Merging business process models

This paper addresses the following problem: given two business process models, create a process model that is the union of the process models given as input. In other words, the behavior of the produced process model should encompass that of the input models. The paper describes an algorithm that produces a single configurable process model from a pair of process models. The algorithm works by extracting the common parts of the input process models, creating a single copy of them, and appending the differences as branches of configurable connectors. This way, the merged process model is kept as small as possible, while still capturing all the behavior of the input models. Moreover, analysts are able to trace back which model(s) a given element in the merged model originates from. The algorithm has been prototyped and tested against process models taken from several application domains

Äriprotsessimudelite ühildamine

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Ettevõtted, kellel on aastatepikkune kogemus äriprotsesside haldamises, omavad sageli protsesside repositooriumeid, mis võivad endas sisaldada sadu või isegi tuhandeid äriprotsessimudeleid. Need mudelid pärinevad erinevatest allikatest ja need on loonud ning neid on muutnud erinevad osapooled, kellel on erinevad modelleerimise oskused ning praktikad. üheks sagedaseks praktikaks on uute mudelite loomine, kasutades olemasolevaid mudeleid, kopeerides neist fragmente ning neid seejärel muutes. See omakorda loob olukorra, kus protsessimudelite repositoorium sisaldab mudeleid, milles on identseid mudeli fragmente, mis viitavad samale alamprotsessile. Kui sellised fragmendid jätta konsolideerimata, siis võib see põhjustada repositooriumis ebakõlasid -- üks ja sama alamprotsess võib olla erinevates protsessides erinevalt kirjeldatud. Sageli on ettevõtetel mudelid, millel on sarnased eesmärgid, kuid mis on mõeldud erinevate klientide, toodete, äriüksuste või geograafiliste regioonide jaoks. Näiteks on äriprotsessid kodukindlustuse ja autokindlustuse jaoks sama ärilise eesmärgiga. Loomulikult sisaldavad nende protsesside mudelid mitmeid identseid alamfragmente (nagu näiteks poliisi andmete kontrollimine), samas on need protsessid mitmes punktis erinevad. Nende protsesside eraldi haldamine on ebaefektiivne ning tekitab liiasusi. Doktoritöös otsisime vastust küsimusele: kuidas identifitseerida protsessimudelite repositooriumis korduvaid mudelite fragmente, ning üldisemalt -- kuidas leida ning konsolideerida sarnasusi suurtes äriprotsessimudelite repositooriumites? Doktoritöös on sisse toodud kaks üksteist täiendavat meetodit äriprotsessimudelite konsolideerimiseks, täpsemalt protsessimudelite ühildamine üheks mudeliks ning mudelifragmentide ekstraktimine. Esimene neist võtab sisendiks kaks või enam protsessimudelit ning konstrueerib neist ühe konsolideeritud protsessimudeli, mis sisaldab kõikide sisendmudelite käitumist. Selline lähenemine võimaldab analüütikutel hallata korraga tervet perekonda sarnaseid mudeleid ning neid muuta sünkroniseeritud viisil. Teine lähenemine, alamprotsesside ekstraktimine, sisaldab endas sagedasti esinevate fragmentide identifitseerimist (protsessimudelites kloonide leidmist) ning nende kapseldamist alamprotsessideks

Process mining meets GDPR compliance:the right to be forgotten as a use case

In a bid to ensure privacy of personal data of data subjects, the General Data Protection Regulation(GDPR) entails stringent obligations on organizations and businesses qualifying as data controllers and data processors. The regulation additionally bestow data subjects certain rights over their personal data, right to be forgotten generally being perceived the landmark. Fulfilling the GDPR’s obligatory requirements and provisioning of the data subject’s rights implicates considerable changes to the existing (pre-GDPR era) business and organizational processes. Being a non-trivial task, several technical as well as procedural challenges are associated. The case for organizations having intertwined or cascaded business processes and business processes stretched over multiple organizations is even more complicated. Process mining discipline has been found highly effective in automatically discovering, conformance/compliance analysis, and enhancement of business processes, organizational workflows, healthcare procedures/guidelines to name a few. Process mining techniques therefore have a great potential to assist and guide the transformation of pre-GDPR era (presumably GDPR non-compliant) business or organizational processes into GDPR-compliant processes, and afterwards monitor the compliance during execution. In addition to the current state of the art offline process mining techniques, stable online conformance checking and online model repair techniques needs to be developed for ensuring compliance to the regulation. We are highlighting the challenges associated with implementation of the right to be forgotten, and the GDPR in general.</p

Automatic Generation of Questionnaires for Managing Configurable BP Models

Managing large collections of business process (BP) models is increasingly being necessary for organizations. For this, configurable BP models can be used for managing these BPs while allowing analysts to understand what these BPs share and what their differences are. Before the execution of the configurable BP model, a BP model has to be selected from it. This selection is typically performed by an analyst who manually individualizes the model in order to address the business requirements. Unlike existing approaches, we propose a totally automated method to create a questionnaire-based application for guiding a business expert on individualizing a model

Challenges and opportunities of applying natural language processing in business process management

The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.Peer ReviewedPostprint (published version

SUPPORTING TERMINOLOGICAL STANDARDIZATION IN CONCEPTUAL MODELS - A PLUGIN FOR A META-MODELLING TOOL

Today´s enterprises are accumulating huge repositories of conceptual models, such as data models, organisational charts and most notably business process models. Those models often grow heterogeneously with the company and are thus often terminologically divers and complex. This terminological diversity originates from the fact that natural language allows an issu to be described in a large variety of ways especially when many modellers are involved. This diversity can become a pitfall when conceptual models are subject to model analysis techniqus, which require terminologically comparable model elements. Therefore, it is essential to ensure model quality by enforcing naming conventions. This paper introduces a prototype, which intends to resolve all associated issus of terminological standardisation already during the modelling phase or ex-post based on existing models. The modeller is guided through the standardization process by providing an automated list of all correct phrase propositions according to his entered phrase. In this approach, naming conventions can easily be defined and enforced. This leads to terminologically unambiguous conceptual models, which are easier to understand and ready for further analysis purposes