A Big Data perspective on Cyber-Physical Systems for Industry 4.0: modernizing and scaling complex event processing

Doctoral program in Advanced Engineering Systems for IndustryNowadays, the whole industry makes efforts to find the most productive ways of working and it already understood that using the data that is being produced inside and outside the factories is a way to improve the business performance. A set of modern technologies combined with sensor-based communication create the possibility to act according to our needs, precisely at the moment when the data is being produced and processed. Considering the diversity of processes existing in a factory, all of them producing data, Complex Event Processing (CEP) with the capabilities to process that amount of data is needed in the daily work of a factory, to process different types of events and find patterns between them. Although the integration of the Big Data and Complex Event Processing topics is already present in the literature, open challenges in this area were identified, hence the reason for the contribution presented in this thesis. Thereby, this doctoral thesis proposes a system architecture that integrates the CEP concept with a rulebased approach in the Big Data context: the Intelligent Event Broker (IEB). This architecture proposes the use of adequate Big Data technologies in its several components. At the same time, some of the gaps identified in this area were fulfilled, complementing Event Processing with the possibility to use Machine Learning Models that can be integrated in the rules' verification, and also proposing an innovative monitoring system with an immersive visualization component to monitor the IEB and prevent its uncontrolled growth, since there are always several processes inside a factory that can be integrated in the system. The proposed architecture was validated with a demonstration case using, as an example, the Active Lot Release Bosch's system. This demonstration case revealed that it is feasible to implement the proposed architecture and proved the adequate functioning of the IEB system to process Bosch's business processes data and also to monitor its components and the events flowing through those components.Hoje em dia as indústrias esforçam-se para encontrar formas de serem mais produtivas. A utilização dos dados que são produzidos dentro e fora das fábricas já foi identificada como uma forma de melhorar o desempenho do negócio. Um conjunto de tecnologias atuais combinado com a comunicação baseada em sensores cria a possibilidade de se atuar precisamente no momento em que os dados estão a ser produzidos e processados, assegurando resposta às necessidades do negócio. Considerando a diversidade de processos que existem e produzem dados numa fábrica, as capacidades do Processamento de Eventos Complexos (CEP) revelam-se necessárias no quotidiano de uma fábrica, processando diferentes tipos de eventos e encontrando padrões entre os mesmos. Apesar da integração do conceito CEP na era de Big Data ser um tópico já presente na literatura, existem ainda desafios nesta área que foram identificados e que dão origem às contribuições presentes nesta tese. Assim, esta tese de doutoramento propõe uma arquitetura para um sistema que integre o conceito de CEP na era do Big Data, seguindo uma abordagem baseada em regras: o Intelligent Event Broker (IEB). Esta arquitetura propõe a utilização de tecnologias de Big Data que sejam adequadas aos seus diversos componentes. As lacunas identificadas na literatura foram consideradas, complementando o processamento de eventos com a possibilidade de utilizar modelos de Machine Learning com vista a serem integrados na verificação das regras, propondo também um sistema de monitorização inovador composto por um componente de visualização imersiva que permite monitorizar o IEB e prevenir o seu crescimento descontrolado, o que pode acontecer devido à integração do conjunto significativo de processos existentes numa fábrica. A arquitetura proposta foi validada através de um caso de demonstração que usou os dados do Active Lot Release, um sistema da Bosch. Os resultados revelaram a viabilidade da implementação da arquitetura e comprovaram o adequado funcionamento do sistema no que diz respeito ao processamento dos dados dos processos de negócio da Bosch e à monitorização dos componentes do IEB e eventos que fluem através desses.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the Doctoral scholarship PD/BDE/135101/2017 and by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039479; Funding Reference: POCI-01- 0247-FEDER-039479]

