A framework for active software engineering ontology

The passive structure of ontologies results in the ineffectiveness to access and manage the knowledge captured in them. This research has developed a framework for active Software Engineering Ontology based on a multi-agent system. It assists software development teams to effectively access, manage and share software engineering knowledge as well as project information to enable effective and efficient communication and coordination among teams. The framework has been evaluated through the prototype system as proof-of-concept experiments

Strategies for Managing Linked Enterprise Data

Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle

HybridMDSD: Multi-Domain Engineering with Model-Driven Software Development using Ontological Foundations

Software development is a complex task. Executable applications comprise a mutlitude of diverse components that are developed with various frameworks, libraries, or communication platforms. The technical complexity in development retains resources, hampers efficient problem solving, and thus increases the overall cost of software production. Another significant challenge in market-driven software engineering is the variety of customer needs. It necessitates a maximum of flexibility in software implementations to facilitate the deployment of different products that are based on one single core. To reduce technical complexity, the paradigm of Model-Driven Software Development (MDSD) facilitates the abstract specification of software based on modeling languages. Corresponding models are used to generate actual programming code without the need for creating manually written, error-prone assets. Modeling languages that are tailored towards a particular domain are called domain-specific languages (DSLs). Domain-specific modeling (DSM) approximates technical solutions with intentional problems and fosters the unfolding of specialized expertise. To cope with feature diversity in applications, the Software Product Line Engineering (SPLE) community provides means for the management of variability in software products, such as feature models and appropriate tools for mapping features to implementation assets. Model-driven development, domain-specific modeling, and the dedicated management of variability in SPLE are vital for the success of software enterprises. Yet, these paradigms exist in isolation and need to be integrated in order to exhaust the advantages of every single approach. In this thesis, we propose a way to do so. We introduce the paradigm of Multi-Domain Engineering (MDE) which means model-driven development with multiple domain-specific languages in variability-intensive scenarios. MDE strongly emphasize the advantages of MDSD with multiple DSLs as a neccessity for efficiency in software development and treats the paradigm of SPLE as indispensable means to achieve a maximum degree of reuse and flexibility. We present HybridMDSD as our solution approach to implement the MDE paradigm. The core idea of HybidMDSD is to capture the semantics of particular DSLs based on properly defined semantics for software models contained in a central upper ontology. Then, the resulting semantic foundation can be used to establish references between arbitrary domain-specific models (DSMs) and sophisticated instance level reasoning ensures integrity and allows to handle partiucular change adaptation scenarios. Moreover, we present an approach to automatically generate composition code that integrates generated assets from separate DSLs. All necessary development tasks are arranged in a comprehensive development process. Finally, we validate the introduced approach with a profound prototypical implementation and an industrial-scale case study.Softwareentwicklung ist komplex: ausführbare Anwendungen beinhalten und vereinen eine Vielzahl an Komponenten, die mit unterschiedlichen Frameworks, Bibliotheken oder Kommunikationsplattformen entwickelt werden. Die technische Komplexität in der Entwicklung bindet Ressourcen, verhindert effiziente Problemlösung und führt zu insgesamt hohen Kosten bei der Produktion von Software. Zusätzliche Herausforderungen entstehen durch die Vielfalt und Unterschiedlichkeit an Kundenwünschen, die der Entwicklung ein hohes Maß an Flexibilität in Software-Implementierungen abverlangen und die Auslieferung verschiedener Produkte auf Grundlage einer Basis-Implementierung nötig machen. Zur Reduktion der technischen Komplexität bietet sich das Paradigma der modellgetriebenen Softwareentwicklung (MDSD) an. Software-Spezifikationen in Form abstrakter Modelle werden hier verwendet um Programmcode zu generieren, was die fehleranfällige, manuelle Programmierung ähnlicher Komponenten überflüssig macht. Modellierungssprachen, die auf eine bestimmte Problemdomäne zugeschnitten sind, nennt man domänenspezifische Sprachen (DSLs). Domänenspezifische Modellierung (DSM) vereint technische Lösungen mit intentionalen Problemen und ermöglicht die Entfaltung spezialisierter Expertise. Um der Funktionsvielfalt in Software Herr zu werden, bietet der Forschungszweig der Softwareproduktlinienentwicklung (SPLE) verschiedene Mittel zur Verwaltung von Variabilität in Software-Produkten an. Hierzu zählen Feature-Modelle sowie passende Werkzeuge, um Features auf Implementierungsbestandteile abzubilden. Modellgetriebene Entwicklung, domänenspezifische Modellierung und eine spezielle Handhabung von Variabilität in Softwareproduktlinien sind von entscheidender Bedeutung für den Erfolg von Softwarefirmen. Zur Zeit bestehen diese Paradigmen losgelöst voneinander und müssen integriert werden, damit die Vorteile jedes einzelnen für die Gesamtheit der Softwareentwicklung entfaltet werden können. In dieser Arbeit wird ein Ansatz vorgestellt, der dies ermöglicht. Es wird das Multi-Domain Engineering Paradigma (MDE) eingeführt, welches die modellgetriebene Softwareentwicklung mit mehreren domänenspezifischen Sprachen in variabilitätszentrierten Szenarien beschreibt. MDE stellt die Vorteile modellgetriebener Entwicklung mit mehreren DSLs als eine Notwendigkeit für Effizienz in der Entwicklung heraus und betrachtet das SPLE-Paradigma als unabdingbares Mittel um ein Maximum an Wiederverwendbarkeit und Flexibilität zu erzielen. In der Arbeit wird ein Ansatz zur Implementierung des MDE-Paradigmas, mit dem Namen HybridMDSD, vorgestellt

