Tiago P Sales

—Risk analysis is a complex and critical activity in various contexts, ranging from strategic planning to IT systems operation. Given its complexity, several Enterprise Architecture (EA) frameworks and modeling languages have been developed to help analysts in representing and analyzing risks. Yet, the notion of risk remains overloaded and conceptually unclear in most of them. In this paper, we investigate the real-world semantics underlying risk-related constructs in one of such approaches, namely ArchiMate's Risk and Security Overlay (RSO). We perform this investigation by means of ontological analysis to reveal semantic limitations in the overlay, such as ambiguity and missing constructs. Building on the results of this analysis, we propose a well-founded redesign of the risk modeling aspects of the RSO.

In this paper we review and discuss some recent attempts at ontological re-engineering of REA in the light of the UFO ontology and the OntoUML language, focusing in particular on different choices concerning the UFO notion of relator. We also take this as an opportunity to clarify and revise Guarino and Guizzardi's general theory of reification and truthmaking proposed in the past.

It is widely recognized that accurately identifying competitors is a challenge for many companies and entrepreneurs. It is one that they cannot escape from, as failing to do so is a recipe for problems. Amongst other factors, competitor identification is challenging because of the complex nature of the competitive relationships that arise in business environments. In this paper, we tackle this issue by means of an initial ontological analysis on competition grounded in the Unified Foun-dational Ontology (UFO). Our analysis, the first of its kind in the literature , allows us to explain why and when competition arises, as well as to distinguish between different three types of competitive relationships, namely direct, indirect and potential competition.

The construction of large-scale reference conceptual models is a com- plex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling languages and methodologies, patterns and anti-patterns and automated support environments. This paper proposes Semantic Anti-Patterns for ontology- driven conceptual modeling. These anti-patterns capture error prone modeling decisions that can result in the creation of models that allow for unintended mod- el instances (representing undesired state of affairs). The anti-patterns presented here have been empirically elicited through an approach of conceptual models validation via visual simulation. The paper also presents a tool that is able to: au- tomatically identify these anti-patterns in user’s models, provide visualization for its consequences, and generate corrections to these models by the automatic in- clusion of OCL constraints.

Given the increasing complexity of ontology-driven conceptual modeling and on-tology engineering, there is an urging need for developing a new generation of complexity management tools for these disciplines [12]. These include a number of methodological and computational tools that are grounded on sound ontological foundations. In particular, we should advance in these disciplines a well-tested body of knowledge in terms of Ontology Patterns, Ontology Pattern Languages and Ontological Anti-Patterns. This chapter focuses on the latter. An anti-pattern is a recurrent error-prone modeling decision [15]. In this paper , we are interested in one specific sort of anti-patterns, namely, model structures that, albeit producing syntactically valid conceptual models, are prone to result in unintended domain representations. In other words, we are interested in configurations that when used in a model will typically cause the set of valid (possible) instances of that model to differ from the set of instances representing intended state of affairs in that domain [11]. We name these configurations Anti-Patterns for Ontology-Driven Conceptual Modeling, or simply, Ontological Anti-Patterns. In this chapter, we focus on the study of Ontological Anti-Patterns in a particular conceptual modeling language named OntoUML [11]. OntoUML is an

—In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition, which ultimately defines what a company delivers to its customers. Despite their widely recognized importance, there is however little agreement on what exactly value propositions are. This lack of conceptual clarity harms the communication among stakeholders and the harmonization of current business strategy theories and strategy support frameworks. Furthermore , it hinders the development of systematic methodologies for crafting value propositions, as well as adequate support for representing and analyzing them. In this paper, we present an ontological analysis of value propositions based on a review of most relevant business and marketing theories and on previous work on value ascription, grounded in the Unified Foundational Ontology (UFO). Our investigation clarifies how value propositions are different from value presentations, and shows the difference between value propositions at the business level from those related to specific offerings.

Over the years, there has been an increasing adoption of ontology-driven conceptual models to represent the conceptual structure of critical domains in reality. Given the complexity of this task, there has been a growing demand for the development of proper engineering tools for supporting the design of these models. Despite a number of advances in this area, there is still a shortage of tools directed at novice and non-technical users that can, at the same time, address two competing requirements, namely: maintain modeling expressivity by being able to represent true ontological distinctions, while remaining intuitive and easy to learn by this class of users. In this article, we sketch a proposal in this direction by introducing the idea of an Ontology Model Canvas.

—In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition, which ultimately defines what a company delivers to its customers. Despite their widely recognized importance, there is however little agreement on what exactly value propositions are. This lack of conceptual clarity harms the communication among stakeholders and the harmonization of current business strategy theories and strategy support frameworks. Furthermore , it hinders the development of systematic methodologies for crafting value propositions, as well as adequate support for representing and analyzing them. In this paper, we present an ontological analysis of value propositions based on a review of most relevant business and marketing theories and on previous work on value ascription, grounded in the Unified Foundational Ontology (UFO). Our investigation clarifies how value propositions are different from value presentations, and shows the difference between value propositions at the business level from those related to specific offerings.

Enterprise information systems are increasingly being conceived as a combination of existing systems and to work as a part of an ecosystem of software products. This change demands methods and tools to deal with the challenging semantic interoperability issues. OntoUML is a well-founded modeling language that allows modelers to formalize world-views in a technologically neutral way, aiding in the solution of such interoperability challenges. In this paper, we present an overview of the OntoUML Lightweight Editor (OLED), our model-based environment to build, evaluate and implement OntoUML models, alongside with its main features and application scenarios.

The preparation of high quality emergency plans to guide operational decisions is an approach to mitigate the emergency management complexity. In such multidisciplinary scenario, teams with different perspectives need to collaborate towards a common goal and interact within a common understanding. In this scenario, the characterization of the variability of the elements involved in these plans is an important issue, which is addressed by the emergency plans generation methodology Document Product Line for Emergency Plans (DPL(EP)). To increase common understanding of plans, we propose an adaptation of this methodology by applying a well-founded emergency ontology, termed OntoEmergePlan, which supports the domain engineering phase. It is grounded in a foundational ontology, which ensures a higher consistency degree to the process of plans generation.

The construction of large-scale reference conceptual models is a complex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling languages and methodologies, patterns and anti-patterns and automated supporting environments. This paper proposes a set of Ontological Anti-Patterns for Ontology-Driven Conceptual Modeling. These anti-patterns capture error-prone modeling decisions that can result in the creation of models that fail to exclude unintended model instances (representing unintended state of affairs) or forbid intended ones (representing intended states of affairs). The anti-patterns presented here have been empirically elicited through an approach of conceptual models validation via visual simulation. The paper also presents a series of refactoring plans for rectifying the models in which these anti-patterns occur. In addition, we present here a computational tool that is able to: automatically identify these anti-patterns in user’s models, guide users in assessing their consequences, and generate corrections to these models by the automatic inclusion of OCL constraints implementing the proposed refactoring plans. Finally, the paper also presents an empirical study for assessing the harmfulness of each of the uncovered anti-patterns (i.e., the likelihood that its occurrence in a model entails unintended consequences) as well as the effectiveness of the proposed refactoring plans.