What we know and what we do not know about DMN

The recent Decision Model and Notation (DMN) establishes business decisions as first-class citizens of executable business processes. This research note has two objectives: first, to describe DMN's technical and theoretical foundations; second, to identify research directions for investigating DMN's potential benefits on a technological, individual and organizational level. To this end, we integrate perspectives from management science, cognitive theory and information systems research

Bridging the gap between textual and formal business process representations

Tesi en modalitat de compendi de publicacionsIn the era of digital transformation, an increasing number of organizations are start ing to think in terms of business processes. Processes are at the very heart of each business, and must be understood and carried out by a wide range of actors, from both technical and non-technical backgrounds alike. When embracing digital transformation practices, there is a need for all involved parties to be aware of the underlying business processes in an organization. However, the representational complexity and biases of the state-of-the-art modeling notations pose a challenge in understandability. On the other hand, plain language representations, accessible by nature and easily understood by everyone, are often frowned upon by technical specialists due to their ambiguity. The aim of this thesis is precisely to bridge this gap: Between the world of the techni cal, formal languages and the world of simpler, accessible natural languages. Structured as an article compendium, in this thesis we present four main contributions to address specific problems in the intersection between the fields of natural language processing and business process management.A l’era de la transformació digital, cada vegada més organitzacions comencen a pensar en termes de processos de negoci. Els processos són el nucli principal de tota empresa i, com a tals, han de ser fàcilment comprensibles per un ampli ventall de rols, tant perfils tècnics com no-tècnics. Quan s’adopta la transformació digital, és necessari que totes les parts involucrades estiguin ben informades sobre els protocols implantats com a part del procés de digitalització. Tot i això, la complexitat i biaixos de representació dels llenguatges de modelització que actualment conformen l’estat de l’art sovint en dificulten la seva com prensió. D’altra banda, les representacions basades en documentació usant llenguatge natural, accessibles per naturalesa i fàcilment comprensibles per tothom, moltes vegades són vistes com un problema pels perfils més tècnics a causa de la presència d’ambigüitats en els textos. L’objectiu d’aquesta tesi és precisament el de superar aquesta distància: La distància entre el món dels llenguatges tècnics i formals amb el dels llenguatges naturals, més accessibles i senzills. Amb una estructura de compendi d’articles, en aquesta tesi presentem quatre grans línies de recerca per adreçar problemes específics en aquesta intersecció entre les tecnologies d’anàlisi de llenguatge natural i la gestió dels processos de negoci.Postprint (published version

Conformance Checking of Mixed-paradigm Process Models

Mixed-paradigm process models integrate strengths of procedural and declarative representations like Petri nets and Declare. They are specifically interesting for process mining because they allow capturing complex behaviour in a compact way. A key research challenge for the proliferation of mixed-paradigm models for process mining is the lack of corresponding conformance checking techniques. In this paper, we address this problem by devising the first approach that works with intertwined state spaces of mixed-paradigm models. More specifically, our approach uses an alignment-based replay to explore the state space and compute trace fitness in a procedural way. In every state, the declarative constraints are separately updated, such that violations disable the corresponding activities. Our technique provides for an efficient replay towards an optimal alignment by respecting all orthogonal Declare constraints. We have implemented our technique in ProM and demonstrate its performance in an evaluation with real-world event logs.Comment: Accepted for publication in Information System

Linguistic Refactoring of Business Process Models

In the past decades, organizations had to face numerous challenges due to intensifying globalization and internationalization, shorter innovation cycles and growing IT support for business. Business process management is seen as a comprehensive approach to align business strategy, organization, controlling, and business activities to react flexibly to market changes. For this purpose, business process models are increasingly utilized to document and redesign relevant parts of the organization's business operations. Since companies tend to have a growing number of business process models stored in a process model repository, analysis techniques are required that assess the quality of these process models in an automatic fashion. While available techniques can easily check the formal content of a process model, there are only a few techniques available that analyze the natural language content of a process model. Therefore, techniques are required that address linguistic issues caused by the actual use of natural language. In order to close this gap, this doctoral thesis explicitly targets inconsistencies caused by natural language and investigates the potential of automatically detecting and resolving them under a linguistic perspective. In particular, this doctoral thesis provides the following contributions. First, it defines a classification framework that structures existing work on process model analysis and refactoring. Second, it introduces the notion of atomicity, which implements a strict consistency condition between the formal content and the textual content of a process model. Based on an explorative investigation, we reveal several reoccurring violation patterns are not compliant with the notion of atomicity. Third, this thesis proposes an automatic refactoring technique that formalizes the identified patterns to transform a non-atomic process models into an atomic one. Fourth, this thesis defines an automatic technique for detecting and refactoring synonyms and homonyms in process models, which is eventually useful to unify the terminology used in an organization. Fifth and finally, this thesis proposes a recommendation-based refactoring approach that addresses process models suffering from incompleteness and leading to several possible interpretations. The efficiency and usefulness of the proposed techniques is further evaluated by real-world process model repositories from various industries. (author's abstract

