Patrick Delfmann

Adaptive Reference Modeling: Integrating Configurative and Generic Adaptation Techniques for Information Models

Reference models have to be adapted to fit to the according application situation. In order to reduce the adaptation efforts, the concept of configurative reference modeling represents a promising approach. Nevertheless, since not every requirement of possible reference model users can be anticipated by the reference model developer, further model adaptations have to be performed. In order to support the reference model user decreasing his adaptation efforts by providing a higher methodological support, we propose to integrate generic model adaptation techniques with configurative reference modeling. Our paper presents recommendations for the construction of modeling languages that realize an integration of configurative and generic reference modeling.

Vorbereitung der Prozessmodellierung

Die Notwendigkeit, sich mit Geschaftsprozessen auseinander zu setzen und diese hierzu transparent zu beschreiben, hat dazu gefuhrt, dass mittlerweile in vielen Unternehmen fur eine Vielzahl an Verwendungszwecken, mit diversen Modellierungstechniken und Modellierungstools, fur eine Vielzahl von Modellnutzern, eine Vielzahl an Modellen erstellt wird.

Generalizability and Applicability of Model-Based Business Process Compliance-Checking Approaches – A State-of-The-Art Analysis and Research Roadmap

With a steady increase of regulatory requirements for business processes, automation support of compliance management is a field garnering increasing attention in Information Systems research. Several approaches have been developed to support compliance checking of process models. One major challenge for such approaches is their ability to handle different modeling techniques and compliance rules in order to enable widespread adoption and application. Applying a structured literature search strategy, we reflect and discuss compliance-checking approaches in order to provide an insight into their generalizability and evaluation. The results imply that current approaches mainly focus on special modeling techniques and/or a restricted set of types of compliance rules. Most approaches abstain from real-world evaluation which raises the question of their practical applicability. Referring to the search results, we propose a roadmap for further research in model-based business process compliance checking.

Konstruktion von Referenzmodellierungssprachen Ein Ordnungsrahmen zur Spezifikation von Adaptionsmechanismen für Informationsmodelle

Construction of Reference Modeling Languages — A Framework for the Specification of Adaptation Mechanisms for Conceptual Information Models Reference modeling languages differ from each other as they use different model types (such as process or data models) and as they provide different mechanisms that allow an adaptation of the reference model to specific contexts. The developer of the reference modeling language has to decide which adaptation technique he wants to use (e. g. configuration, aggregation, instantiation, specialization or analogy construction) and which of these techniques he wants to integrate into the language specification. In this paper, these adaptation techniques are compared, and reference solutions for the specification of extended reference modeling languages are proposed. The introduced solutions are structured by a methodical framework that assigns modeling examples and meta-models as well as meta-meta-models to the different adaptation techniques. Based on this framework, possible combinations of configurative adaptation mechanisms with aggregative, instantiation based, specialization based and analogy construction based mechanisms are discussed.

Comprehensible predictive models for business processes

Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The topic is gaining momentum in process mining, a field of research that has traditionally developed tools to discover business process models from data sets of past process behavior. Predictive modeling techniques are built on top of process-discovery algorithms. As these algorithms describe business process behavior using models of formal languages (e.g., Petri nets), strong language biases are necessary in order to generate models with the limited amounts of data included in the data set. Naturally, corresponding predictive modeling techniques reflect these biases. Based on theory from grammatical inference, a field of research that is concerned with inducing language models, we design a new predictive modeling technique based on weaker biases. Fitting a probabilistic model to a data set of past behavior makes it possible to predict how currently running process instances will behave in the future. To clarify how this technique works and to facilitate its adoption, we also design a way to visualize the probabilistic models. We assess the effectiveness of the technique in an experimental evaluation with synthetic and real-world data.

