correctesa semàntica del model, per respondre a preguntes com ara "representa la realitat correctament?" o "conté errors o contradiccions?". En conseqüència, la segona part d'aquesta tesi se centra en buscar una manera per determinar la correctesa semàntica d'un model BAUML. Ens interessa considerar el model com un tot, incloent el significat de les tasques (és a dir, el detall del que fan). Per aconseguir-ho, primer traduïm les tasques a un framework reconegut, DCDSs (Data-centric Dynamic Systems). Un cop obtingut, s'hi poden aplicar tècniques de model-checking per determinar si compleix certes propietats. Malauradament, els DCDSs s'han definit a nivell teòric i no hi ha cap eina que els implementi. Per aquest motiu, hem creat un prototip d'eina, AuRUS-BAUML, que és capaç de traduir els nostres models BAUML a lògica i aplicar-hi tècniques de raonament per determinar-ne la correctesa semàntica. Per la part de raonament, l'AuRUS-BAUML fa servir una eina existent, l'SVTe. La integració entre l'AuRUS-BAUML i l'SVTe és transparent de cara a l'usuari. Lògicament, la tesi també presenta la traducció a lògica que porta a terme l'eina

Artifact-centric business process models in UML : specification and reasoning

Business processes are directly involved in the achievement of an organization's goals, and for this reason they should be performed in the best possible way. Modeling business processes can help to achieve this as, for instance, models can facilitate the communication between the people involved in the process, they provide a basis for process improvement and they can help perform process management. Processes can be modeled from many different perspectives. Traditional process modeling has followed the process-centric (or activity-centric) perspective, where the focus is on the sequencing of activities (i.e. the control flow), largely ignoring or underspecifying the data required by these tasks. In contrast, the artifact-centric (or data-centric) approach to process modeling focuses on defining the data required by the tasks and the details of the tasks themselves in terms of the changes they make to the data. The BALSA framework defines four dimensions which should be represented in any artifact-centric business process model: business artifacts, lifecycle, services (i.e. tasks) and associations. Using different types of models to represent these dimensions will result in distinct representations, whose differing characteristics (e.g. the degree of formality or understandability) will make them more appropriate for one purpose or another. Considering this, in the first part of this thesis we propose a framework, BAUML, for modeling business processes following an artifact-centric perspective. This framework is based on using a combination of UML and OCL models, and its goal is to have a final representation of the process which is both understandable and formal, to avoid ambiguities and errors. However, once a process model has been defined, it is important to ensure its quality. This will avoid the propagation of errors to the process's implementation. Although there are many different quality criteria, we focus on the semantic correctness of the model, answering questions such as "does it represent reality correctly?" or "are there any errors and contradictions in it?". Therefore, the second part of this thesis is concerned with finding a way to determine the semantic correctness of our BAUML models. We are interested in considering the BAUML model as a whole, including the meaning of the tasks. To do so, we first translate our models into a well-known framework, a DCDS (Data-centric Dynamic System) to which then modelchecking techniques can be applied. However, DCDSs have been defined theoretically and there is no tool that implements them. For this reason, we also created a prototype tool, AuRUS-BAUML, which is able to translate our BAUML models into logic and to reason on their semantic correctness using an existing tool, SVTe. The integration between AuRUS-BAUML and SVTe is transparent to the user. Logically, the thesis also presents the logic translation which is performed by the tool.Els processos de negoci estan directament relacionats amb els objectius de negoci, i per tant és important que aquests processos es duguin a terme de la millor manera possible. Optar per modelar-los pot ajudar a aconseguir-ho, ja que els models proporcionen nombrosos avantatges. Per exemple: faciliten la comunicació entre les parts involucrades en el procés, proporcionen una base a partir del qual millorar-lo, i poden ajudar a gestionar-lo. Els processos es poden modelar des de diferents perspectives. El modelat tradicional de processos s'ha basat molt en la perspectiva anomenada "process-centric" (centrada en processos) o "activity-centric" (centrada en activitats), que posa l'èmfasi en la seqüència d'activitats o tasques que s'han d'executar, ignorant en gran mesura les dades necessàries per dur a terme aquestes tasques. Per altra banda, la perspectiva "artifact-centric" (centrada en artefactes) o "data-centric" es basa en definir les dades que necessiten les tasques i els detalls de les tasques en si, representant els canvis que aquestes fan a les dades. El framework BALSA defineix quatre dimensions que haurien de representar-se en qualsevol model artifact-centric: els artefactes de negoci (business artifacts), els cicles de vida (lifecycles), els serveis (services) i les associacions (associations). Utilitzant diferents tipus de models per representar aquestes dimensions porta a obtenir diverses representacions amb característiques diferents. Aquesta varietat de característiques farà que els models resultants siguin més apropiats per un propòsit o per un altre. Considerant això, en la primera part d'aquesta tesi proposem un framework, BAUML, per modelar processos de negoci seguint una perspectiva artifact-centric. El framework es basa en utilitzar una combinació de models UML i OCL, i el seu objectiu és obtenir una representació final del procés que sigui a la vegada comprensible i formal, per tal d'evitar ambigüitats i errors. Un cop definit el procés, és important assegurar-ne la qualitat. Això evitarà la propagació d'errors a la implementació final del procés. Malgrat que hi ha molts criteris de qualitat diferents, ens centrarem en la correctesa semàntica del model, per respondre a preguntes com ara "representa la realitat correctament?" o "conté errors o contradiccions?". En conseqüència, la segona part d'aquesta tesi se centra en buscar una manera per determinar la correctesa semàntica d'un model BAUML. Ens interessa considerar el model com un tot, incloent el significat de les tasques (és a dir, el detall del que fan). Per aconseguir-ho, primer traduïm les tasques a un framework reconegut, DCDSs (Data-centric Dynamic Systems). Un cop obtingut, s'hi poden aplicar tècniques de model-checking per determinar si compleix certes propietats. Malauradament, els DCDSs s'han definit a nivell teòric i no hi ha cap eina que els implementi. Per aquest motiu, hem creat un prototip d'eina, AuRUS-BAUML, que és capaç de traduir els nostres models BAUML a lògica i aplicar-hi tècniques de raonament per determinar-ne la correctesa semàntica. Per la part de raonament, l'AuRUS-BAUML fa servir una eina existent, l'SVTe. La integració entre l'AuRUS-BAUML i l'SVTe és transparent de cara a l'usuari. Lògicament, la tesi també presenta la traducció a lògica que porta a terme l'eina.Postprint (published version