Äriprotsesside ajaliste näitajate selgitatav ennustav jälgimine

Kaasaegsed ettevõtte infosüsteemid võimaldavad ettevõtetel koguda detailset informatsiooni äriprotsesside täitmiste kohta. Eelnev koos masinõppe meetoditega võimaldab kasutada andmejuhitavaid ja ennustatavaid lähenemisi äriprotsesside jõudluse jälgimiseks. Kasutades ennustuslike äriprotsesside jälgimise tehnikaid on võimalik jõudluse probleeme ennustada ning soovimatu tegurite mõju ennetavalt leevendada. Tüüpilised küsimused, millega tegeleb ennustuslik protsesside jälgimine on “millal antud äriprotsess lõppeb?” või “mis on kõige tõenäolisem järgmine sündmus antud äriprotsessi jaoks?”. Suurim osa olemasolevatest lahendustest eelistavad täpsust selgitatavusele. Praktikas, selgitatavus on ennustatavate tehnikate tähtis tunnus. Ennustused, kas protsessi täitmine ebaõnnestub või selle täitmisel võivad tekkida raskused, pole piisavad. On oluline kasutajatele seletada, kuidas on selline ennustuse tulemus saavutatud ning mida saab teha soovimatu tulemuse ennetamiseks. Töö pakub välja kaks meetodit ennustatavate mudelite konstrueerimiseks, mis võimaldavad jälgida äriprotsesse ning keskenduvad selgitatavusel. Seda saavutatakse ennustuse lahtivõtmisega elementaarosadeks. Näiteks, kui ennustatakse, et äriprotsessi lõpuni on jäänud aega 20 tundi, siis saame anda seletust, et see aeg on moodustatud kõikide seni käsitlemata tegevuste lõpetamiseks vajalikust ajast. Töös võrreldakse omavahel eelmainitud meetodeid, käsitledes äriprotsesse erinevatest valdkondadest. Hindamine toob esile erinevusi selgitatava ja täpsusele põhinevale lähenemiste vahel. Töö teaduslik panus on ennustuslikuks protsesside jälgimiseks vabavaralise tööriista arendamine. Süsteemi nimeks on Nirdizati ning see süsteem võimaldab treenida ennustuslike masinõppe mudeleid, kasutades nii töös kirjeldatud meetodeid kui ka kolmanda osapoole meetodeid. Hiljem saab treenitud mudeleid kasutada hetkel käivate äriprotsesside tulemuste ennustamiseks, mis saab aidata kasutajaid reaalajas.Modern enterprise systems collect detailed data about the execution of the business processes they support. The widespread availability of such data in companies, coupled with advances in machine learning, have led to the emergence of data-driven and predictive approaches to monitor the performance of business processes. By using such predictive process monitoring approaches, potential performance issues can be anticipated and proactively mitigated. Various approaches have been proposed to address typical predictive process monitoring questions, such as what is the most likely continuation of an ongoing process instance, or when it will finish. However, most existing approaches prioritize accuracy over explainability. Yet in practice, explainability is a critical property of predictive methods. It is not enough to accurately predict that a running process instance will end up in an undesired outcome. It is also important for users to understand why this prediction is made and what can be done to prevent this undesired outcome. This thesis proposes two methods to build predictive models to monitor business processes in an explainable manner. This is achieved by decomposing a prediction into its elementary components. For example, to explain that the remaining execution time of a process execution is predicted to be 20 hours, we decompose this prediction into the predicted execution time of each activity that has not yet been executed. We evaluate the proposed methods against each other and various state-of-the-art baselines using a range of business processes from multiple domains. The evaluation reaffirms a fundamental trade-off between explainability and accuracy of predictions. The research contributions of the thesis have been consolidated into an open-source tool for predictive business process monitoring, namely Nirdizati. It can be used to train predictive models using the methods described in this thesis, as well as third-party methods. These models are then used to make predictions for ongoing process instances; thus, the tool can also support users at runtime

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

Analysing the provenance tracking of business process management in the quality domain

This work presents a framework of how Provenance can be combined with BPM. This is achieved through a use case, from the Martine Spécialités enterprise which is using the Agilium BPM system (without the provenance functionality). Using the PROV Data Model, a graphical representation of the provenance tracking of the processes of the enterprise has been done and the functional requirements for combining provenance and BPM have been defined. A survey was submitted to the employees in order to see if adding the provenance functionality is something beneficial for them. The results have shown that provenance is adding value for the decision making during the execution of the processes, and that several benefits can be obtained from this, such as better decision making and time saving