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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.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

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.

This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.

The study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.

It is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.

The study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.

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 performance of the measurement matrix directly affects the quality of reconstruction of compressive sensing signal, and it is also the key to solve practical problems. In order to solve data collection problem of wireless sensor network (WSN), the authors design a kind of optimization of sparse matrix. The paper aims to discuss these issues.

Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing singular value decomposition (SVD). Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated.

The performance of reconstruction is better than that of Gaussian random matrix. The authors also apply this matrix to the data collection scheme in WSN. The result shows that it costs less energy and reduces the collection frequency of nodes compared with general method.

The authors design a kind of optimization of sparse matrix. Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing SVD. Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated.

The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field.

To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals.

The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data.

This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research.

To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

Describes selection criteria for property management software and details some available programs. Summarizes the key difficulties of implementing property management software systems as the lack of a systematic IT strategy and the lack of information about software availability. Considers the three main stages of a systematic approach to software selection to be: feasibility in cost and benefit terms, investigation of software options and successful implementation of a system.

Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).

The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.

Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.

The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.