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

Explainable Predictive and Prescriptive Process Analytics of customizable business KPIs

Recent years have witnessed a growing adoption of machine learning techniques for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring. Predictive analytics leverages machine learning and data analytics techniques to predict the future outcome of a process based on historical data. Therefore, the goal of predictive analytics is to identify future trends, and discover potential issues and anomalies in the process before they occur, allowing organizations to take proactive measures to prevent them from happening, optimizing the overall performance of the process. Prescriptive analytics systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running process in order to improve the outcome of a process, which can be defined in various ways depending on the business goals; this can involve measuring process-specific Key Performance Indicators (KPIs), such as costs, execution times, or customer satisfaction, and using this data to make informed decisions about how to optimize the process. This Ph.D. thesis research work has focused on predictive and prescriptive analytics, with particular emphasis on providing predictions and recommendations that are explainable and comprehensible to process actors. In fact, while the priority remains on giving accurate predictions and recommendations, the process actors need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way and they need to be convinced that the recommended actions are the most suitable ones to maximize the KPI of interest; otherwise, users would not trust and follow the provided predictions and recommendations, and the predictive technology would not be adopted.Recent years have witnessed a growing adoption of machine learning techniques for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring. Predictive analytics leverages machine learning and data analytics techniques to predict the future outcome of a process based on historical data. Therefore, the goal of predictive analytics is to identify future trends, and discover potential issues and anomalies in the process before they occur, allowing organizations to take proactive measures to prevent them from happening, optimizing the overall performance of the process. Prescriptive analytics systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running process in order to improve the outcome of a process, which can be defined in various ways depending on the business goals; this can involve measuring process-specific Key Performance Indicators (KPIs), such as costs, execution times, or customer satisfaction, and using this data to make informed decisions about how to optimize the process. This Ph.D. thesis research work has focused on predictive and prescriptive analytics, with particular emphasis on providing predictions and recommendations that are explainable and comprehensible to process actors. In fact, while the priority remains on giving accurate predictions and recommendations, the process actors need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way and they need to be convinced that the recommended actions are the most suitable ones to maximize the KPI of interest; otherwise, users would not trust and follow the provided predictions and recommendations, and the predictive technology would not be adopted

