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Auflistung EMISAJ Vol. 15 - 2020 nach Erscheinungsdatum
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- ZeitschriftenartikelCatchword: Blockchains and Enterprise Modeling(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 16, 2020) Fill, Hans-Georg; Fettke, Peter; Rinderle-Ma, StefanieIn this catchword article we describe the current technological opportunities that are available through blockchain technologies and outline how the field of enterprise modeling can contribute to these developments as well as benefit itself from them. For this purpose, we discuss the technical foundations of blockchains and derive a framework for relating both sides. Finally, it is reported about recent approaches that already engage in these opportunities.
- ZeitschriftenartikelSatisfying Four Requirements for More Flexible Modeling Methods: Theory and Test Case(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 3, 2020) Bork, Dominik; Alter, StevenRecent research in conceptual modeling and enterprise modeling calls for relaxing common assumptions about the nature of modeling methods and related modeling languages and metamodels. This paper pursues that goal by proposing a new vision of modeling methods that overcomes some of the limitations identified in the literature by satisfying four requirements for more flexible modeling methods. That vision builds upon the integration of multiple modeling techniques that are related to an overarching metaphor. Those techniques may address heterogeneous purposes such as specifying a system’s capabilities or specifying which resources are used by specific activities. This paper presents design characteristics and metamodel design options to guide method engineers in adopting this broader notion of modeling methods, integrating multiple modeling techniques, and using appropriate modeling languages. To demonstrate feasibility, an extended version of the work system method (WSM) is presented in the form of a Work System Modeling Method (WSMM) that encompasses seven purposes of modeling that call for successively more formal approaches. A final section summarizes how WSMM addresses the issues and requirements from the introduction, explains how coherence is maintained within WSMM, and identifies areas for future research, with emphasis on ways to make WSMM and similar modeling methods as valuable as possible.
- ZeitschriftenartikelInformatics as a Science(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 6, 2020) Reisig, WolfgangThis contribution addresses the quest for a framework for a comprehensive science of informatics as a formal theory of discrete dynamic systems, in analogy to the model of natural sciences. A variety of examples show that this endeavor is promising indeed, and that (detached) parts of it exist already. In the long run, informatics may evolve as a self-contained science, more comprehensive than nowadays Computer Science, by complementing its strong technological aspects with a consistent theoretical, mathematical basis, on an equal footing with natural sciences.
- ZeitschriftenartikelA Taxonomy of Business Rule Organizing Approaches in Regard to Business Process Compliance(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 4, 2020) Corea, Carl; Delfmann, PatrickBusiness Process Compliance (BPC) bridges the disciplines of Business Process Management and Compliance Management, and is aimed to ensure that business processes are aligned with laws and regulations. In this context, business rules are used as a central means to represent regulatory policies and consequently to (automatedly) verify, whether business process models abide by respective rules. While there has been a plethora of works regarding this actual verification of process models relative to business rules, we see a strong lack of works regarding the actual creation and maintenance of business rules. More precisely, many works assume sound sets of business rules as a basis for subsequent techniques. However, recent works suggest this assumption cannot be made in practice, and companies actually need to be supported in the scope of managing and organizing business rules, e.g., to remove redundant or contradictory rules. Organizing business rules is a mandatory prerequisite to BPC, as errors in business rules make these rule bases unusable and impede a subsequent verification of process compliance. However, the literature on business rule organization is sparse - especially its relation to BPC. We therefore investigate how to harmonize company efforts in business rule organization and BPC by the means of a systematic literature review. The main contribution of this work is a guideline which supports companies to select appropriate rule organization approaches based on company BPC needs. Also, we identify research gaps and propose a corresponding research agenda based on our findings.
- ZeitschriftenartikelDPMF: A Modeling Framework for Data Protection by Design(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 10, 2020) Sion, Laurens; Dewitte, Pierre; Van Landuyt, Dimitri; Wuyts, Kim; Valcke, Peggy; Joosen, WouterBuilding software-intensive systems that respect the fundamental rights to privacy and data protection requires explicitly addressing data protection issues at the early development stages. Data Protection by Design (DPbD)—as coined by Article 25(1) of the General Data Protection Regulation (GDPR)—therefore calls for an iterative approach based on (i) the notion of risk to data subjects, (ii) a close collaboration between the involved stakeholders and (iii) accountable decision-making. In practice, however, the legal reasoning behind DPbD is often conducted on the basis of informal system descriptions that lack systematicity and reproducibility. This affects the quality of Data Protection Impact Assessments (DPIA)—i.e. the concrete manifestation of DPbD at the organizational level. This is a major stumbling block when it comes to conducting a comprehensive and durable assessment of the risks that takes both the legal and technical complexities into account. In this article, we present DPMF, a data protection modeling framework that allows for a comprehensive and accurate description of the data processing operations in terms of the key concepts used in the GDPR. The proposed modeling approach supports the automation of a number of legal reasonings and compliance assessments (e.g., purpose compatibility) that are commonly addressed in a DPIA exercise and this support is strongly rooted upon the system description models. The DPMF is supported in a prototype modeling tool and its practical applicability is validated in the context of a realistic e-health system for a number of complementary development scenarios.
