Automated error correction of business process models

As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model within a reasonable response time

Propagating Changes between Declarative and Procedural Process Models

Debatt protseduuriliste ja deklaratiivsete keelte eeliste ja puuduste üle erinevate kasutusjuhtude korral on olnud tuline. Protseduurilised keeled on sobivamad operatiivsete protsesside modelleerimiseks, deklaratiivsed keeli kasutatakse regulatsioonide/juhiste jaoks. Ometi tekib olukordi, kus on mõistlik kombineerida neid keeli, et saavutada parem tulemus. Selle asemel, et sundida modelleerijaid õppima uusi hübriidkeeli, peame me paremaks kahe spetsifikatsiooni eraldi hoidmist ja pakume välja viisi, kuidas protseduurilist mudelit automaatselt muuta nii, et see oleks kooskõlas deklaratiivsete reeglitega. Nõudlus sellise lahenduse jaoks tekib, näiteks kui organisatsioon peab muutma protsesse vastavalt muutuvatele välistele reeglitele. Üldiselt on nii võimalik ära kasutada deklaratiivsete keelte paindlikust ja hoida kõrgetasemelist tuge, mida pakuvad protseduurilised keeled. Lisaks, võrreldes originaalset ja parandatud mudelit, on võimalik selgelt näha reeglite mõju. Käesolevas lõputöös sõnastame me antud probleemi, loome teoreetilise vundamendi ja pakume välja olekumasinatel põhineva lahenduse, mida me võrdleme olemasolevate lahendustega mudelite parandamiseks ja protsesside avastamiseks.The debate on advantages and disadvantages of declarative versus procedural process modelling languages for different usage scenarios has been intense. Procedural languages are more suited for describing operational processes while declarative ones for expressing regulations/guidelines, and in many situations the need of combining the benefits of the two rises. Instead of forcing modellers to use a hybrid language, we envisage to keep the two specifications separate and propose a technique that automatically adapts procedural models so as to comply with sets of declarative rules. This not only fits scenarios where, e.g., company processes have to be modified according to changing external rules, but, more in general, it presents a way to take advantage of the flexibility of declarative while maintaining the high level of support provided by procedural languages. Furthermore, by comparing the original and the resulting procedural models, the impact of rules is clearly exposed. In this thesis, we frame the problem above by providing its theoretical characterisation and propose an automata-based solution, which is then evaluated against approaches leveraging state-of-the-art techniques for process discovery and model repair

Использование журналов событий для локальной корректировки моделей процессов

During the life-cycle of an Information System (IS) its actual behaviour may not correspond to the original system model. However, to the IS support it is very important to have the latest model that reflects the current system behaviour. To correct the model, the information from the event log of the system may be used. In this paper, we consider the problem of process model adjustment (correction) using the information from an event log. The input data for this task are the initial process model (a Petri net) and the event log. The result of correction should be a new process model, better reflecting the real IS behavior than the initial model. The new model could be also built from scratch, for example, with the help of one of the known algorithms for automatic synthesis of the process model from an event log. However, this may lead to crucial changes in the structure of the original model, and it will be difficult to compare the new model with the initial one, hindering its understanding and analysis. It is important to keep the initial structure of the model as much as possible. In this paper, we propose a method for process model correction based on the principle of “divide and conquer”. The initial model is decomposed in several fragments. For each fragment its conformance to the event log is checked. Fragments which do not match the log are replaced by newly synthesized ones. The new model is then assembled from the fragments via transition fusion. The experiments demonstrate that our correction algorithm gives good results when it is used for correcting local discrepancies. The paper presents the description of the algorithm, the formal justification for its correctness, as well as the results of experimental testing by some artificial examples.В ходе жизненного цикла информационной системы (ИС) ее реальное поведение может перестать соответствовать исходной модели системы. Между тем для поддержки системы очень важно иметь актуальную модель, отражающую текущее поведение системы. Для корректировки модели можно использовать информацию из журнала событий системы. Журналы событий процессно-ориентированных информационных систем содержат запись истории исполнения поддерживаемых процессов в виде более или менее детальных списков событий. Такие журналы, как правило, записываются всеми современным ИС. Эта информация может использоваться для анализа реального поведения ИС и ее усовершенствования. В работе рассматривается задача корректировки (исправления) модели процесса на основе информации из журнала событий. Исходными данными для этой задачи являются первоначальная модель процесса в виде сети Петри и журнал событий. Результатом корректировки должна быть новая модель процесса, лучше отображающая реальное поведение ИС, чем исходная модель. Актуальная модель может быть построена и полностью заново, например, с помощью одного из известных алгоритмов автоматического синтеза модели процесса по журналу событий. Однако структура исходной модели при этом может полностью измениться. Полученную модель будет трудно сопоставить с прежней моделью процесса, что затруднит ее понимание и анализ. Поэтому при корректировке модели важно по возможности сохранить ее прежнюю структуру. Предлагаемый в настоящей работе алгоритм корректировки модели основан на принципе «разделяй и властвуй». Исходная модель процесса декомпозируется на фрагменты. Для каждого из фрагментов проверяется, соответствует ли он актуальному журналу событий. Фрагменты, для которых выявлены несоответствия, заменяются на заново синтезированные. Новая модель собирается из фрагментов путем слияния переходов. Проведенные эксперименты показывают, что наш алгоритм корректировки дает хорошие результаты, если применяется для исправления локальных несоответствий. Работа содержит описание алгоритма, формальное обоснование его корректности, а также результаты экспериментального тестирования на искусственных примерах.

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided

The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications

The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data

The Design of Graphical Process Modeling Languages: from Free Composition to Modular Construction

Un Process Modeling Language (PML) grafico \ue8 un linguaggio specializzato per la modellazione di sistemi software in termini di processi. Tale linguaggio \ue8 detto grafico perch\ue8 la rappresentazione principale dei modelli consiste in diagrammi ottenuti combinando costrutti grafici e componenti precedentemente definiti. Un Process-Aware Information System (PAIS) \ue8 un sistema software guidato da modelli di processi con lo scopo di coordinare e supportare gli agenti nello svolgimento delle loro attivit\ue0. Tale sistema \ue8 responsabile della gestione simulatanea di diverse istanze di processo e del bilanciamento delle risorse disponibili. Un PML \ue8 l'interfaccia principale di un PAIS ed un aspetto fondamentale della sua progettazione, poich\ue8 \ue8 utilizzato da utenti finali, consulenti, e sviluppatori al fine di comprendere, implementare ed eseguire processi complessi. L'utilizzo di tecnologie PAIS pu\uf2 essere considerevolmente limitato dalle carenze di un PML nel descrivere casi complessi. Lo scopo principale della tesi \ue8 migliorare la progettazione di PML grafici al fine di costruire PAIS pi\uf9 efficaci. Tale obiettivo \ue8 perseguito attraverso tre percorsi interconnessi: per prima cosa, i PMLs esistenti e la loro teoria sottostante sono stati analizzati al fine di individuare pregi e difetti; successivamente, una tecnica di verifica molto diffusa in questo campo \ue8 stata consolidata ed estesa con una nuova tecnica per la correzione automatica di processi. Infine, una diversa soluzione per il design di PMLs \ue8 stata esplorata attraverso la definizione di un nuovo linguaggio, chiamato NestFlow, che migliora la modularit\ue0 e la comprensibilit\ue0 attraverso l'adozione di un approccio strutturato alla modellazione di processi. Un approccio modulare \ue8 possible solo se gli aspetti legati ai dati sono accettati come aspetto primario nel design di un PML. NestFlow cerca di semplificare l'attivit\ue0 di modellazione fornendo un insieme integrato di costrutti di control-flow e data-flow, promuovendo i secondi come aspetti principali nella modellazione di processi.A graphical Process Modeling Language (PML) is a language tailored for modeling software systems by means of process models. It is said to be graphical because the primary representation of models are diagrams obtained combining visual constructs and previously defined components. Graphical PMLs are interesting as they open the design space to new geometric representations of complex interrelated aspects like concurrency and interaction. A Process-Aware Information System (PAIS) is a software system driven by explicit process models with the aim to coordinate and support agents in performing their activities. It is responsible for managing several process model instances at the same time balancing the available resources. A PML is the primary interface of a PAIS and a main concern in its design, because it is used by end-users, consultants, and developers for understanding, implementing and enacting complex processes. The adoption of PAIS technology may be severely limited by the weakness of PMLs in describing complex use cases. The overall aim of this thesis is to improve the design of graphical PMLs in order to engineer more effective PAISs. This goal is pursued following three intertwined paths: firstly, mainstream PMLs and their theoretical foundations are analyzed for exposing their features and limits; secondly, a widespread PML verification method is consolidated and then extended with a novel technique for automating process correction; finally, an alternative PML design solution is explored through a proof-of-concept language, called NestFlow, that improves both modularity and comprehensibility by providing a more structured modeling approach. A modular approach is only possible if data-flow dependencies are accepted as a main concern in PML design. NestFlow tries to ease the modeling activity by providing a comprehensive set of tightly integrated control-flow and data-flow constructs, promoting the latter as first-class citizens in process modeling

Automated error correction of business process models

Abstract. As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model within a reasonable response time

Automated error correction of business process models

As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging\ud as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this\ud technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model and that the response times are short