Koostööäriprotsesside läbiviimine plokiahelal: süsteem

Tänapäeval peavad organisatsioonid tegema omavahel koostööd, et kasutada ära üksteise täiendavaid võimekusi ning seeläbi pakkuda oma klientidele parimaid tooteid ja teenuseid. Selleks peavad organisatsioonid juhtima äriprotsesse, mis ületavad nende organisatsioonilisi piire. Selliseid protsesse nimetatakse koostööäriprotsessideks. Üks peamisi takistusi koostööäriprotsesside elluviimisel on osapooltevahelise usalduse puudumine. Plokiahel loob detsentraliseeritud pearaamatu, mida ei saa võltsida ning mis toetab nutikate lepingute täitmist. Nii on võimalik teha koostööd ebausaldusväärsete osapoolte vahel ilma kesksele asutusele tuginemata. Paraku on aga äriprotsesside läbiviimine selliseid madala taseme plokiahela elemente kasutades tülikas, veaohtlik ja erioskusi nõudev. Seevastu juba väljakujunenud äriprotsesside juhtimissüsteemid (Business Process Management System – BPMS) pakuvad käepäraseid abstraheeringuid protsessidele orienteeritud rakenduste kiireks arendamiseks. Käesolev doktoritöö käsitleb koostööäriprotsesside automatiseeritud läbiviimist plokiahela tehnoloogiat kasutades, kombineerides traditsioonliste BPMS- ide arendusvõimalused plokiahelast tuleneva suurendatud usaldusega. Samuti käsitleb antud doktoritöö küsimust, kuidas pakkuda tuge olukordades, milles uued osapooled võivad jooksvalt protsessiga liituda, mistõttu on vajalik tagada paindlikkus äriprotsessi marsruutimisloogika muutmise osas. Doktoritöö uurib tarkvaraarhitektuurilisi lähenemisviise ja modelleerimise kontseptsioone, pakkudes välja disainipõhimõtteid ja nõudeid, mida rakendatakse uudsel plokiahela baasil loodud äriprotsessi juhtimissüsteemil CATERPILLAR. CATERPILLAR-i süsteem toetab kahte lähenemist plokiahelal põhinevate protsesside rakendamiseks, läbiviimiseks ja seireks: kompileeritud ja tõlgendatatud. Samuti toetab see kahte kontrollitud paindlikkuse mehhanismi, mille abil saavad protsessis osalejad ühiselt otsustada, kuidas protsessi selle täitmise ajal uuendada ning anda ja eemaldada osaliste juurdepääsuõigusi.Nowadays, organizations are pressed to collaborate in order to take advantage of their complementary capabilities and to provide best-of-breed products and services to their customers. To do so, organizations need to manage business processes that span beyond their organizational boundaries. Such processes are called collaborative business processes. One of the main roadblocks to implementing collaborative business processes is the lack of trust between the participants. Blockchain provides a decentralized ledger that cannot be tamper with, that supports the execution of programs called smart contracts. These features allow executing collaborative processes between untrusted parties and without relying on a central authority. However, implementing collaborative business processes in blockchain can be cumbersome, error-prone and requires specialized skills. In contrast, established Business Process Management Systems (BPMSs) provide convenient abstractions for rapid development of process-oriented applications. This thesis addresses the problem of automating the execution of collaborative business processes on top of blockchain technology in a way that takes advantage of the trust-enhancing capabilities of this technology while offering the development convenience of traditional BPMSs. The thesis also addresses the question of how to support scenarios in which new parties may be onboarded at runtime, and in which parties need to have the flexibility to change the default routing logic of the business process. We explore architectural approaches and modelling concepts, formulating design principles and requirements that are implemented in a novel blockchain-based BPMS named CATERPILLAR. The CATERPILLAR system supports two methods to implement, execute and monitor blockchain-based processes: compiled and interpreted. It also supports two mechanisms for controlled flexibility; i.e., participants can collectively decide on updating the process during its execution as well as granting and revoking access to parties.https://www.ester.ee/record=b536494

Anonymization Techniques for Privacy-preserving Process Mining

Process Mining ermöglicht die Analyse von Event Logs. Jede Aktivität ist durch ein Event in einem Trace recorded, welcher jeweils einer Prozessinstanz entspricht. Traces können sensible Daten, z.B. über Patienten enthalten. Diese Dissertation adressiert Datenschutzrisiken für Trace Daten und Process Mining. Durch eine empirische Studie zum Re-Identifikations Risiko in öffentlichen Event Logs wird die hohe Gefahr aufgezeigt, aber auch weitere Risiken sind von Bedeutung. Anonymisierung ist entscheidend um Risiken zu adressieren, aber schwierig weil gleichzeitig die Verhaltensaspekte des Event Logs erhalten werden sollen. Dies führt zu einem Privacy-Utility-Trade-Off. Dieser wird durch neue Algorithmen wie SaCoFa und SaPa angegangen, die Differential Privacy garantieren und gleichzeitig Utility erhalten. PRIPEL ergänzt die anonymiserten Control-flows um Kontextinformationen und ermöglich so die Veröffentlichung von vollständigen, geschützten Logs. Mit PRETSA wird eine Algorithmenfamilie vorgestellt, die k-anonymity garantiert. Dafür werden privacy-verletztende Traces miteinander vereint, mit dem Ziel ein möglichst syntaktisch ähnliches Log zu erzeugen. Durch Experimente kann eine bessere Utility-Erhaltung gegenüber existierenden Lösungen aufgezeigt werden.Process mining analyzes business processes using event logs. Each activity execution is recorded as an event in a trace, representing a process instance's behavior. Traces often hold sensitive info like patient data. This thesis addresses privacy concerns arising from trace data and process mining. A re-identification risk study on public event logs reveals high risk, but other threats exist. Anonymization is vital to address these issues, yet challenging due to preserving behavioral aspects for analysis, leading to a privacy-utility trade-off. New algorithms, SaCoFa and SaPa, are introduced for trace anonymization using noise for differential privacy while maintaining utility. PRIPEL supplements anonymized control flows with trace contextual info for complete protected logs. For k-anonymity, the PRETSA algorithm family merges privacy-violating traces based on a prefix representation of the event log, maintaining syntactic similarity. Empirical evaluations demonstrate utility improvements over existing techniques

