Decomposing conformance checking on Petri nets with data

Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a Petri net based on an event log. Process mining is not limited to process discovery and also includes conformance checking. Conformance checking techniques are used for evaluating the quality of discovered process models and to diagnose deviations from some normative model (e.g., to check compliance). Existing conformance checking approaches typically focus on the control flow, thus being unable to diagnose deviations concerning data. This paper proposes a technique to check the conformance of data-aware process models. We use so-called "data Petri nets" to model data variables, guards, and read/write actions. Additional perspectives such as resource allocation and time constraints can be encoded in terms of variables. Data-aware conformance checking problem may be very time consuming and sometimes even intractable when there are many transitions and data variables. Therefore, we propose a technique to decompose large data-aware conformance checking problems into smaller problems that can be solved more efficiently. We provide a general correctness result showing that decomposition does not influence the outcome of conformance checking. Moreover, two decomposition strategies are presented. The approach is supported through ProM plug-ins and experimental results show that significant performance improvements are indeed possible

Conformance Checking Based on Multi-Perspective Declarative Process Models

Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behaviors provided in the form of a process model. The majority of these approaches require the input process model to be procedural (e.g., a Petri net). However, in turbulent environments, characterized by high variability, the process behavior is less stable and predictable. In these environments, procedural process models are less suitable to describe a business process. Declarative specifications, working in an open world assumption, allow the modeler to express several possible execution paths as a compact set of constraints. Any process execution that does not contradict these constraints is allowed. One of the open challenges in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. In this paper, we close this gap by providing a framework for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented in three real life case studies

Decomposed process discovery and conformance checking

Decomposed process discovery and decomposed conformance checking are the corresponding variants of the two monolithic fundamental problems in process mining (van der Aalst 2011): automated process discovery, which considers the problem of discovering a process model from an event log (Leemans 2009), and conformance checking, which addresses the problem of analyzing the adequacy of a process model with respect to observed behavior (Munoz-Gama 2009), respectively. The term decomposed in the two definitions is mainly describing the way the two problems are tackled operationally, to face their computational complexity by splitting the initial problem into smaller problems, that can be solved individually and often more efficiently.Postprint (author's final draft

Repairing Alignments of Process Models

Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds

Enhancing BPMN Conformance Checking with OR Gateways and Data Objects

Äriprotsessimudel ja -notatsioon (BPMN) on arenev standard äriprotsesside graafiliseks kujutamiseks. Protsessimudel kirjeldab, kuidas äriprotsess peaks toimima. Kui äriprotsessi tegelikust käitamisest on saadaval ka sündmuste logi, on võimalik vastata küsimusele, kas protsessimudel vastab tegelikkusele. Vastavusanalüüs püüab tuvastada mittevastavusi protsessimudeli ja äriprotsessi käitamisel tekkinud sündmuste logi vahel. BPMN vastavuseanalüsaator on üks Itaalia ettevõtte SIAV-i poolt arendatud protsessikaeve tööriista osadest. Nimetatud tööriistal on aga puudujäägid formaalse semantika osas. Nimelt keskendub vastavusanalüüs järgnevuse voole (control-flow) protsessis, kuid jätab arvesse võtmata andmetevahelisi sõltuvusi. Lisaks ei ole vastavusanalüüsil võimalik kasutada protsessimudeleid, mis sisaldavad OR väravaid (OR gateway). OR-join omab mitme-tähenduslikku semantikat. Se lle konstruktsiooni jaoks on pakutud mitmeid formaalseid semantikaid sarnastes keeltes, nagu EPCs ja YAWL. Nimetatud semantikate kasutatamine mudelite käitamisel ja vastavuse analaüüsil on aga arvutuslikult kulukas. Seega on käesolevas lõputöös implementeeritud OR värava aktiveerimine lineaarse ajalise sõltuvusega mudeli suuruse suhtes. Kuna SIAV-i vastavusanalüsaator ei võta arvesse andmetevahelisi sõltuvusi, võib puudulik analüüs viia vigase vastavusdiagnostikani. Näiteks võib andmeatribuut anda infot selle kohta, et käitati vale tegevus. Kirjeldatud põhjustel ei peaks vastavusanalüsaator tegelema vaid järgnevuse voo vastavuse analüüsiga, vaid peaks arvesse võtma ka andmeid ja nendevahelisi sõltuvusi ning aega. Käesoleva töö teises osas täiendati olemasolevat andmeanalüsaatorit andmeatribuutidega.The Business Process Model and Notation is a developing standard for capturing business processes. Process models describe how the business process is expected to be executed. When a log is available from process executions, this situation raises the interesting question “Are the model and the log conformant?". Conformance checking, also referred to as conformance analysis, aims at the detection of inconsistencies between a process model and its corresponding execution log.The BPMN conformance checker, as a part of a process mining tool, developed an Italian company called SIAV, however, this tool lacks some formal semantics. In particular, the previous conformance checking approach in SIAV tends to focus on the control-flow in a process, while abstracting from data dependencies and process models containing OR gateways could not be used.OR-join has an ambiguous semantics. The several formal semantics of this construct have been proposed for similar languages such as EPCs and YAWL. However, executing and verifying models using these semantics is computationally expensive. Therefore, in this thesis, we implemented enablement of an OR-join in linear time in the size of the workflow graph.Data dependencies are also not considered in conformance checker developed in SIAV, which may lead to misleading conformance diagnostics. For example, a data attribute may provide strong evidence that the wrong activity was executed. That’s why the conformance checker should not only describe the process behaviour from the control flow point of view, but also from other perspectives like data or time. In the second part of the thesis, we enhanced the existing conformance checker with data attributes

