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The study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.

The problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.

An appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.

The authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.

The tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.

This work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.

This study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

The purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.

The paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.

The paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.

The paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

Presents a review on implementing finite element methods on supercomputers, workstations and PCs and gives main trends in hardware and software developments. An appendix included at the end of the paper presents a bibliography on the subjects retrospectively to 1985 and approximately 1,100 references are listed.

This study aims to investigate the influence of corporate social responsibility (CSR) on corporate sustainable performance (CSP) of small- and medium-sized enterprises (SMEs) by looking into the significance of mediating factors, namely, brand image (BI) and brand loyalty (BL), within the context of an emerging economy.

The authors conduct an extensive literature study on the subjects of CSR, BI and BL to assess their influence on the sustainable performance of SMEs in an emerging market. The study adopts a quantitative methodology. A total of 438 answers were obtained from a sample size of 513. The data of the SMEs in Vietnam was analyzed using the smart partial least squares structural equation modeling software, specifically version 3.3.2.

The results of the authors demonstrate notable and favorable correlations between CSR and CSP, CSR and BI and CSR and BL. Importantly, the findings contribute to existing knowledge by looking into the mediating influence of BI and BL in the relationship between CSR and CSP.

According to the authors’ understanding, a number of research have investigated the correlation between CSR and CSP within the realm of SMEs. Nevertheless, there is a scarcity of scholarly research examining the mediating function of BI and BL in this association. The study’s findings have important implications for entrepreneurs and senior management in effectively guiding their enterprises and improving their business strategies with an emphasis on sustainability in emerging markets. The outcome of this study has the potential to significantly contribute to SMEs in Vietnam as well as other emerging countries.

The goal of structural optimization is to find the best possible configuration that minimizes the objective function and satisfies a set of constraints. Here we present a method based on the evolutionary structural optimization method, where the quality of the solution is improved by avoiding the chain‐like sets of elements which are sources of potential kinematic instabilities, and by including local error estimators. Both of these enhancements are employed to activate refining the mesh so as to obtain accurate and stable solutions as the volume removal proceeds. Several related contributions of Professor E. Hinton are cited.

Investigates the efficiency of hybrid solution methods when incorporated into large‐scale topology and shape optimization problems and to demonstrate their influence on the overall performance of the optimization algorithms. Implements three innovative solution methods based on the preconditioned conjugate gradient (PCG) and Lanczos algorithms. The first method is a PCG algorithm with a preconditioner resulted from a complete or an incomplete Cholesky factorization, the second is a PCG algorithm in which a truncated Neumann series expansion is used as preconditioner, and the third is a preconditioned Lanczos algorithm properly modified to treat multiple right‐hand sides. The numerical tests presented demonstrate the computational advantages of the proposed methods which become more pronounced in large‐scale and/or computationally intensive optimization problems.

Futures research and long‐range planning in an urban school district involve the use of a methodology having no direct answers or precise rules in terms of experimental and design alternatives. While some design options can be found in operations research and management science, futures research projects in education are more likely to follow the directions for policy analysis initially suggested by Yehezkel Dror and re‐examined in Aaron Wildavsky. They note in policy analysis, 1. Much attention would be paid to the political aspects of public decision‐making and public policy‐making (instead of ignoring or condescendingly regarding political aspects) … 2. A broad conception of decision‐making and policy‐making would be involved (instead of viewing all decision‐making as mainly a resources allocation) … 3. A main emphasis would be on creativity and search for new policy alternatives, with explicit attention to encouragement of innovative thinking … 4. There would be extensive reliance on … qualitative methods … 5. There would be much more emphasis on futuristic thinking … 6. The approach would be looser and less rigid, but nevertheless systematic, one which would recognise the complexity of means‐ends interdependence, the multiplicity of relevant criteria of decision, and the partial and tentative nature of every analysis … (Wildavsky, Aaron, “Rescuing Policy Analysis from PPBS” Public Administration Review 29. 1969. pp. 189–202. Wildavsky's reference is to Dror's “Policy Analysts : A New Professional Role in Government Service” Public Administration Review. 27. 1967. pp.200–201). The intent is to describe a single futures research project conducted in a large urban school district staff development program. It is reported here as a means to outline a general approach to policy planning that might be used or adapted by other administrators who share an interest in futures research.

This paper presents the architecture for a new finite element program written in the C⁺⁺ programming language. A powerful command interpreter allows the user not only to introduce data, but also to define the algorithms that will treat this data to obtain the desired results. In this way, the program can be very easily configured to new computational strategies. By following an object‐oriented programming technique, we expect the program would not fall into the “stagnation” state that affects large finite element codes currently in use.

A context‐aware system is a system that uses context to provide relevant information or services to its users. While there has been a variety of context middleware infrastructures and context‐aware applications, little work has been done on integrating context into database management systems. The purpose of this paper is to consider a preference database system that supports context‐aware queries, that is, queries whose results depend on the context at the time of their submission.

The paper proposes using data cubes to store the dependencies between context‐dependent preferences and database relations and on‐line analytical processing techniques for processing context‐aware queries. This allows for the manipulation of the captured context data at various levels of abstraction, for instance, in the case of a context parameter representing location, preferences can be expressed, for example, at the level of a city, the level of a country or both. To improve query performance, the paper uses an auxiliary data structure, called context tree. The context tree stores results of past context‐aware queries indexed by the context of their execution. Finally, the paper outline the implementation of a prototype context‐aware restaurant recommender.

The use of context is important in many applications such as pervasive computing where it is important that users receive only relevant information.

Although there is much research on location‐aware query processing in the area of spatial‐temporal databases, integrating other forms of context in query processing is a rather new research topic.

The aim of the present work is to describe a numerical approach to the analysis of three‐dimensional reinforced concrete structures subject to prestressing. The finite element approach developed is described, with particular regard to the configuration of finite elements in relation to numerical model generation. An elasto‐viscoplastic material law is adopted. The non‐linear formulation is discussed, pointing out theoretical and numerical aspects. The computational examples, carried out using a specially developed code, aim at illustrating the characteristic aspects of the proposed approach.