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

Conformance checking and diagnosis for declarative business process models in data-aware scenarios

A business process (BP) consists of a set of activities which are performed in coordination in an organizational and technical environment and which jointly realize a business goal. In such context, BP management (BPM) can be seen as supporting BPs using methods, techniques, and software in order to design, enact, control, and analyze operational processes involving humans, organizations, applications, and other sources of information. Since the accurate management of BPs is receiving increasing attention, conformance checking, i.e., verifying whether the observed behavior matches a modelled behavior, is becoming more and more critical. Moreover, declarative languages are more frequently used to provide an increased flexibility. However, whereas there exist solid conformance checking techniques for imperative models, little work has been conducted for declarative models. Furthermore, only control-flow perspective is usually considered although other perspectives (e.g., data) are crucial. In addition, most approaches exclusively check the conformance without providing any related diagnostics. To enhance the accurate management of flexible BPs, this work presents a constraint-based approach for conformance checking over declarative BP models (including both control-flow and data perspectives). In addition, two constraint-based proposals for providing related diagnosis are detailed. To demonstrate both the effectiveness and the efficiency of the proposed approaches, the analysis of different performance measures related to a wide diversified set of test models of varying complexity has been performed.Ministerio de Ciencia e Innovación TIN2009-1371

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

What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

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

Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

For more than a decade, the interest in aligning information systems in a process-oriented way has been increasing. To enable operational support for business processes, the latter are usually specified in an imperative way. The resulting process models, however, tend to be too rigid to meet the flexibility demands of the actors involved. Declarative process modeling languages, in turn, provide a promising alternative in scenarios in which a high level of flexibility is demanded. In the scientific literature, declarative languages have been used for modeling rather simple processes or synthetic examples. However, to the best of our knowledge, they have not been used to model complex, real-world scenarios that comprise constraints going beyond control-flow. In this paper, we propose the use of a declarative language for modeling a sophisticated healthcare process scenario from the real world. The scenario is subject to complex temporal constraints and entails the need for coordinating the constraint-based interactions among the processes related to a patient treatment process. As demonstrated in this work, the selected real process scenario can be suitably modeled through a declarative approach.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Data-Aware Declarative Process Mining with SAT

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this paper, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely log generation, conformance checking and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT

Runtime Monitoring of Data-Aware business rules with Integer Linear Programming

Käitusaegne seire (Runtime Compliance Monitoring) on oluline osa äriprotsesside halduse elutsüklis, mittevastavuse õigeaegses avastamises, samuti vastumeetmete korraldamises ja ennetamises. Täpsemalt on see seotud operatiivse otsuse toega, mille eesmärgiks on laiendada protsessikaeve tehnikat sidusrežiimis, käitada protsessi isendeid nii, et kõrvalekaldeid on võimalik avastada, ning on võimalik soovitada, mida võiks järgmiseks teha, ning samuti ennustada, mis hakkab juhtuma tulevaste juhtumite täitmisel. Antud magistritöö keskendub käitusaegse seire andmeteadlikele ärireeglitele. Töös kasutatakse varajaste rikkumiste tuvastamiseks lineaarset täisarvulist planeerimist (Integer Linear Programming (ILP)), mida rakendatakse kahe või enama kitsenduse koosmõjul. Töökorras toepakkujas on rakendatud protsessikaeve raamistikku ProM ja meetod on valideeritud kasutades sünteetilisi ja reaalseid logisid.Runtime Compliance Monitoring is vital building block in the Business Process Management lifecycle, in timely detection of non-compliance as well as provision of responsive and proactive countermeasures. In particular, it is linked to operational decision support, which aims at extending the application of process mining techniques to on-line, running process instances, so that deviations can be detected and it is possible to recommend what to do next and predict what will happen in the future instance execution. \n\r\n\rIn this thesis, we focus on Runtime Compliance Monitoring of data-aware business rules. In particular, we use Integer Linear Programming (ILP) for early detection of violations that occur from interplay of two or more constraints. An operational support provider has been implemented as part of process mining framework ProM and the approach has been validated using synthetic and real life logs

Rule Mining with RuM

Protsessikaeve on äriprotsesside juhtimise (BPM) valdkonda kuuluv andmeteaduse tehnika, mille lähtepunktiks on protsessi täitmise logide analüüsimine. Hetkel kasutatakse protsessikaeve jaoks põhiliselt kahte töölauarakendust: Disco ja ProM. Disco sobib protseduuriliseks protsesside tuvastamiseks ja on intuitiivse kasutusliidesega, aga ei toeta deklaratiivseid mudeleid. ProM toetab mitmeid erinevaid deklaratiivse protsessikaeve tehnikaid, aga on ebaintuitiivsema kasutusliidesega kui Disco. Antud magistritöö eesmärk on välja pakkuda uus deklaratiivsele protsessianalüütikale suunatud tööriist RuM, mis pakub ühtlasi intuitiivset kasutajakogemust. RuM pakub sobiva keskkonna protsesside väljaselgitamise, vastavuskontrolli ja logide genereerimise meetodite jaoks. Kõik RuM poolt toetatud meetodid tuginevad deklaratiivsetel mudelitel ja on kasutaja poolt seadistatavad. Meetodite tulemusi on võimalik lihtsustamise eesmärgil järeltöödelda ja failisüsteemi salvestada. RuM töölauarakenduse hindamiseks on teostatud kasutatavuse testimine, milles osales viis deklaratiivse protsessianalüütika valdkonna eksperti.Process Mining is a data science practice in Business Process Management (BPM). It is based on the analysis of process execution logs. Nowadays, ProM and Disco are two mainstream desktop applications for process mining. Disco can be used for procedural process discovery with intuitive user experience. However, it does not support declarative models. ProM gives support for a wealth of techniques for declarative process mining. However, its user interface is not so intuitive as the one of Disco. In the light of this, this Master’s Thesis is focused on proposing RuM, an application for declarative process analysis with an intuitive user experience.The application presents an environment for a set of process discovery, conformance checking and log generation methods based on declarative models. The methods can be configured with various options and the results can be post-processed to simplify and store them in the file system. The tool has been evaluated through a usability test involving 5 experts on declarative process analysis