Greedy Approach to Compute Alignments of Process Models and Event Logs

openIn Process Mining, computing alignments is a conformance-checking technique to compare a process model with an event log of the same process to pinpoint difference between how the model would prescribe the process to be executed, and how the event log states the process has been executed. The complexity of this problem is naturally exponential with respect to the size of the model, and benefits can be achieved using divide-and-conquer approaches: the model is decomposed into small fragment for which we can compute alignments. This thesis compares the time to compute alignments using the traditional approaches and our decomposition-based approaches to identify the possible benefits. The results are also compared with different approaches based on process-model decompositions.In Process Mining, computing alignments is a conformance-checking technique to compare a process model with an event log of the same process to pinpoint difference between how the model would prescribe the process to be executed, and how the event log states the process has been executed. The complexity of this problem is naturally exponential with respect to the size of the model, and benefits can be achieved using divide-and-conquer approaches: the model is decomposed into small fragment for which we can compute alignments. This thesis compares the time to compute alignments using the traditional approaches and our decomposition-based approaches to identify the possible benefits. The results are also compared with different approaches based on process-model decompositions

Data-aware Conformance Checking

Vastavuse kontrollimine on üks kõige tavalisemaid ülesandeid protsessikaeve valdkonnas. Vastavuse kontrollimise peamine eesmärk on kontrollida protsessimudeli vastavust sündmuste logidele selleks, et hinnata või kirjeldada kuidas registreeritud käitumine protsessimudelis kirjeldatud käitumisest erineb. Enamus olemasolevatest vastavuse kontrollimise tehnikaid põhineb kontrollvoolu perspektiivile. Käesolev lõputöö pakub välja tehnika, mis lisaks kontrollvoolule põhinevale tehnikale arvestab ka andmete perspektiivile. Väljapakutud lähenemisviis on implementeeritud tarkvaralise lahendusena, mis kasutab sisendiks BPMN mudelit ja sündmuste logi. Loodud tarkvara töörist on loodud kasutades programmeerimiskeelt Elixir. Lõputöö sisaldab samuti ka välja töötatud lahenduse tulemuslikkuse hinnangut.Conformance checking is one of the most common tasks in the field process mining. The goal of conformance checking is to compare a process model against an event log in order to quantify or describe how the behavior recorded in the log deviates with respect to the behavior captured by the process model. Most of the existing conformance checking techniques focus on the control-flow perspective. In this thesis, we propose a conformance checking technique that takes into account the data perspectives in addition to the control-flow perspective. The proposed approach is implemented as a tool that takes as input a BPMN process model and an event log. The tool has been implemented using the Elixir programming language. The thesis also reports on a performance evaluation of the proposed approach