- Journal EditorialTowards Privacy Preservation and Data Protection in Information System Design(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 7, 2020) Koschmider, Agnes; Michael, Judith; Baracaldo, NathalieThis paper serves as an editorial to the corresponding special issue setting out solutions and future directions of privacy preservation and data protection in information system design.
- ZeitschriftenartikelTrust and Privacy in Process Analytics(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 8, 2020) Mannhardt, Felix; Koschmider, Agnes; Biermann, Lars; Lange, Jana; Tschorsch, Florian; Wynn, Moe ThandarThis paper summarizes the panel discussion at the 1st Workshop on Trust and Privacy in Process Analytics (TPPA) co-located with the 2nd International Conference on Process Mining. The panel discussed to what extend trust and privacy is embedded in applications of process mining and took place on 5th October 2020. The virtual session was chaired by Felix Mannhardt and Agnes Koschmider and the invited panelists were Moe Wynn, Jana Lange, Lars Biermann and Florian Tschorsch. The major challenges that this panel identified related to privacy-preserving process mining are to include (user-centric) privacy filters, understanding the privacy-utility trade-off and to link privacy-preserving techniques with dataset quality.
- ZeitschriftenartikelWorkflow Management on BFT Blockchains(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 14, 2020) Evermann, Joerg; Kim, HenryBlockchains have been proposed as infrastructure technology for a wide variety of applications. They provide an immutable record of transactions, making them useful when business actors do not trust each other, and their distributed nature makes them suitable for inter-organizational applications. However, widely-used proof-of-work based blockchains are computationally inefficient and do not provide final consensus, although they scale well to large networks. In contrast, blockchains built around Byzantine Fault Tolerance (BFT) consensus algorithms are more efficient and provide immediate and final consensus, but do not scale well to large networks. We argue that this makes them well-suited for workflow management applications, which typically include no more than a few dozen participants. This paper is motivated by a use case in the resource extraction industry. We develop an architecture for a BFT blockchain based workflow management system (WfMS) and present a prototype implementation. We discuss its advantages and limitations with respect to proof-of-work based systems and provide an outlook to future research.
- ZeitschriftenartikelMachine Learning and Complex Event Processing(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 1, 2020) Wanner, Jonas; Wissuchek, Christopher; Janiesch, ChristianIn the Industrial Internet of Things, cyber-physical systems bridge the gap between the physical and digital world by connecting advanced manufacturing systems with digital services in so-called smart factories. This interplay generates a large amount of data. By analyzing the data, manufacturers can reap many benefits and optimize their operations. Here, the value of information is at its highest with low latency to its emergence and its value decreases over time. Complex Event Processing (CEP) is a technology, which enables real-time analysis of complex events (i.e., combined data values from different sources). In this way, CEP assists in the identification and localization of anomalous process sequences in smart factories. However, CEP comes with limitations that reduce its effectiveness. Setting up CEP requires in-depth domain knowledge and is primarily declarative as well as reactive by nature. Combining CEP with machine learning (ML) is a possible extension to circumvent these technological limitations. However, there is no up-to-date overview on the integration of both paradigms in research and no review of their transferability for application in smart factories. In this article, we provide (1) a synthesis of research on the integration of CEP and ML identifying supervised learning as the predominant approach, and (2) a transfer of potentials for the use in smart factories. Here, reactive and proactive policies are used in equal frequency.
- ZeitschriftenartikelPersonal data management inside and out(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 9, 2020) Labadie, Clément; Legner, ChristinePersonal data is increasingly positioned as a valuable asset. While individuals generate and expose ever-expanding volumes of personal information online, certain tech companies have built their business models on the personal data they gather. In this context, lawmakers are revising data protection regulations in order to provide individuals with enhanced rights and set new rules regarding the way corporations collect, manage, and share personal information. We argue that recent data protection regulatory frameworks such as the European Union’s General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA) are fundamentally about data management. Yet, there have been no attempts to analyze the regulations in terms of their implications on the data life cycle. In this paper, we systematically analyze the GDPR and the CCPA, and identify their implications on the data life cycle. To synthesize our findings, we propose a semi-formal notation of the resulting changes on the personal data life cycle, in the form of a process and data model governed by business rules, consolidated in a reference personal data life cycle model for data protection. To the best of our knowledge, this study represents one of the first attempts to provide a data-centric view on data protection regulatory requirements.