http://dl.gi.de/handle/20.500.12116/33872 2026-07-22T22:08:51Z 2026-07-22T22:08:51Z Vavpoti?, Damjan Robnik-Šikonja, Marko Hovelja, Tomaž http://dl.gi.de/handle/20.500.12116/33880 2020-09-02T07:50:42Z 2020-01-01T00:00:00Z

Exploring the Relations Between Net Benefits of IT Projects and CIOs' Perception of Quality of Software Development Disciplines Vavpoti?, Damjan; Robnik-Šikonja, Marko; Hovelja, Tomaž Software development enterprises are under consistent pressure to improve their management techniques and development processes. These are comprised of several software development methodology (SDM) disciplines such as requirements acquisition, design, coding, testing, etc. that must be continuously improved and individually tailored to suit specific software development projects. The paper proposes a methodology that enables the identification of SDM discipline quality categories and the evaluation of SDM disciplines' net benefits. It advances the evaluation of software process quality from single quality category evaluation to multiple quality categories evaluation as proposed by the Kano model. An exploratory study was conducted to test the proposed methodology. The exploratory study results show that different types of Kano quality are present in individual SDM disciplines and that applications of individual SDM disciplines vary considerably in their relation to net benefits of IT projects. Consequently, software process quality evaluation models should start evaluating multiple categories of quality instead of just one and should not assume that the application of every individual SDM discipline has the same effect on the enterprise's net benefits.

2020-01-01T00:00:00Z Miehle, Daniel Häckel, Björn Pfosser, Stefan Übelhör, Jochen http://dl.gi.de/handle/20.500.12116/33882 2020-09-02T07:50:42Z 2020-01-01T00:00:00Z

Modeling IT Availability Risks in Smart Factories Miehle, Daniel; Häckel, Björn; Pfosser, Stefan; Übelhör, Jochen In the course of the ongoing digitalization of production, production environments have become increasingly intertwined with information and communication technology. As a consequence, physical production processes depend more and more on the availability of information networks. Threats such as attacks and errors can compromise the components of information networks. Due to the numerous interconnections, these threats can cause cascading failures and even cause entire smart factories to fail due to propagation effects. The resulting complex dependencies between physical production processes and information network components in smart factories complicate the detection and analysis of threats. Based on generalized stochastic Petri nets, the paper presents an approach that enables the modeling, simulation, and analysis of threats in information networks in the area of connected production environments. Different worst-case threat scenarios regarding their impact on the operational capability of a close-to-reality information network are investigated to demonstrate the feasibility and usability of the approach. Furthermore, expert interviews with an academic Petri net expert and two global leading companies from the automation and packaging industry complement the evaluation from a practical perspective. The results indicate that the developed artifact offers a promising approach to better analyze and understand availability risks, cascading failures, and propagation effects in information networks in connected production environments.

2020-01-01T00:00:00Z Voglhofer, Thomas Rinderle-Ma, Stefanie http://dl.gi.de/handle/20.500.12116/33879 2020-09-02T07:50:42Z 2020-01-01T00:00:00Z

Collection and Elicitation of Business Process Compliance Patterns with Focus on Data Aspects Voglhofer, Thomas; Rinderle-Ma, Stefanie Business process compliance is one of the prevalent challenges for companies. Despite an abundance of research proposals, companies still struggle with manual compliance checks and the understanding of compliance violations in the light of missing root-cause explanations. Moreover, approaches have merely focused on the control flow perspective in compliance checking, neglecting other aspects such as the data perspective. This paper aims at analyzing the gap between existing academic work and compliance demands from practice with a focus on the data aspects. The latter emerges from a small set of regulatory documents from different domains. Patterns are assumed as the right level of abstraction for compliance specification due to their independence of (technical) implementation in (process-aware) information systems, potential for reuse, and understandability. A systematic literature review collects and assesses existing compliance patterns. A first analysis of ten regulatory documents from different domains specifically reveals data-oriented compliance constraints that are not yet reflected by existing compliance patterns. Accordingly, data-related compliance patterns are specified.

2020-01-01T00:00:00Z Zelst, Sebastiaan J. Buijs, Joos C. A. M. Vázquez-Barreiros, Borja Lama, Manuel Mucientes, Manuel http://dl.gi.de/handle/20.500.12116/33878 2020-09-02T07:50:42Z 2020-01-01T00:00:00Z

Repairing Alignments of Process Models Zelst, Sebastiaan J.; Buijs, Joos C. A. M.; Vázquez-Barreiros, Borja; Lama, Manuel; Mucientes, Manuel Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds.

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