Efficient and Effective Event Pattern Management

The goal of this thesis is to reduce the barriers stopping more enterprises from accessing CEP technology by providing additional support in managing relevant business situations. Therefore we outline the role of event pattern management and present a methodology, methods and tools aiming at an efficient and effective event pattern management. We provide a meta model for event patterns, an event pattern life cycle methodology, methods for guidance, refinement and evolution

Semantic discovery and reuse of business process patterns

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In modern organisations business process modelling has become fundamental due to the increasing rate of organisational change. As a consequence, an organisation needs to continuously redesign its business processes on a regular basis. One major problem associated with the way business process modelling (BPM) is carried out today is the lack of explicit and systematic reuse of previously developed models. Enabling the reuse of previously modelled behaviour can have a beneficial impact on the quality and efficiency of the overall information systems development process and also improve the effectiveness of an organisation’s business processes. In related disciplines, like software engineering, patterns have emerged as a widely accepted architectural mechanism for reusing solutions. In business process modelling the use of patterns is quite limited apart from few sporadic attempts proposed by the literature. Thus, pattern-based BPM is not commonplace. Business process patterns should ideally be discovered from the empirical analysis of organisational processes. Empiricism is currently not the basis for the discovery of patterns for business process modelling and no systematic methodology for collecting and analysing process models of business organisations currently exists. The purpose of the presented research project is to develop a methodological framework for achieving reuse in BPM via the discovery and adoption of patterns. The framework is called Semantic Discovery and Reuse of Business Process Patterns (SDR). SDR provides a systematic method for identifying patterns among organisational data assets representing business behaviour. The framework adopts ontologies (i.e., formalised conceptual models of real-world domains) in order to facilitate such discovery. The research has also produced an ontology of business processes that provides the underlying semantic definitions of processes and their constituent parts. The use of ontologies to model business processes represents a novel approach and combines advances achieved by the Semantic Web and BPM communities. The methodological framework also relates to a new line of research in BPM on declarative business processes in which the models specify what should be done rather than how to ‘prescriptively’ do it. The research follows a design science method for designing and evaluating SDR. Evaluation is carried out using real world sources and reuse scenarios taken from both the financial and educational domains