Hanns-Alexander Dietrich

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.

The generic model query language GMQL - Conceptual specification, implementation, and runtime evaluation

The generic model query language GMQL is designed to query collections of conceptual models created in arbitrary graph-based modelling languages. Querying conceptual models means searching for particular model subgraphs that comply with a predefined pattern query. Such a query specifies the structural and semantic properties of the model fragment to be returned. In this paper, we derive requirements for a generic model query language from the literature and formally specify the languages syntax and semantics. We conduct an analysis of GMQLs theoretical and practical runtime performance concluding that it returns query results within satisfactory time. Given its generic nature, GMQL contributes to a broad range of different model analysis scenarios ranging from business process compliance management to model translation and business process weakness detection. As GMQL returns results with acceptable runtime performance, it can be used to query large collections of hundreds or thousands of conceptual models containing not only process models, but also data models or organizational charts. In this paper, we furthermore evaluate GMQL against the backdrop of existing query approaches thereby carving out its advantages and limitations as well as pointing toward future research. We describe the language GMQL for querying any graph-based conceptual model.GMQL is applicable to a broad range of model analysis scenarios.GMQL returns query results within (fractions of) milliseconds.The model size and the number of query results influence its runtime performance.GMQL provides features that no other existing query language provides.

A Runtime Analysis of Graph-Theoretical Algorithms to Detect Patterns in Process Model Collections

Pattern detection serves different purposes in managing large collections of process models, ranging from syntax checking to compliance validation. This paper presents a runtime analysis of four graph-theoretical algorithms for (frequent) pattern detection. We apply these algorithms to large collections of process and data models to demonstrate that, despite their theoretical intractability, they are able to return results within (milli-) seconds. We discuss the relative performance of these algorithms and their applicability in practice.

Graph theory and model collection management: conceptual framework and runtime analysis of selected graph algorithms

Abstract Analysing conceptual models is a frequent task of business process management (BPM), for instance to support comparison or integration of business processes, to check business processes for compliance or weaknesses, or to tailor conceptual models for different audiences. As recently, many companies have started to maintain large model collections and analysing such collections manually may be laborious, practitioners have articulated a demand for automatic model analysis support. Hence, BPM scholars have proposed a plethora of different model analysis techniques. As virtually any conceptual model can be interpreted as a mathematical graph and model analysis techniques often include some kind of graph problem, in this paper, we introduce a graph algorithm based model analysis framework that can be accessed by specialized model analysis techniques. To prove that basic graph algorithms are feasible to support such a framework, we conduct a performance analysis of selected graph algorithms.

A Language-Independent Model Query Tool

This paper introduces a prototype implementing a visual graph-based model query language. Querying models refers to identifying particular fragments in the model that comply with a predefined pattern query. The language takes advantage of the fact that models of any type and modelling language can conceptually be represented as a labeled graph. As a consequence the query language remains flexible and is not restricted to specific model types or languages. The language supports topologically exact as well as similar pattern matching and includes additional constraints and attributes in the matching process. In doing so, the language is applicable to many different analysis tasks. Following the design science approach we develop and demonstrate a prototype of such a language which allows for visually defining a pattern query and visually representing the results of the pattern matching process.

Developing Graphical Model Editors for Meta-Modelling Tools - Requirements, Conceptualisation, and Implementation

Meta-modelling tools have been proposed to facilitate the development and adoption of domain-specific modelling languages (DSMLs). These languages specify a set of domain-specific concepts and assign diagrammatic representations to them. A considerable amount of work has been done to develop metamodelling tools ensuring syntactical correctness of models created with DSMLs. However, little has been published about the challenges of developing a graphical model editor for meta-modelling tools. Specifying how conceptual elements of a DSML are to be represented graphically is often cumbersome. Moreover, tools are sometimes too inflexible to handle advanced features beyond displaying static icons. Furthermore, graphical representations must be kept consistent in case of reuse in multiple, potentially integrated DSMLs. This paper’s aim is to carve out a set of requirements for graphical model editors as used in meta-modelling tools. We present a conceptual model considering these requirements. Furthermore, we discuss an exemplary software implementation of a model editor.

Guidelines for reporting productivity studies – a review of the reproducibility of data envelopment analysis in the service sector

With respect to the scientific method, reproducing empirical research is a necessary step to develop generalisable knowledge. However, in service productivity measurement and managerial science in general, little attention seems to be paid to replicating empirical studies. One reason could be that many studies do not report their data and methodology in the degree of detail sufficient to reproduce them. In this paper, we provide evidence for a lack of reproducibility of empirical studies on service productivity measurement with data envelopment analysis (DEA), based on reviewing service productivity measurement literature. The major impediments identified comprise a superficial description of the measurement process and data sources, insufficient details on the retrieval methodology, and an incomplete account of the extraction methodology. To improve the reproducibility of future studies, this article offers a structured set of guidelines with which authors can report their studies more comprehensively.

Effizienzmessung industrieller Dienstleistungen mittels Data Envelopment Analysis (ServDEA)

Industrielle Dienstleistungen spielen heute eine wichtigere Rolle denn je zur Erschliesung neuer Ertragspotenziale bei steigendem Wettbewerbsdruck fur Industrieguterhersteller. Die Erbringung derartiger Dienstleistungen wird dabei in der industriellen Praxis nur unzureichenden Effizienzbeobachtungen unterzogen. Gefordert ist daher ein ganzheitlicher Ansatz zur Messung von Produktivitat von Dienstleistungen. Dieser Beitrag widmet sich dieser Problematik und stellt die Methodik der Data Envelopment Analysis (DEA) als Ansatz zur Effizienzmessung industrieller Dienstleistungen vor. Dabei leiten die Autoren auf Basis der Literatur ein integriertes Dienstleistungsproduktivitatskonzept ab, welches den Handlungsrahmen fur die Anwendung der DEA vorgibt. Mit der Vorstellung verschiedener DEA-Varianten werden Moglichkeiten und Potenziale der Methode aufgezeigt. Der besondere Mehrwert des Beitrags liegt in der detaillierten Dokumentation der DEA-Anwendung anhand eines Praxisfalls. Diese Anwendung umfasst sowohl die Ableitung von relevanten Input- und Outputfaktoren sowie die Ergebnisdarstellung auf unterschiedlichen Betrachtungsebenen. Daraus lassen sich verschiedene Praxisimplikationen ableiten. Der Beitrag schliest mit einer kritischen Wurdigung der Potenziale und Herausforderungen der DEA-Anwendung, die sich im Verlauf des Beitrags gezeigt haben.

A Framework for Fast Graph-Based Pattern Matching in Conceptual Models

We introduce a pattern matching approach for conceptual models suitable for a number of model analysis scenarios like process weakness detection, process compliance checking, syntax verification and model translation. The approach does not depend on any particular modeling language which is achieved by treating conceptual models as labeled graphs. Consequently, we use pattern matching techniques known from algorithmic graph theory - sub graph isomorphism and sub graph homeomorphism. In general, algorithms solving these problems can be computationally expensive. However, special properties of conceptual models such as low tree width and planarity can be exploited to keep computational complexity manageable. This makes pattern matching applicable even to large models typically used in large companies or corporate groups. We introduce a high-level meta algorithm checking structural properties of input models and patterns to decide which low-level pattern matching algorithm will likely deliver search results quickest.