On the discovery of declarative control flows for artful processes

Artful processes are those processes in which the experience, intuition, and knowledge of the actors are the key factors in determining the decision making. They are typically carried out by the "knowledge workers," such as professors, managers, and researchers. They are often scarcely formalized or completely unknown a priori. Throughout this article, we discuss how we addressed the challenge of discovering declarative control flows in the context of artful processes. To this extent, we devised and implemented a two-phase algorithm, named MINERful. The first phase builds a knowledge base, where statistical information extracted from logs is represented. During the second phase, queries are evaluated on that knowledge base, in order to infer the constraints that constitute the discovered process. After outlining the overall approach and offering insight on the adopted process modeling language, we describe in detail our discovery technique. Thereupon, we analyze its performances, both from a theoretical and an experimental perspective. A user-driven evaluation of the quality of results is also reported on the basis of a real case study. Finally, a study on the fitness of discovered models with respect to synthetic and real logs is presented

Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Declarative Process Mining on the Cloud

Antud magistritöö annab ülevaate deklaratiivse keele ja deklaratiivse protsessikaeve algoritmide kohta. Sellele järgneb deklaratiivse protsessikaeve tarvis kasutatavate vahendite kirjeldus. Töö tagab eelnevalt käsitletud vahendite kättesaadavust pilvplatvormil ning tutvustab kaks uut vahendit, mis pakuvad sündmuse seirevõimekust ja deklaratiivse mudeli suulise esitluse genereerimist. Kõik kirjeldatud protsessikaeve vahendid on rakendatud kimpudena pilvplatvormil RuM. Samuti on kirjeldatud uus kasutajaliides ja vahendite funktsioonid. Töö hindamisosas olid esitatud pilvel olevad kaevevahendid ja otsesündmuste seirevahendi võimed.This thesis provides an overview of the Declare language and declarative process mining algorithms, followed by the description of currently available tools for a declarative process mining. This thesis provides the availability of all the discussed tools on a cloud platform and introduces two new tools. One provides the event monitoring capabilities and and the other one generates a verbal representation of a Declare model. All the described process mining tools are implemented as bundles of the cloud platform RuM. Afterwards, the new user interface and functionalities of the tools are described. The evaluation part of the thesis presents, the mining tools on the cloud and the capabilities of the live event monitoring tool

Interestingness of traces in declarative process mining: The janus LTLPf Approach

Declarative process mining is the set of techniques aimed at extracting behavioural constraints from event logs. These constraints are inherently of a reactive nature, in that their activation restricts the occurrence of other activities. In this way, they are prone to the principle of ex falso quod libet: they can be satisfied even when not activated. As a consequence, constraints can be mined that are hardly interesting to users or even potentially misleading. In this paper, we build on the observation that users typically read and write temporal constraints as if-statements with an explicit indication of the activation condition. Our approach is called Janus, because it permits the specification and verification of reactive constraints that, upon activation, look forward into the future and backwards into the past of a trace. Reactive constraints are expressed using Linear-time Temporal Logic with Past on Finite Traces (LTLp f). To mine them out of event logs, we devise a time bi-directional valuation technique based on triplets of automata operating in an on-line fashion. Our solution proves efficient, being at most quadratic w.r.t. trace length, and effective in recognising interestingness of discovered constraints

Generating Synthetic Event Logs based on Multi- perspective Business Rules

Traditsiooniline äriprotsesside modelleerimine kasutab imperatiivset lähenemist, kus äri-protsesse kirjeldatakse üksteise järel sooritatavate tegevuste abil. On näidatud, et imper-atiivne lähenemine on sobivam lahendus stabiilsete ja ennustatavate protsesside puhul. Deklaratiivsed mudelid seevastu sobivad muutuvate protsesside kirjeldamiseks. Deklaratiivne mudel sisaldab endas reeglite hulka mida ei tohi eirata protsessi käitamisel. Viimastel aastatel on arendatud mitmeid uusi meetodeid deklaratiivsete protsessimudelite leidmiseks sündmuste logidest. Meetodite testimiseks on vajalik tööriistade olemasolu, mis genereerivad sünteetilisi sündmuste logisid, mille peal neid meetodeid katsetada. Enamus olemasolevaid tööriistu kasutavad imperatiivseid protsessimudelid logide genereerimiseks. Selline lähenemine ei ole sobiv deklaratiivsete protsessimudelite avastamise meetodite tes-timiseks. Sarnaselt on olemas vajadus tööriistade järgi, mis genereeriks sündmuste logisid kasutades mitmeperspektiivseid Declare mudeleid. Käesolevas töös esitleme tööriista mitmeperspektiivsete Declare mudelite genereerimiseks. See töörist tõlgib Declare piirangud lõpliku olekumasina esitusse,et neid kasutada deklaratiivsete mudelite simu-leerimiseks. Tööriist võimaldab kasutajatel genereerida logisid eeldefineeritud omadustega ( näiteks protsessi instantside arv ja protsessi pikkus), mis on kooskõlas Declare mudeli-tega.\n\rMärksõnad: Declare, deklaratiivne protsessimudel, protsessi simuleerimine, logide gene-reerimine, mitmeperspektiive, lineaarne taisarvuline planeerimineTraditional business modelling is imperative in the sense that activities are provided step by step, from start to end, leading towards full business process. It has been proved that the imperative paradigm is most suitable in the context of stable and predictable processes. Declarative models are more suitable for variable processes. A declarative model is made of a set of constrains that cannot be violated during the process execution. In recent years, many techniques have been developed to discover declarative process model from event logs. To test these techniques it is sometime necessary to have tools that generate synthetic logs on which the techniques can be applied. However, majority of the existing tools avail-able in this field use simulation of an imperative process model to generate synthetic event logs. These approaches are not suitable for the evaluation of process discovery techniques using declarative process models. Additionally, there is a need for tools to generate event logs based on the simulation of multi-perspective declarative models. To close this gap, we developed a tool for log generation based on multi- perspective Declare models. This mod-el simulator will base on the translation of Declare constraints into Finite State Automata for the simulation of declarative processes. The tool will allows users to generate logs with predefined characteristics (e.g., number and length of the process instances), which is compliant with a given Declare model.\n\rKeywords: Declare, Declarative Process Models, Process Simulation, Log Generation, Multi-perspective, Integer Linear Programmin

Comprehensive process drift analysis with the visual drift detection tool

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this tool demonstration paper, we present a novel software tool to analyze process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The tool is of benefit to the researchers and practitioners in the business intelligence and process analytics area, and can constitute a valuable aid to those who are involved in business process redesign endeavors