The main information systems design techniques focus, almost exclusively, on the functional requirements of the system to be implemented. In its standard formulation, axiomatic design has such characteristics. However, in complex operational environments, this can lead to the identification of functionally valid solutions, but do not perfectly adhere to the system’s nonfunctional requirements. In specific operational contexts, particularly in information systems design, neglecting nonfunctional requirements has been identified as a major threat to projects, which can prevent their proper utilization throughout the design process. In this paper, we focus on nonfunctional requirements in axiomatic design, whose impact assessments can only be performed according to a heuristic basis, i.e., by expert judgment. However, the value assignment by experts can lead to a decisional indeterminacy or cognitive bias. To overcome these limitations, we propose the adoption of a methodological approach based on a reinterpretation of the information axiom of axiomatic design in terms of a multi-criteria decision problem. This approach allows the formal inclusion of nonfunctional requirements in the design process, which can be accomplished by setting them as evaluation attributes to achieve the robust design solution. In this paper, we propose an algorithm to evaluate alternative design solutions based on the information theory of entropy, which then comply with the nonfunctional requirements. We illustrate our approach by a case study, which implements the process of managing patients in home care and compare it with the mathematical-based analytic hierarchy process method proposed in the literature. According to our method, the robust solution is computed in just a single step saving significant computational cost with respect to the iterative-based analytic hierarchy process method. In this perspective, the proposed approach can support information systems designers in decision making as it allows to select the most suitable solution for the context in which it must operate.

Nowadays, information systems are evolving towards increasingly interconnected, smart, and self-adaptive models. This transformation has led to the representation of the systems themselves in terms of natural ecosystems. Similar to the natural environment, the virtual world can be threatened by specific forms of pollution, such as illegitimate access to the system, unwanted changes to data, and loss of information, which affect the only resource it possesses, i.e., data. In order to provide proactive protection of data integrity and confidentiality, in this paper we consider the well-known principles of privacy by design and privacy by default in the design phase of system development. To this end, we propose an approach based on axiomatic design, which allows us to implement these two principles through an appropriate reinterpretation of the information axiom, in terms of privacy impact assessment. We illustrate our approach by a case study, which implements the process of managing patients in home care. However, the proposed method can be applied to processing systems that provide services. The main result achieved is to select the most digitally sustainable design solution, i.e., the one that best prevents the threats mentioned above.

This work addresses the problem of relaxing spatial constraints for pictorial queries having null answers in geographical databases. It focuses on the polygon-polyline topological relationship and proposes a computational model which is based on the notions of Operator Conceptual Neighborhood (OCN) graph and the relative 16-intersection matrix. The former is addressed to represent the conceptual topological neighborhood between pairs of Symbolic Graphical Objects and indicates how spatial constraints can be relaxed. The nodes of the OCN graph are labeled with geo-operators that have been formalized and their semantics has been enriched in order to capture user query details. The latter is a 16- intersection matrix which provides additional information about the query with respect to the well-known 9-intersection matrix proposed in the literature. It has been conceived to identify, among the approximate answers, the one closer to the user needs. In particular, it allows us to quantify the difference among the user query and the configurations corresponding to the proposed approximate answers on the basis of the OCN graph. The main characteristics of our approach are highlighted through some query examples.

This paper presents SemSim^e, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSim^e is an enhancement of SemSim and, with respect to the latter, it uses a frequency approach for weighting the ontology, and refines both the user request and the digital resources with the addition of rating scores. Such scores are High, Medium, and Low, and in the user request indicate the preferences assigned by the user to each of the concepts representing the searching criteria, whereas in the annotation of the digital resources they represent the levels of quality associated with each concept in describing the resources. The SemSim^e has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as $SemSi{m}_{RV}$ .