At the crossroads of big science, open science, and technology transfer

Les grans infraestructures científiques s’enfronten a demandes creixents de responsabilitat pública, no només per la seva contribució al descobriment científic, sinó també per la seva capacitat de generar valor econòmic secundari. Per construir i operar les seves infraestructures sofisticades, sovint generen tecnologies frontereres dissenyant i construint solucions tècniques per a problemes d’enginyeria complexos i sense precedents. En paral·lel, la dècada anterior ha presenciat la ràpida irrupció de canvis tecnològics que han afectat la manera com es fa i es comparteix la ciència, cosa que ha comportat l’emergència del concepte d’Open Science (OS). Els governs avancen ràpidament vers aquest paradigma de OS i demanen a les grans infraestructures científiques que "obrin" els seus processos científics. No obstant, aquestes dues forces s'oposen, ja que la comercialització de tecnologies i resultats científics requereixen normalment d’inversions financeres importants i les empreses només estan disposades a assumir aquest cost si poden protegir la innovació de la imitació o de la competència deslleial. Aquesta tesi doctoral té com a objectiu comprendre com les noves aplicacions de les TIC afecten els resultats de la recerca i la transferència de tecnologia resultant en el context de les grans infraestructures científiques. La tesis pretén descobrir les tensions entre aquests dos vectors normatius, així com identificar els mecanismes que s’utilitzen per superar-les. La tesis es compon de quatre estudis: 1) Un estudi que aplica un mètode de recerca mixt que combina dades de dues enquestes d’escala global realitzades online (2016, 2018), amb dos cas d’estudi de dues comunitats científiques en física d’alta energia i biologia molecular que avaluen els factors explicatius darrere les pràctiques de compartir dades per part dels científics; 2) Un estudi de cas d’Open Targets, una infraestructura d’informació basada en dades considerades bens comuns, on el Laboratori Europeu de Biologia Molecular-EBI i empreses farmacèutiques col·laboren i comparteixen dades científiques i eines tecnològiques per accelerar el descobriment de medicaments; 3) Un estudi d’un conjunt de dades únic de 170 projectes finançats en el marc d’ATTRACT (un nou instrument de la Comissió Europea liderat per les grans infraestructures científiques europees) que té com a objectiu comprendre la naturalesa del procés de serendipitat que hi ha darrere de la transició de tecnologies de grans infraestructures científiques a aplicacions comercials abans no anticipades. ; i 4) un cas d’estudi sobre la tecnologia White Rabbit, un hardware sofisticat de codi obert desenvolupat al Consell Europeu per a la Recerca Nuclear (CERN) en col·laboració amb un extens ecosistema d’empreses.Las grandes infraestructuras científicas se enfrentan a crecientes demandas de responsabilidad pública, no solo por su contribución al descubrimiento científico sino también por su capacidad de generar valor económico para la sociedad. Para construir y operar sus sofisticadas infraestructuras, a menudo generan tecnologías de vanguardia al diseñar y construir soluciones técnicas para problemas de ingeniería complejos y sin precedentes. Paralelamente, la década anterior ha visto la irrupción de rápidos cambios tecnológicos que afectan la forma en que se genera y comparte la ciencia, lo que ha llevado a acuñar el concepto de Open Science (OS). Los gobiernos se están moviendo rápidamente hacia este nuevo paradigma y están pidiendo a las grandes infraestructuras científicas que "abran" el proceso científico. Sin embargo, estas dos fuerzas se oponen, ya que la comercialización de tecnología y productos científicos generalmente requiere importantes inversiones financieras y las empresas están dispuestas a asumir este coste solo si pueden proteger la innovación de la imitación o la competencia desleal. Esta tesis doctoral tiene como objetivo comprender cómo las nuevas aplicaciones