Conformance Checking of Large Process Model: An Approach based on Decomposition

Conformance checking is the problem to pinpoint deviations between how processes are executed in reality and how processes are expected to be performed according to norms, regulations and protocols. The executions are recorded in event logs, while the expected behaviors are encoded in a process model. The complexity of the problem is exponential with respect to the size of the model, this makes the problem not scale when models become very large. To keep the problem tractable, one can decompose the model into parts for which conformance checking is carried out

Aligning Data-Aware Declarative Process Models and Event Logs

Vastavusanalüüs on haru protsessikaevanduses, mis võimaldab analüütikutel saada aru, kas äriprotsesside sooritused järgivad mudeldatud käitumist. Protsesside mudelid võivad olla nii protseduurilised kui ka deklaratiivsed. Kui protseduurilised mudelid kirjeldavad ära täpsed võimalikud tegevused, siis deklaratiivsed mudelid kirjeldavad reeglid, mis peavad olema protsessi sooritusel olema järgitud. Äriprotsesside täitmiste hoiustamiseks kasutatakse sündmuste logisid. Vastavusanalüüsi meetodid kontrollivad erinevaid protsessi sooritusega seotud vaateid, milleks on juhtimisvoog, andmed ja ressursid. Meetodid, mis käsitlevad endas lisaks juhtimisvoole ka andmeid ning ressursse kutsutakse mitmevaatelisteks või andmeteadlikeks lähenemisteks. Mitmevaatelised meetodid annavad rohkem informatsiooni kõrvalekallete kohta võrreldes juhtimisvoogudel põhinevate meetoditega. Joondustel põhinevad vastavusanalüüsi meetodid on olnud edukad nii juhtimisvool põhinevate kui ka andmeteadlike lähenemiste puhul. On olemas mitmeid joondamisel põhinevaid andmeteadlikke lähenemisi protseduuriliste mudelite jaoks, kuid deklaratiivsete mudelite jaoks need puuduvad.Antud töös on kohandatud olemasolev meetod, mis võimaldab sooritada vastavusanalüüsi andmeteadlike protseduuriliste mudelite puhul, kasutades logide joondustel põhinevat meetodit, võimaldamaks kasutamist ka deklaratiivsetel mudelitel. Deklaratiivsetel mudelitel rakendatav meetod implementeeriti moodulina protsessikaeve keskkonna ProM jaoks ja hinnati implementatsiooni kasutades erinevaid sündmuste logisid.Märksõnad: Protsessikaevandus, Deklaratiivsed protsessimudelid, Andmeteadlik vastavusanalüüs, JoondamineConformance checking, a branch of process mining, allows analysts to determine whether the execution of a business process matches the modeled behavior. Process models can be procedural or declarative. Procedural models dictate the exact behavior that is allowed to execute a specific process whilst declarative models implicitly specify allowed behavior with the rules that must be followed during execution. The execution of a business process is represented by event logs. Conformance checking approaches check various perspectives of a process execution including control-flow, data and resources. Approaches that checks not only the control-flow perspective, but also data and resources are called multi-perspective or data-aware approaches. The approaches provide more deviation information than control-flow based techniques. Alignment based techniques of conformance checking have proved to be advantageous in both control-flow based and data-aware approaches. While there exist several data-aware approaches for procedural process models that are based on the principle of finding alignments, there is none so far for declarative process models. In this thesis, we adapt an existing technique for finding alignments of logs and data-aware procedural models to declarative models. We implemented our approach as a plugin of the process mining framework ProM and evaluated it using event logs with different characteristics.Keywords: Process Mining, Declarative Process Models, Data-aware Conformance checking, Alignmen