Conceptual fit: A criterion for COTS selection

COTS systems selection consists in evaluating the user requirements with respect to characteristics of candidate systems, using a set of criteria. One criterion that has received little attention is what we call conceptual fit. The criterion assesses the fit between the conceptual structure of the user requirements and that of a system. We evaluate the fit in terms of the existing misfits. We formally define the notion of conceptual misfit and we present a method that determines the conceptual misfits between the user requirements and a set of candidate systems. The method consists in defining a superschema, the mapping of the conceptual schemas of the candidate systems and of the user requirements to that superschema, and the automatic computation of the existing conceptual misfits. The method has been formalized in UML/OCL. We have conducted an exploratory experiment with the aim of evaluating the feasibility, difficulty and usefulness of the method, with positive results. We believe that the conceptual fit criterion could be taken into account by almost all existing COTS selection methods.Preprin

Simplification of UML/OCL schemas for efficient reasoning

Ensuring the correctness of a conceptual schema is an essential task in order to avoid the propagation of errors during software development. The kind of reasoning required to perform such task is known to be exponential for UML class diagrams alone and even harder when considering OCL constraints. Motivated by this issue, we propose an innovative method aimed at removing constraints and other UML elements of the schema to obtain a simplified one that preserve the same reasoning outcomes. In this way, we can reason about the correctness of the initial artifact by reasoning on a simplified version of it. Thus, the efficiency of the reasoning process is significantly improved. In addition, since our method is independent from the reasoning engine used, any reasoning method may benefit from it.Peer ReviewedPostprint (author's final draft

Computing the Importance of Schema Elements Taking Into Account the Whole SCHEMA

Conceptual Schemas are one of the most important artifacts in the development cycle of information systems. To understand the conceptual schema is essential to get involved in the information system that is described within it. As the information system increases its size and complexity, the relative conceptual schema will grow in the same proportion making di cult to understand the main concepts of that schema/information system. The thesis comprises the investigation of the in uence of the whole schema in computing the relevance of schema elements. It will include research and implementation of algorithms for scoring elements in the literature, an study of the di erent results obtained once applied to a few example conceptual schemas, an extension of those algorithms including new components in the computation process like derivation rules, constraints and the behavioural subschema speci cation, and an in-depth comparison among the initial algorithms and the extended ones studying the results in order to choose those algorithms that give the most valuable output

Extending the methods for computing the importance of entity types in large conceptual schemas

Visualizing and understanding large conceptual schemas requires the use of specific methods. These methods generate clustered, summarized, or focused schemas that are easier to visualize and understand. All of these methods require computing the importance of each entity type in the schema. In principle, the totality of knowledge defined in the schema could be relevant for the computation of that importance but, up to now, only a small part of that knowledge has been taken into account. In this paper, we extend seven existing methods for computing the importance of entity types by taking into account more relevant knowledge defined in the structural and behavioural parts of the schema. We experimentally evaluate the original and extended versions of these methods with three large real-world schemas. We present the two main conclusions we have drawn from the experiments.Postprint (published version

A filtering engine for large conceptual schemas

Postprint (published version

NOSQL design for analytical workloads: Variability matters

Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads. As result, co-relational alternatives, commonly known as NOSQL (Not Only SQL) databases, are extensively used for Big Data. As the primary focus of NOSQL is on performance, NOSQL databases are directly designed at the physical level, and consequently the resulting schema is tailored to the dataset and access patterns of the problem in hand. However, we believe that NOSQL design can also benefit from traditional design approaches. In this paper we present a method to design databases for analytical workloads. Starting from the conceptual model and adopting the classical 3-phase design used for relational databases, we propose a novel design method considering the new features brought by NOSQL and encompassing relational and co-relational design altogether.Peer ReviewedPostprint (author's final draft

Which game narratives do adolescents of different gameplay and sociodemographic backgrounds prefer? a mixed-methods analysis

OBJECTIVE: The aim of this study was to investigate which narrative elements of digital game narratives are preferred by the general adolescent population, and to examine associations with gender, socioeconomic status (SES), and gameplay frequency. Further, the study aims to discuss how results can be translated to serious digital games. MATERIALS AND METHODS: Adolescents were recruited through school to complete a survey on narrative preferences in digital games. The survey included questions on sociodemographic information, frequency of gameplay, and an open-ended question on what could be an appealing narrative for them. Data were analyzed in a mixed-methods approach, using thematic analysis and chi-square analyses to determine narrative preferences and the associations between game narrative elements and player characteristics (gender, SES, and frequency of gameplay). RESULTS: The sample consisted of 446 adolescents (12-15 years old) who described 30 narrative subthemes. Preferences included human characters as protagonists; nonhuman characters only as antagonists; realistic settings, such as public places or cities; and a strong conflict surrounding crime, catastrophe, or war. Girls more often than boys defined characters by their age, included avatars, located the narrative in private places, developed profession-related skills, and included a positive atmosphere. Adolescents of nonacademic education more often than adolescents of academic education defined characters by criminal actions. Infrequent players more often included human characters defined by their age than frequent players. After performing a Bonferroni correction, narrative preferences for several gender differences remained. CONCLUSION: Different narrative elements related to subgroups of adolescents by gender, SES, and frequency of gameplay. Customization of narratives in serious digital health games should be warranted for boys and girls; yet, further research is needed to specify how to address girls in particular

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models

Some shortcomings of long-term working memory

Within the framework of their long-term working memory theory, Ericsson and Kintsch (1995) propose that experts rapidly store information in long-term memory through two mechanisms: elaboration of long-term memory patterns and schemas and use of retrieval structures. They use chess players’ memory as one of their most compelling sources of empirical evidence. In this paper, I show that evidence from chess memory, far from supporting their theory, limits its generality. Evidence from other domains reviewed by Ericsson and Kintsch, such as medical expertise, is not as strong as claimed, and sometimes contradicts the theory outright. I argue that Ericsson and Kintsch’s concept of retrieval structure conflates three different types of memory structures that possess quite different properties. One of these types of structures—generic, general-purpose retrieval structures—has a narrower use than proposed by Ericsson and Kintsch: it applies only in domains where there is a conscious, deliberate intent by individuals to improve their memory. Other mechanisms, including specific retrieval structures, exist that permit a rapid encoding into long-term memory under other circumstances