Pattern Specification and Matching in Conceptual Models - A Generic Approach Based on Set Operations

Searching for patterns in conceptual models is useful for a number of purposes, for example revealing syntactical errors, model comparison, and identification of business process improvement potentials. In this contribution, we introduce a formal approach for the specification and matching of structural patterns in conceptual models. Unlike existing approaches, we do not focus on a certain application problem or a specific modelling language. Instead, our approach is generic making it applicable for any pattern matching purpose and most conceptual modelling languages. In order to build sets representing structural model patterns, we define formal operations based on set theory, which can be applied to arbitrary models represented by sets. The basic sets represent the model elements, which in turn originate from the modelling language specification’s instances. Besides a conceptual and formal specification of our approach, we present particular application examples and a prototypical modelling tool showing its general applicability.

e-Government Success Factors

Electronic Government (eGovernment) has been seen as the silver bullet for modernizing public administrations over last few years. But fact is that the broad variety of eGovernment initiatives results in an evenly heterogeneous picture regarding the quality of eGovernment services provided. Within an international context differences in eGovernment quality may result from nationally specific factors such as e-readiness, legal restrictions, existence of a nation-wide eGovernment strategy, and so forth. But what are the factors which have influence on eGovernment success and which do not result from national characteristics? In order to answer this question we conducted an empirical study in the northern German region “Muensterland”. The goal of this article is to present this empirical study. Internal data from 56 and external data from 70 local municipal administrations was gathered and analyzed with regard of the question: What are eGovernment success factors?

Business process compliance checking --- applying and evaluating a generic pattern matching approach for conceptual models in the financial sector

Given the strong increase in regulatory requirements for business processes the management of business process compliance becomes a more and more regarded field in IS research. Several methods have been developed to support compliance checking of conceptual models. However, their focus on distinct modeling languages and mostly linear (i.e., predecessor-successor related) compliance rules may hinder widespread adoption and application in practice. Furthermore, hardly any of them has been evaluated in a real-world setting. We address this issue by applying a generic pattern matching approach for conceptual models to business process compliance checking in the financial sector. It consists of a model query language, a search algorithm and a corresponding modelling tool prototype. It is (1) applicable for all graph-based conceptual modeling languages and (2) for different kinds of compliance rules. Furthermore, based on an applicability check, we (3) evaluate the approach in a financial industry project setting against its relevance for decision support of audit and compliance management tasks.

Formalizing Linguistic Conventions for Conceptual Models

A precondition for the appropriate analysis of conceptual models is not only their syntactic correctness but also their semantic comparability. Assuring comparability is challenging especially when models are developed by different persons. Empirical studies show that such models can vary heavily, especially in model element naming, even if they express the same issue. In contrast to most ontology-driven approaches proposing the resolution of these differences ex-post, we introduce an approach that avoids naming differences in conceptual models already during modeling. Therefore we formalize naming conventions combining domain thesauri and phrase structures based on a lin-guistic grammar. This allows for guiding modelers automatically during the modeling process using standardized labels for model elements. Our approach is generic, making it applicable for any modeling language.

Supporting Distributed Conceptual Modelling through Naming Conventions - A Tool-based Linguistic Approach

Empirical studies attest that conceptual models created in distributed modelling environments often vary heavily in the way their respective model elements are labelled. Although the same issues are being modelled, different names are chosen by the involved persons. By this, the analysis and comparison of the models, which is required for their subsequent integration, is extremely challenging and time consuming. Literature analysis reveals several approaches addressing this problem by either manually or semi-automatically integrating existing models after their construction. However, this proves to be an exhaustive and error prone task. In this article we propose a domain and modelling language independent approach that prevents the emergence of naming conflicts already during the modelling process. This is done by formalising naming conventions consisting of context specific thesauri and customised phrase structures, which are both derived from natural language grammars and supplemented by domain-specific terms. These conventions serve as basis for a fully automated guidance of the modeller during the model creation process, resulting in semantically comparable conceptual models. For this, we present a research prototype that integrates our approach into a modelling tool.