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

Reusing Model Transformations across Heterogeneous Metamodels

Model transformations are key enablers for multi-paradigm modeling. However, currently there is little support for reusing transformations in different contexts since they are tightly coupled to the metamodels they are defined upon, and hence reusing them for other metamodels becomes challenging. Inspired from generic programming, we proposed generic model-to-model transformations, which are defined over so-called metamodel concepts, which are later bound to specific metamodels. Nevertheless, the current binding mechanism lacks automated resolution support for recurring structural heterogeneities between metamodels. Therefore, based on a systematic classification of heterogeneities, we propose a flexible binding mechanism being able to automatically resolve recurring structural heterogeneities between metamodels. For this, the binding model is analyzed and required adaptors are automatically added to the transformation

Herramienta web para interoperabilidad conceptual entre UML, EER y ORM 2

La presente investigación se desarrolla mediante el trabajo colaborativo de docentes investigadores de la Universidad Nacional del Comahue (UNCo) y de la Universidad Nacional del Sur (UNS), en el contexto de proyectos de investigación financiados por las universidades indicadas. El objetivo general de esta línea de investigación y desarrollo es el diseño e implementación de una herramienta Web que permita facilitar la interoperabilidad entre los lenguajes de modelado UML, EER y ORM 2. Para esto, se considera un metamodelo integrador, llamado KF, el cual formaliza las bases de los distintos lenguajes, permitiendo así, identificar las similitudes entre los lenguajes previamente mencionados. El resultado de esta implementación será integrado a crowd, la cual es una herramienta para modelado visual ontológico utilizando lenguajes de modelado conceptual, desarrollada por nuestros grupos de investigación.Eje: Innovación en sistemas de software.Red de Universidades con Carreras en Informátic

A graph-based meta-model for heterogeneous data management

The wave of interest in data-centric applications has spawned a high variety of data models, making it extremely difficult to evaluate, integrate or access them in a uniform way. Moreover, many recent models are too specific to allow immediate comparison with the others and do not easily support incremental model design. In this paper, we introduce GSMM, a meta-model based on the use of a generic graph that can be instantiated to a concrete data model by simply providing values for a restricted set of parameters and some high-level constraints, themselves represented as graphs. In GSMM, the concept of data schema is replaced by that of constraint, which allows the designer to impose structural restrictions on data in a very flexible way. GSMM includes GSL, a graph-based language for expressing queries and constraints that besides being applicable to data represented in GSMM, in principle, can be specialised and used for existing models where no language was defined. We show some sample applications of GSMM for deriving and comparing classical data models like the relational model, plain XML data, XML Schema, and time-varying semistructured data. We also show how GSMM can represent more recent modelling proposals: the triple stores, the BigTable model and Neo4j, a graph-based model for NoSQL data. A prototype showing the potential of the approach is also described

On the Automated Transformation of Domain Models into Tabular Datasets

We are surrounded by ubiquitous and interconnected soft- ware systems which gather valuable data. The analysis of these data, although highly relevant for decision making, cannot be performed di- rectly by business users, as its execution requires very speci c technical knowledge in areas such as statistics and data mining. One of the com- plexity problems faced when constructing an analysis of this kind resides in the fact that most data mining tools and techniques work exclusively over tabular-formatted data, preventing business users from analysing excerpts of a data bundle which have not been previously traduced into this format by an expert. In response, this work presents a set of transfor- mation patterns for automatically generating tabular data from domain models. The described patterns have been integrated into a language, which allows business users to specify the elements of a domain model that should be considered for data analysis.This work has been partially funded by the Government of Cantabria (Spain) under the doctoral studentship program from the University of Cantabria, and by the Spanish Government under grant TIN2014- 56158-C4-2-P (M2C2)

Experimentally motivated transformations for intermodel links between conceptual models

Complex system development and information integration at the conceptual layer raises the requirement to be able to declare intermodel assertions between entities in models that may, or may not, be represented in the same modelling language. This is compounded by the fact that semantically equivalent notions may have been represented with a different element, such as an attribute or class. We first investigate such occurrences in six ICOM projects and 40 models with 33 schema matchings. While equivalence and subsumption are in the overwhelming majority, this extends mainly to different types of attributes, and therewith requiring non-1:1 mappings. We present a solution that bridges these semantic gaps. To facilitate implementation, the mappings and transformations are declared in ATL. This avails of a common, and logic-based, metamodel to aid verification of the links. This is currently being implemented as proof-of-concept in the ICOM tool

Conceptual Model Interoperability: a Metamodel-driven Approach

Linking, integrating, or converting conceptual data models represented in different modelling languages is a common aspect in the design and maintenance of complex information systems. While such languages seem similar, they are known to be distinct and no unifying framework exists that respects all of their language features in either model transformations or inter-model assertions to relate them. We aim to address this issue using an approach where the rules are enhanced with a logic-based metamodel. We present the main approach and some essential metamodel-driven rules for the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2. The transformations for model elements and patterns are used with the metamodel to verify correctness of inter-model assertions across models in different languages

Incremental schema integration for data wrangling via knowledge graphs

Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present NextiaDI, a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.This work was partly supported by the DOGO4ML project, funded by the Spanish Ministerio de Ciencia e Innovación under project PID2020-117191RB-I00, and D3M project, funded by the Spanish Agencia Estatal de Investigación (AEI) under project PDC2021-121195-I00. Javier Flores is supported by contract 2020-DI-027 of the Industrial Doctorate Program of the Government of Catalonia and Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico). Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovación, as well as the European Union – NextGenerationEU, under project FJC2020-045809-I.Peer ReviewedPostprint (published version