de las TIC están afectando los resultados científicos y la transferencia de tecnología resultante en el contexto de las grandes infraestructuras científicas. La tesis pretende descubrir las tensiones entre estas dos fuerzas normativas e identificar los mecanismos que se emplean para superarlas. La tesis se compone de cuatro estudios: 1) Un estudio que emplea un método mixto de investigación que combina datos de dos encuestas de escala global realizadas online (2016, 2018), con dos caso de estudio sobre dos comunidades científicas distintas -física de alta energía y biología molecular- que evalúan los factores explicativos detrás de las prácticas de intercambio de datos científicos; 2) Un caso de estudio sobre Open Targets, una infraestructura de información basada en datos considerados como bienes comunes, donde el Laboratorio Europeo de Biología Molecular-EBI y compañías farmacéuticas colaboran y comparten datos científicos y herramientas tecnológicas para acelerar el descubrimiento de fármacos; 3) Un estudio de un conjunto de datos único de 170 proyectos financiados bajo ATTRACT, un nuevo instrumento de la Comisión Europea liderado por grandes infraestructuras científicas europeas, que tiene como objetivo comprender la naturaleza del proceso fortuito detrás de la transición de las tecnologías de grandes infraestructuras científicas a aplicaciones comerciales previamente no anticipadas ; y 4) un estudio de caso de la tecnología White Rabbit, un sofisticado hardware de código abierto desarrollado en el Consejo Europeo de Investigación Nuclear (CERN) en colaboración con un extenso ecosistema de empresas.Big science infrastructures are confronting increasing demands for public accountability, not only within scientific discovery but also their capacity to generate secondary economic value. To build and operate their sophisticated infrastructures, big science often generates frontier technologies by designing and building technical solutions to complex and unprecedented engineering problems. In parallel, the previous decade has seen the disruption of rapid technological changes impacting the way science is done and shared, which has led to the coining of the concept of Open Science (OS). Governments are quickly moving towards the OS paradigm and asking big science centres to "open up” the scientific process. Yet these two forces run in opposition as the commercialization of scientific outputs usually requires significant financial investments and companies are willing to bear this cost only if they can protect the innovation from imitation or unfair competition. This PhD dissertation aims at understanding how new applications of ICT are affecting primary research outcomes and the resultant technology transfer in the context of big and OS. It attempts to uncover the tensions in these two normative forces and identify the mechanisms that are employed to overcome them. The dissertation is comprised of four separate studies: 1) A mixed-method study combining two large-scale global online surveys to research scientists (2016, 2018), with two case studies in high energy physics and molecular biology scientific communities that assess explanatory factors behind scientific data-sharing practices; 2) A case study of Open Targets, an information infrastructure based upon data commons, where European Molecular Biology Laboratory-EBI and pharmaceutical companies collaborate and share scientific data and technological tools to accelerate drug discovery; 3) A study of a unique dataset of 170 projects funded under ATTRACT -a novel policy instrument of the European Commission lead by European big science infrastructures- which aims to understand the nature of the serendipitous process behind transitioning big science technologies to previously unanticipated commercial applications; and 4) a case study of White Rabbit technology, a sophisticated open-source hardware developed at the European Council for Nuclear Research (CERN) in collaboration with an extensive ecosystem of companies