Proceedings of the Doctoral Consortium Papers Presented at the 31st International Conference on Advanced Information Systems Engineering (CAiSE 2019), Rome, Italy, June 3-7, 2019

In the tradition of the CAiSE conference series, the CAiSE’19 edition which has been located in Rome from June 3rd to June 7th hosted the 26th edition of the Doctoral Consortium which is intended to bring to-gether PhD students working on foundations, techniques, tools and ap-plications in the Information Systems Engineering field. The Doctoral Consortium provides them with an opportunity to present their research to and discuss it with an audience of peers and senior faculty in a sup-portive environment, as well as to participate in a number of plenary sessions with Information Systems academics. This year the Doctoral Consortium has received 12 submissions and 8 of them have been selected to be presented during the event and to be included in these proceedings. These papers have been selected after a careful evaluation process involving 3 senior academics each, which goal is to give constructive feedback and advice on the research pro-jects presented in the papers. For the selected work, the Ph.D. student has the opportunity meet experts with different backgrounds working on topics related to the Information Systems Engineering field, interact with other PhD students and stimulate an exchange of ideas and sug-gestions among participants, and discuss concerns about research, su-pervision, job market, and other career-related issues. We would like to thank the Doctoral Consortium mentors for their sup-port in reviewing the papers and stimulate the discussion during the four sessions at CAiSE. A great thanks to the students that have applied to the Doctoral Consortium for sharing their ideas, we wish them a long and fruitful career in academia or industry. We also thank the General Chairs Massimo Mecella and Barbara Pernici for their kind support to this initiative

Enterprise reference architectures for higher education institutions: Analysis, comparison and practical uses

Enterprise Architecture (EA) is currently accepted as one on the major instruments for enabling organisations in their transformation processes to achieve business-technology alignment. Despite that over the last years EA has been successfully adopted in many industries, Higher Education still represents one of the sectors with lower levels of adoption and maturity of EA practices. The present thesis puts the emphasis particularly on the study Enterprise Reference Architectures (ERAs), as a particular type of EA artefact, in Higher Education Institutions (HEIs). After formally clarifying the concept of ERAs and giving a panoramic view of the current state-of-the-art of existing HEI-oriented ERAs, the thesis proposes an artefact framework build through a Design Science Research (DSR) approach aimed to facilitate practitioners their (re-)use or application in their own real practical settings. The purpose of the constructed artefact is to support practitioners when conducting the necessary adjustments to exiting HEI-oriented ERAs in order to be successfully applied for their specific needs.La Arquitectura Empresarial (AE) es actualmente reconocida como una disciplina que permite configurar procesos de trasformación organizativa a objeto de alinear el negocio con la tecnología. A pesar de que en los últimos años la AE se ha ido adoptando progresivamente de forma exitosa en diversas industrias, la educación superior representa todavía hoy en día uno de los sectores con menores niveles de adopción y de madurez en lo que se refiere a las prácticas de AE. La presente tesis hace especial hincapié en el estudio de las Arquitecturas de Referencia Empresariales (AREs), entendidas como un artefacto específico de AE, en Instituciones de Educación Superior (IES). Así, después de clarificar formalmente el concepto de ARE y de ofrecer una visión panorámica del estado del arte relativo a las AREs para IES existentes, la tesis propone un framework de trabajo construido a través de un enfoque de investigación basado en la Ciencia del diseño destinado a facilitar su (re-)utilización o aplicación práctica en dominios de trabajo reales. El objetivo del artefacto es proporcionar soporte práctico a los profesionales para realizar los ajustes necesarios a las AREs para IES existentes para que puedan aplicarlas con éxito a sus necesidades específicas.L'Arquitectura Empresarial (AE) és actualment reconeguda com una disciplina que permet configurar processos de transformació organitzatius a fi d'alinear el negoci amb la tecnologia. Tot i que en els darrers anys l'AE s'ha anat adoptant progressivament amb èxit en diverses indústries, l'educació superior representa encara avui dia un dels sectors amb menors nivells d'adopció i de maduresa pel que fa a pràctiques d'AE. Aquesta tesi posa especial èmfasi en l'estudi de les Arquitectures de Referència Empresarials (AREs), enteses com un artefacte concret d'AE, a Institucions d'Educació Superior (IES). Així, després d'aclarir formalment el concepte d'ARE i oferir una visió panoràmica de l'estat de l'art relatiu a les ARE per a IES existents, la tesi proposa un framework de treball construït a través d'un enfocament de recerca basat en la ciència del disseny destinat a facilitar-ne la seva (re-)utilització o aplicació pràctica en dominis de treball reals. L'objectiu de l'artefacte és proporcionar suport pràctic als professionals per realitzar els ajustaments necessaris a les AREs per a IES existents de forma que les puguin aplicar amb èxit a les seves necessitats específiques.Tecnologies de la informació i de xarxe

