Model-based Analysis of Data Inaccuracy Awareness in Business Processes

Problem definition: Data errors in business processes can be a source for exceptions and hamper business outcomes. Relevance: The paper proposes a method for analyzing data inaccuracy issues already at process design time, in order to support process designers by identifying process parts where data errors might remain unrecognized, so decisions could be taken based on inaccurate data. Methodology: The paper follows design science, developing a method as an artifact. The conceptual basis is the notion of data inaccuracy awareness – the ability to tell whether potential discrepancies between real and IS values may exist. Results: The method was implemented on top of a Petri net modeling tool and validated in a case study performed in a large manufacturing company of safety–critical systems. Managerial implications: Anticipating consequences of data inaccuracy already during process design can help avoiding them at runtime

Non-deterministic Multi-valued Matrices for First-Order Logics of Formal Inconsistency

Paraconsistent logic is the study of contradictory yet non-trivial theories. One of the best-known approaches to designing useful paraconsistent logics is da Costa’s ap-proach, which has led to the family of Logics of Formal Inconsistency (LFIs), where the notion of inconsistency is expressed at the object level. In this paper we use non-deterministic matrices, a generalization of standard multi-valued matrices, to provide simple and modular finite-valued semantics for a large family of first-order LFIs. The modular approach provides new insights into the semantic role of each of the studied axioms and the dependencies be-tween them. We also prove the effectiveness of our seman-tics, a crucial property for constructing counterexamples, and apply it to show a non-trivial proof-theoretical prop-erty of the studied LFIs. 1

What practitioners really want:requirements for visual notations in conceptual modeling

This research was aimed at eliciting the requirements of practitioners who use conceptual modeling in their professional work for the visual notations of modeling languages. While the use of conceptual modeling in practice has been addressed, what practitioners in fact require of the visual notation of the modeling languages they use has received little attention. This work was thus motivated by the need to understand to what extent practitioners’ requirements are acknowledged and accommodated by visual notation research efforts. A mixed-method study was conducted, with a survey being offered over the course of several months to LinkedIn professional groups. The requirements included in the survey were based on a leading design theory for visual notations, the Physics of Notations. After preprocessing, 104 participant responses were analyzed. Data analysis included descriptive coding and qualitative analysis of purposes for modeling and additional requirements beyond the scope of visual design. Statistical and factorial analysis was used to explore potential correlations between the importance of different requirements as perceived by practitioners and the demographic factors (e.g., domain, purpose, topics). The results indicate several correlations between demographic factors and the perceived importance of visual notation requirements, as well as differences in the perceived relative importance of different requirements for models used to communicate with modeling experts as compared to non-experts. Furthermore, the results show an evolution from trends identified in studies conducted in the previous decade. The identified correlations with practitioners’ demographics reveal several research challenges that should be addressed, as well as the potential benefits of more purpose-specific tailoring of visual notation design. Furthermore, the shift in practitioner demographics as compared to those found in earlier work indicates that the research and development of conceptual modeling efforts needs to stay up-to-date with the way practitioners employ conceptual modeling