Proactive Quality Guidance for Model Evolution in Model Libraries

Model evolution in model libraries differs from general model evolution. It limits the scope to the manageable and allows to develop clear concepts, approaches, solutions, and methodologies. Looking at model quality in evolving model libraries, we focus on quality concerns related to reusability. In this paper, we put forward our proactive quality guidance approach for model evolution in model libraries. It uses an editing-time assessment linked to a lightweight quality model, corresponding metrics, and simplified reviews. All of which help to guide model evolution by means of quality gates fostering model reusability.Comment: 10 pages, figures. Appears in Models and Evolution Workshop Proceedings of the ACM/IEEE 16th International Conference on Model Driven Engineering Languages and Systems, Miami, Florida (USA), September 30, 201

[RETRACTED ARTICLE] Complexity theory and the historical study of religion: navigating the transdisciplinary space between the Humanities and the Natural Sciences

This article advocates for a set of recent transdisciplinary options for the History of Religion, combining methods from the Natural and Human Sciences, through a special focus on the study of so-called “complex systems”. We elucidate their theoretical bases and limitations while assuming a pragmatic positioning between a defense of the historical-scientific study of religion and the promotion of groundbreaking methodological outlooks emerging from the Digital Humanities. From this background, throughout the text, we argue for a complementation of historiographical “close reading” with both “distant reading” techniques and interdisciplinary research, using computer-based methods and a diversity of formal modeling techniques. In short, we conclude that such methods offer novel ways for data representation and are best understood not only as creative schemes for solving issues in historiography, but also as a springboard for new inquiries arising from the transdisciplinarity between the Humanities and the Natural Sciences

GAMETH A Process Modeling Approach to Identify and Locate Crucial Knowledge.

In a knowledge management initiative, one of the main issues is to identify and locate which knowledge to capitalize on. To deal with this issue, a General Analysis Methodology so called GAMETH® has been developed. In this article, we describe the postulates, the guiding principles, and the main phases, which constitute the basis of GAMETH® Framework. Notably, we emphasize the process modeling approach that is inherent to the second phase of the methodology. This process modeling approach supports the effective capability to locate and identify “crucial knowledge”. Furthermore, we present lessons learned from two case studies.Process modeling; Knowledge Management (KM); GAMETH; Identifying and Locating Company’s Crucial Knowledge; Crucial knowledge;

Towards defining semantic foundations for purpose-based privacy policies

We define a semantic model for purpose, based on which purpose-based privacy policies can be meaningfully expressed and enforced in a business system. The model is based on the intuition that the purpose of an action is determined by its situation among other inter-related actions. Actions and their relationships can be modeled in the form of an action graph which is based on the business processes in a system. Accordingly, a modal logic and the corresponding model checking algorithm are developed for formal expression of purpose-based policies and verifying whether a particular system complies with them. It is also shown through various examples, how various typical purpose-based policies as well as some new policy types can be expressed and checked using our model

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

The value of ontology, The BPM ontology

It is generally accepted that the creation of added value requires collaboration inside and between organizations. Collaboration requires sharing knowledge (e.g., a shared understanding of business processes) between trading partners and between colleagues. It is on the (unique) knowledge that is shared between and created by colleagues that organizations build their competitive advantage. To take full advantage of this knowledge, it should be disseminated as widely as possible within an organization. Nonaka distinguished tacit knowledge, which is personal, context specific, and not so easy to communicate (e.g., intuitions, unarticulated mental models, embodied technological skills), from explicit knowledge, which is meaningful information articulated in clear language, including numbers and diagrams. Tacit knowledge can be disseminated through socialization (e.g., face-to-face communication, sharing experiences), which implies a reduced dissemination speed, or can be externalized , which is the conversion of tacit into explicit knowledge. Although explicit knowledge can take many forms (e.g., business (process) models, manuals), this chapter focuses on ontologies, which are versatile knowledge artifacts created through externalization, with the power to fuel Nonaka’s knowledge spiral. Nonaka’s knowledge spiral visualizes how a body of unique corporate knowledge, and hence a competitive advantage, is developed through a collaborative and iterative knowledge creation process that involves iterative cycles of externalization, combination, and internalization. When corporate knowledge is documented with ontology, a knowledge spiral leads to ontology evolution

Challenges and Directions in Formalizing the Semantics of Modeling Languages

Developing software from models is a growing practice and there exist many model-based tools (e.g., editors, interpreters, debuggers, and simulators) for supporting model-driven engineering. Even though these tools facilitate the automation of software engineering tasks and activities, such tools are typically engineered manually. However, many of these tools have a common semantic foundation centered around an underlying modeling language, which would make it possible to automate their development if the modeling language specification were formalized. Even though there has been much work in formalizing programming languages, with many successful tools constructed using such formalisms, there has been little work in formalizing modeling languages for the purpose of automation. This paper discusses possible semantics-based approaches for the formalization of modeling languages and describes how this formalism may be used to automate the construction of modeling tools