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

Enabling Process Mining in Aircraft Manufactures: Extracting Event Logs and Discovering Processes from Complex Data

Process mining is employed by organizations to completely understand and improve their processes and to detect possible deviations from expected behavior. Process discovery uses event logs as input data, which describe the times of the actions that occur the traces. Currently, Internet-of-Things environments generate massive distributed and not always structured data, which brings about new complex scenarios since data must first be transformed in order to be handled by process min ing tools. This paper shows the success case of application of a solution that permits the transformation of complex semi-structured data of an assembly-aircraft process in order to create event logs that can be man aged by the process mining paradigm. A Domain-Specific Language and a prototype have been implemented to facilitate the extraction of data into the unified traces of an event log. The implementation performed has been applied within a project in the aeronautic industry, and promis ing results have been obtained of the log extraction for the discovery of processes and the resulting improvement of the assembly-aircraft process.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

The Business Process Design Space for exploring process redesign alternatives

Purpose – Process redesign refers to the intentional change of business processes. While process redesign methods provide structure to redesign projects, they provide limited support during the actual creation of to-be processes. More specifically, existing approaches hardly develop an ontological perspective on what can be changed from a process design point of view and they provide limited procedural guidance on how to derive possible process design alternatives. This paper aims to provide structured guidance during the to-be process creation. Design/methodology/approach – Using design space exploration as a theoretical lens, we develop a conceptual model of the design space for business processes, which facilitates the systematic exploration of design alternatives along different dimensions. We utilized an established method for taxonomy development for constructing our conceptual model. First, we derived design dimensions for business processes and underlying characteristics through a literature review. Second, we conducted semi-structured interviews with professional process experts. Third, we evaluated our artifact through three real-world applications. Findings – We identified 19 business process design dimensions that are grouped into different layers and specified by underlying characteristics. Guiding questions and illustrative real-world examples help to deploy these design dimensions in practice. Taken together, the design dimensions form the “Business Process Design Space” (BPD-Space). Research limitations/implications – Practitioners can use the BPD-Space to explore, question, and rethink business processes in various respects. Originality/value – The BPD-Space complements existing approaches by explicating process design dimensions. It abstracts from specific process flows and representations of processes and supports an unconstrained exploration of various alternative process designs

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

Design principles for ensuring compliance in business processes

In this thesis, we evaluate the complexity and understandability of compliance languages. First, to calculate the complexity, we apply established software metrics and interpret the results with respect to the languages’ expressiveness. Second, to investigate the languages’ understandability, we use a cognitive model of the human problem-solving process and analyze how efficiently users perform a compliance modeling task. Our results have theoretical and practical implications that give directions for the development of compliance languages, and rule-based languages in general.Diese Arbeit beurteilt die Komplexität und Verständlichkeit von Compliance-Sprachen. Zur Messung der Komplexität wenden wir etablierte Software-Metriken an und interpretieren die Ergebnisse in Hinblick auf die Aussagekraft der Sprachen. Zur Untersuchung der Verständlichkeit verwenden wir ein kognitives Modell und analysieren, wie effizient eine Compliance-Sprache zur Lösung eines Modellierungsproblems eingesetzt wird. Unsere Ergebnisse haben theoretische und praktische Implikationen für die Entwicklung von Compliance-Sprachen und anderen regelbasierten Sprachen

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

Executable Models and Instance Tracking for Decentralized Applications on Blockchains and Cloud Platforms -- Metamodel and Implementation

Decentralized applications rely on non-centralized technical infrastructures and coordination principles. Without trusted third parties, their execution is not controlled by entities exercising centralized coordination but is instead realized through technologies supporting distribution such as blockchains and serverless computing. Executing decentralized applications with these technologies, however, is challenging due to the limited transparency and insight in the execution, especially when involving centralized cloud platforms. This paper extends an approach for execution and instance tracking on blockchains and cloud platforms permitting distributed parties to observe the instances and states of executable models. The approach is extended with (1.) a metamodel describing the concepts for instance tracking on cloud platforms independent of concrete models or implementation, (2.) a multidimensional data model realizing the concepts accordingly, permitting the verifiable storage, tracking, and analysis of execution states for distributed parties, and (3.) an implementation on the Ethereum blockchain and Amazon Web Services (AWS) using state machine models. Towards supporting decentralized applications with high scalability and distribution requirements, the approach establishes a consistent view on instances for distributed parties to track and analyze the execution along multiple dimensions such as specific clients and execution engines.Comment: This is an unpublished preprint; both versions archived on arXiv.org have not been published. Although initially intended for publication, the preprint has undergone further improvements and has been utilized as input for new publications. (see also: https://www.unifr.ch/inf/digits/en/group/team/haerer.html