The alignment of formal, structured and unstructured process descriptions

Nowadays organizations are experimenting a drift on the way processes are managed. On the one hand, formal notations like Petri nets or Business Process Model and Notation (BPMN) enable the unambiguous reasoning and automation of designed processes. This way of eliciting processes by manual design, which stemmed decades ago, will still be an important actor in the future. On the other hand, regulations require organizations to store their process executions in structured representations, so that they are known and can be analyzed. Finally, due to the different nature of stakeholders within an organization (ranging from the most technical members, e.g., developers, to less technical), textual descriptions of processes are also maintained to enable that everyone in the organization understands their processes. In this paper I will describe techniques for facilitating the interconnection between these three process representations. This requires interdisciplinary research to connect several fields: business process management, formal methods, natural language processing and process mining.Peer ReviewedPostprint (author's final draft

Encoding conformance checking artefacts in SAT

Conformance checking strongly relies on the computation of artefacts, which enable reasoning on the relation between observed and modeled behavior. This paper shows how important conformance artefacts like alignments, anti-alignments or even multi-alignments, defined over the edit distance, can be computed by encoding the problem as a SAT instance. From a general perspective, the work advocates for a unified family of techniques that can compute conformance artefacts in the same way. The prototype implementation of the techniques presented in this paper show capabilities for dealing with some of the current benchmarks, and potential for the near future when optimizations similar to the ones in the literature are incorporated.Peer ReviewedPostprint (author's final draft

Structural computation of alignments of business processes over partial orders

Relating event data and process models is becoming an important element for organizations. This paper presents a novel approach for aligning traces and process models. The approach is based on the structural theory of Petri nets (the marking equation), applied over an unfolding of the initial process model. Given an observed trace, the approach adopts an iterative optimization mechanism on top of the unfolding, computing at each iteration part of the resulting alignment. In contrast to the previous work that is primarily grounded in the marking equation, this approach is guaranteed to provide real solutions, and tries to mimic as much as possible the events observed in the trace. Experiments witness the significance of this approach both in quality and execution time perspectives.Peer ReviewedPostprint (author's final draft

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Process discovery methods automatically infer process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a process discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper

Software developers reasoning behind adoption and use of software development methods – a systematic literature review

When adopting and using a Software Development Method (SDM), it is important to stay true to the philosophy of the method; otherwise, software developers might execute activities that do not lead to the intended outcomes. Currently, no overview of SDM research addresses software developers’ reasoning behind adopting and using SDMs. Accordingly, this paper aims to survey existing SDM research to scrutinize the current knowledge base on software developers’ type of reasoning behind SDM adoption and use. We executed a systematic literature review and analyzed existing research using two steps. First, we classified papers based on what type of reasoning was addressed regarding SDM adoption and use: rational, irrational, and non-rational. Second, we made a thematic synthesis across these three types of reasoning to provide a more detailed characterization of the existing research. We elicited 28 studies addressing software developers’ reasoning and identified five research themes. Building on these themes, we framed four future research directions with four broad research questions, which can be used as a basis for future research

Äriprotsessi tulemuste ennustav ja korralduslik seire

Viimastel aastatel on erinevates valdkondades tegutsevad ettevõtted üles näidanud kasvavat huvi masinõppel põhinevate rakenduste kasutusele võtmiseks. Muuhulgas otsitakse võimalusi oma äriprotsesside efektiivsuse tõstmiseks, kasutades ennustusmudeleid protsesside jooksvaks seireks. Sellised ennustava protsessiseire meetodid võtavad sisendiks sündmuslogi, mis koosneb hulgast lõpetatud äriprotsessi juhtumite sündmusjadadest, ning kasutavad masinõppe algoritme ennustusmudelite treenimiseks. Saadud mudelid teevad ennustusi lõpetamata (antud ajahetkel aktiivsete) protsessijuhtumite jaoks, võttes sisendiks sündmuste jada, mis selle hetkeni on toimunud ning ennustades kas järgmist sündmust antud juhtumis, juhtumi lõppemiseni jäänud aega või instantsi lõpptulemust. Lõpptulemusele orienteeritud ennustava protsessiseire meetodid keskenduvad ennustamisele, kas protsessijuhtum lõppeb soovitud või ebasoovitava lõpptulemusega. Süsteemi kasutaja saab ennustuste alusel otsustada, kas sekkuda antud protsessijuhtumisse või mitte, eesmärgiga ära hoida ebasoovitavat lõpptulemust või leevendada selle negatiivseid tagajärgi. Erinevalt puhtalt ennustavatest süsteemidest annavad korralduslikud protsessiseire meetodid kasutajale ka soovitusi, kas ja kuidas antud juhtumisse sekkuda, eesmärgiga optimeerida mingit kindlat kasulikkusfunktsiooni. Käesolev doktoritöö uurib, kuidas treenida, hinnata ja kasutada ennustusmudeleid äriprotsesside lõpptulemuste ennustava ja korraldusliku seire raames. Doktoritöö pakub välja taksonoomia olemasolevate meetodite klassifitseerimiseks ja võrdleb neid katseliselt. Lisaks pakub töö välja raamistiku tekstiliste andmete kasutamiseks antud ennustusmudelites. Samuti pakume välja ennustuste ajalise stabiilsuse mõiste ning koostame raamistiku korralduslikuks protsessiseireks, mis annab kasutajatele soovitusi, kas protsessi sekkuda või mitte. Katsed näitavad, et väljapakutud lahendused täiendavad olemasolevaid meetodeid ning aitavad kaasa ennustava protsessiseire süsteemide rakendamisele reaalsetes süsteemides.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. Such predictive process monitoring techniques take an event log (a set of completed business process execution traces) as input and use machine learning techniques to train predictive models. At runtime, these techniques predict either the next event, the remaining time, or the final outcome of an ongoing case, given its incomplete execution trace consisting of the events performed up to the present moment in the given case. In particular, a family of techniques called outcome-oriented predictive process monitoring focuses on predicting whether a case will end with a desired or an undesired outcome. The user of the system can use the predictions to decide whether or not to intervene, with the purpose of preventing an undesired outcome or mitigating its negative effects. Prescriptive process monitoring systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running case in order to optimize a given utility function. This thesis addresses the question of how to train, evaluate, and use predictive models for predictive and prescriptive monitoring of business process outcomes. The thesis proposes a taxonomy and performs a comparative experimental evaluation of existing techniques in the field. Moreover, we propose a framework for incorporating textual data to predictive monitoring systems. We introduce the notion of temporal stability to evaluate these systems and propose a prescriptive process monitoring framework for advising users if and how to act upon the predictions. The results suggest that the proposed solutions complement the existing techniques and can be useful for practitioners in implementing predictive process monitoring systems in real life