Privaatsuskaitse tehnoloogiaid äriprotsesside kaeveks

Protsessikaeve tehnikad võimaldavad organisatsioonidel analüüsida protsesside täitmise käigus tekkivaid logijälgi eesmärgiga leida parendusvõimalusi. Nende tehnikate eelduseks on, et nimetatud logijälgi koondavad sündmuslogid on andmeanalüütikutele analüüside läbi viimiseks kättesaadavad. Sellised sündmuslogid võivad sisaldada privaatset informatsiooni isikute kohta kelle jaoks protsessi täidetakse. Sellistel juhtudel peavad organisatsioonid rakendama privaatsuskaitse tehnoloogiaid (PET), et võimaldada analüütikul sündmuslogi põhjal järeldusi teha, samas säilitades isikute privaatsust. Kuigi PET tehnikad säilitavad isikute privaatsust organisatsiooni siseselt, muudavad nad ühtlasi sündmuslogisid sellisel viisil, mis võib viia analüüsi käigus valede järeldusteni. PET tehnikad võivad lisada sündmuslogidesse sellist uut käitumist, mille esinemine ei ole reaalses sündmuslogis võimalik. Näiteks võivad mõned PET tehnikad haigla sündmuslogi anonüümimisel lisada logijälje, mille kohaselt patsient külastas arsti enne haiglasse saabumist. Käesolev lõputöö esitab privaatsust säilitavate lähenemiste komplekti nimetusega privaatsust säilitav protsessikaeve (PPPM). PPPM põhiline eesmärk on leida tasakaal võimaliku sündmuslogi analüüsist saadava kasu ja analüüsile kohaldatavate privaatsusega seonduvate regulatsioonide (näiteks GDPR) vahel. Lisaks pakub käesolev lõputöö lahenduse, mis võimaldab erinevatel organisatsioonidel protsessikaevet üle ühise andmete terviku rakendada, ilma oma privaatseid andmeid üksteisega jagamata. Käesolevas lõputöös esitatud tehnikad on avatud lähtekoodiga tööriistadena kättesaadavad. Nendest tööriistadest esimene on Amun, mis võimaldab sündmuslogi omanikul sündmuslogi anonüümida enne selle analüütikule jagamist. Teine tööriist on Libra, mis pakub täiendatud võimalusi kasutatavuse ja privaatsuse tasakaalu leidmiseks. Kolmas tööriist on Shareprom, mis võimaldab organisatsioonidele ühiste protsessikaartide loomist sellisel viisil, et ükski osapool ei näe teiste osapoolte andmeid.Process Mining Techniques enable organizations to analyze process execution traces to identify improvement opportunities. Such techniques need the event logs (which record process execution) to be available for data analysts to perform the analysis. These logs contain private information about the individuals for whom a process is being executed. In such cases, organizations need to deploy Privacy-Enhancing Technologies (PETs) to enable the analyst to drive conclusions from the event logs while preserving the privacy of individuals. While PETs techniques preserve the privacy of individuals inside the organization, they work by perturbing the event logs in such a way that may lead to misleading conclusions of the analysis. They may inject new behaviors into the event logs that are impossible to exist in real-life event logs. For example, some PETs techniques anonymize a hospital event log by injecting a trace that a patient may visit a doctor before checking in inside the hospital. In this thesis, we propose a set of privacy-preserving approaches that we call Privacy-Preserving Process Mining (PPPM) approaches to strike a balance between the benefits an analyst can get from analyzing these event logs and the requirements imposed on them by privacy regulations (e.g., GDPR). Also, in this thesis, we propose an approach that enables organizations to jointly perform process mining over their data without sharing their private information. The techniques proposed in this thesis have been proposed as open-source tools. The first tool is Amun, enabling an event log publisher to anonymize their event log before sharing it with an analyst. The second tool is called Libra, which provides an enhanced utility-privacy tradeoff. The third tool is Shareprom, which enables organizations to construct process maps jointly in such a manner that no party learns the data of the other parties.https://www.ester.ee/record=b552434

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

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction