Improving Exception Handling by Discovering Change Dependencies in Adaptive Process Management Systems

Process-aware information systems should enable the flexible alignment of business processes to new requirements by supporting deviations from the predefined process model at runtime. To facilitate such dynamic process changes we have adopted techniques from casebased reasoning (CBR). In particular, our existing approach allows to capture the semantics of ad-hoc changes, to support their memorization, and to enable their reuse in upcoming exceptional situations. To further improve change reuse this paper presents an approach for discovering dependencies between ad-hoc modifications from change history. Based on this information better user assistance can be provided when dynamic process changes have to be made

Partial-order-based process mining: a survey and outlook

The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms

Integration of Building Information Modeling (BIM) and Process Mining for Design Authoring Processes

Building Information Modelling (BIM) corresponds to the generation and management of the digital representation for building products by wrapping building elements and their information in a unique source file. Open BIM, relying on platform-independent standards, such as IFC (Industry Foundation Classes), is supposed to increase the interoperability in the BIM environment. BIM, as a shared work platform in AEC (Architecture, Engineering and Construction) industry, can be upgraded to act as an Enterprise Resource Management (ERM) system and support data mining for the management of design and construction processes. ERM systems rely on transaction data, also known as “event logs”. eXtensibile Event Stream (XES) is an XML (Extensible Markup Language) schema aiming to provide a format for supporting the interchange of event logs. XES-based Event logs commonly include some semantics (called extensions) regarding events. This work aims to enable BIM to act as an ERM system. To realize this goal, four research objectives were defined and achieved. First, an ‘IFC archiver algorithm’ was developed to take snapshots, on a regular basis, from different stages of building modeling process (performed in Autodesk Revit), throughout the design phase from start to the end. Second, an ‘IFC logger algorithm’ was created to consecutively compare archived IFC files, detect design activities and save them in the CSV format event log. Then, XESame module is used to map the CSV format event log to the appropriate data format for Process Mining (i.e., XES format event logs). The activities were categorized in five classes: Addition, Removal, Rotation, Relocation of elements (e.g., a wall), and changes in their properties (e.g., the size, type or family of an element). Five attributes for each activity were stored in the database. Those included: Element ID, Designer, Element Name (Name of the Activity), Start and End time of each activity. Third, Process Mining techniques were used to detect the as-happened processes. Last but not least, Process Mining helped to derive different types of design process information (analytics) such as social networks of actors, bottlenecks of processes and process deviations. Two case studies were performed to validate and verify the research methodology. Around 300 and 30,000 events were captured respectively, during the design phase of our first and second case studies. Then, the activity log was fed to a Process Mining tool to mine the as-happened design processes. Two levels of process maps were discovered: As-happened level 2 and “level 3” BIM maps. As-happened maps were derived and represented in Petri net and process tree formats. Moreover, different types of animations of the as-happened design processes were derived for level 2 and “level 3” BIM maps from replaying the event logs on top of the captured processes. Those animations showed project paths, activities queue lengths and service times. In a nutshell, the study successfully applied Process Mining on the foundation of BIM (as an ERM system) and accordingly made discovery, monitoring and optimizing BIM processes possible. The present study aims to assist BIM and project managers by enabling BIM as a management tool for design processes. These processes are important, because the design phase is at the early stage of every construction project

Integration Adaptiver Prozess-Management-Technologie und Process Mining

Viele Unternehmen haben in den letzten Jahren prozess-gestützte Informationssysteme(Process-aware Information Systems, PAIS) eingeführt, um ihre Geschäftsprozesse zu unterstützen. Diese Systeme zeichnen typischerweise die Ereignisse auf, die sich auf die Ausführungen aktueller Geschäftsprozesse beziehen (z.B. in Ausführungs-Logs). Diese Daten können sowohl für eine Prozess-Performanz- Analyse als auch für Prozessoptimierungen verwendet werden. Process Mining bietet in diesem Kontext viel versprechende Perspektiven. Die bisher existierenden Mining Techniken werden im Zusammenhang mit operativen Prozessen verwendet, d.h. Information wird aus Ausführungs-Logs extrahiert (Prozesserkennung) oder Ausführungs-Logs werden mit den zugrunde liegenden Prozessmodellen verglichen (Konformitätsprüfung). Allerdings machen Ausführungs- Logs nur einen Teil der Daten aus, die während der Prozessausführung gesammelt werden. Adaptive Prozess-Management-Systeme (PMS) bieten zusätzliche Informationen über Prozessänderungen (z.B. Ad-Hoc-Änderungen einer Prozessinstanz) in Änderungs-Logs. Aus diesen Log-Daten können Informationen für mögliche Prozessoptimierungen gewonnen werden und die adaptiven PMS bieten die Werkzeuge, um durch Process Mining angestoßene Prozessoptimierungen nahtlos einzubringen. In dieser Arbeit werden verschiedene Process Mining Algorithmen evaluiert und basierend auf den Ergebnissen ein Rahmenwerk vorgestellt, das die Technologien adaptives Prozess-Management und Process Mining zusammenfasst, um die Vorteile beider Ansätze nutzen zu können. Dazu wird ein Datenmodell für Änderungs-Log-Daten präsentiert und ein neuer Algorithmus für das Mining von Änderungs-Logs eingeführt. Der Änderungsprozess, den dieser neue Algorithmus (Change Mining Algorithmus) liefert, bietet eine Gesamtübersicht über alle Änderungen die (bisher) stattgefunden haben. Diese kann wiederum als Basis für alle Arten von Prozessoptimierungen dienen, z.B. könnten eine Neugestaltung des Prozesses oder bessere Kontrollmechanismen veranlasst werden

A genetic programming based business process mining approach

As business processes become ever more complex there is a need for companies to understand the processes they already have in place. To undertake this manually would be time consuming. The practice of process mining attempts to automatically construct the correct representation of a process based on a set of process execution logs. The aim of this research is to develop a genetic programming based approach for business process mining. The focus of this research is on automated/semi automated business processes within the service industry (by semi automated it is meant that part of the process is manual and likely to be paper based). This is the first time a GP approach has been used in the practice of process mining. The graph based representation and fitness parsing used are also unique to the GP approach. A literature review and an industry survey have been undertaken as part of this research to establish the state-of-the-art in the research and practice of business process modelling and mining. It is observed that process execution logs exist in most service sector companies are not utilised for process mining. The development of a new GP approach is documented along with a set of modifications required to enable accuracy in the mining of complex process constructs, semantics and noisy process execution logs. In the context of process mining accuracy refers to the ability of the mined model to reflect the contents of the event log on which it is based; neither over describing, including features that are not recorded in the log, or under describing, just including the most common features leaving out low frequency task edges, the contents of the event log. The complexity of processes, in terms of this thesis, involves the mining of parallel constructs, processes containing complex semantic constructs (And/XOR split and join points) and processes containing 20 or more tasks. The level of noise mined by the business process mining approach includes event logs which have a small number of randomly selected tasks missing from a third of their structure. A novel graph representation for use with GP in the mining of business processes is presented along with a new way of parsing graph based individuals against process execution logs. The GP process mining approach has been validated with a range of tests drawn from literature and two case studies, provided by the industrial sponsor, utilising live process data. These tests and case studies provide a range of process constructs to fully test and stretch the GP process mining approach. An outlook is given into the future development of the GP process mining approach and process mining as a practice.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Graph-based Pattern Matching and Discovery for Process-centric Service Architecture Design and Integration

Process automation and applications integration initiatives are often complex and involve significant resources in large organisations. The increasing adoption of service-based architectures to solve integration problems and the widely accepted practice of utilising patterns as a medium to reuse design knowledge motivated the definition of this work. In this work a pattern-based framework and techniques providing automation and structure to address the process and application integration problem are proposed. The framework is a layered architecture providing modelling and traceability support to different abstraction layers of the integration problem. To define new services - building blocks of the integration solution - the framework includes techniques to identify process patterns in concrete process models. Graphs and graph morphisms provide a formal basis to represent patterns and their relation to models. A family of graph-based algorithms support automation during matching and discovery of patterns in layered process service models. The framework and techniques are demonstrated in a case study. The algorithms implementing the pattern matching and discovery techniques are investigated through a set of experiments from an empirical evaluation. Observations from conducted interviews to practitioners provide suggestions to enhance the proposed techniques and direct future work regarding analysis tasks in process integration initiatives