Improving the Usability of OCL as an Ad-hoc Model Querying Language

Abstract. The OCL is often perceived as difficult to learn and use. In previous research, we have defined experimental query languages exhibiting higher levels of usability than OCL. However, none of these alternatives can rival OCL in terms of adoption and support. In an attempt to leverage the lessons learned from our research and make it accessible to the OCL community, we propose the OCL Query API (OQAPI), a library of query-predicates to improve the user-friendliness of OCL for ad-hoc querying. The usability of OQAPI is studied using controlled experiments. We find considerable evidence to support our claim that OQAPI facilitates user querying using OCL.

Supporting Information Systems Analysis Through Conceptual Model Query – The Diagramed Model Query Language (DMQL)

Analyzing conceptual models such as process models, data models, or organizational charts is useful for several purposes in information systems engineering (e.g., for business process improvement, compliance management, model driven software development, and software alignment). To analyze conceptual models structurally and semantically, so-called model query languages have been put forth. Model query languages take a model pattern and conceptual models as input and return all subsections of the models that match this pattern. Existing model query languages typically focus on a single modeling language and/or application area (such as analysis of execution semantics of process models), are restricted in their expressive power of representing model structures, and/or abstain from graphical pattern specification. Because these restrictions may hamper query languages from propagating into practice, we close this gap by proposing a modeling language-spanning structural model query language based on flexible graph search that, hence, provides high structural expressive power. To address ease-of-use, it allows one to specify model queries using a diagram. In this paper, we present the syntax and the semantics of the diagramed model query language (DMQL), a corresponding search algorithm, an implementation as a modeling tool prototype, and a performance evaluation

A cognitive exploration of the “non-visual” nature of geometric proofs

Why are Geometric Proofs (Usually) “Non-Visual”? We asked this question as a way to explore the similarities and differences between diagrams and text (visual thinking versus language thinking). Traditional text-based proofs are considered (by many to be) more rigorous than diagrams alone. In this paper we focus on human perceptual-cognitive characteristics that may encourage textual modes for proofs because of the ergonomic affordances of text relative to diagrams. We suggest that visual-spatial perception of physical objects, where an object is perceived with greater acuity through foveal vision rather than peripheral vision, is similar to attention navigating a conceptual visual-spatial structure. We suggest that attention has foveal-like and peripheral-like characteristics and that textual modes appeal to what we refer to here as foveal-focal attention, an extension of prior work in focused attention

Ontology-based end-user visual query formulation: Why, what, who, how, and which?

Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users’ technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users’ understanding. However, on the one hand, today the most of world’s enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity—known as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems

Model-Driven Engineering in the Large: Refactoring Techniques for Models and Model Transformation Systems

Model-Driven Engineering (MDE) is a software engineering paradigm that aims to increase the productivity of developers by raising the abstraction level of software development. It envisions the use of models as key artifacts during design, implementation and deployment. From the recent arrival of MDE in large-scale industrial software development – a trend we refer to as MDE in the large –, a set of challenges emerges: First, models are now developed at distributed locations, by teams of teams. In such highly collaborative settings, the presence of large monolithic models gives rise to certain issues, such as their proneness to editing conflicts. Second, in large-scale system development, models are created using various domain-specific modeling languages. Combining these models in a disciplined manner calls for adequate modularization mechanisms. Third, the development of models is handled systematically by expressing the involved operations using model transformation rules. Such rules are often created by cloning, a practice related to performance and maintainability issues. In this thesis, we contribute three refactoring techniques, each aiming to tackle one of these challenges. First, we propose a technique to split a large monolithic model into a set of sub-models. The aim of this technique is to enable a separation of concerns within models, promoting a concern-based collaboration style: Collaborators operate on the submodels relevant for their task at hand. Second, we suggest a technique to encapsulate model components by introducing modular interfaces in a set of related models. The goal of this technique is to establish modularity in these models. Third, we introduce a refactoring to merge a set of model transformation rules exhibiting a high degree of similarity. The aim of this technique is to improve maintainability and performance by eliminating the drawbacks associated with cloning. The refactoring creates variability-based rules, a novel type of rule allowing to capture variability by using annotations. The refactoring techniques contributed in this work help to reduce the manual effort during the refactoring of models and transformation rules to a large extent. As indicated in a series of realistic case studies, the output produced by the techniques is comparable or, in the case of transformation rules, partly even preferable to the result of manual refactoring, yielding a promising outlook on the applicability in real-world settings