This paper deals with the problem of efficiently computing the most preferred objects, in the sense of Pareto ordering, in a set of multidimensional objects involving outliers. These outliers, for instance, odd data from ad sales websites, can hide interesting points, so it is important to limit their impact. To do this, we propose a graded Skyline based on the typicality of each object. Such an approach allows to supply an α-cut of the skyline containing both nondominated points, and almost nondominated but more representative points. The computational cost of a naive algorithm performing this task is very high, especially in high-dimensional spaces, due to the large number of comparisons between objects. Since common skyline optimizations cannot be used in our approach, we propose an implementation on parallel architectures, CUDA and OpenMP. Experimental results on both real-world and synthetic data sets show that this processing strategy makes the approach scalable.

A large amount of linked data is now available, but to retrieve knowledge from these data, queries have to be formulated using formal query languages. While expressive query languages are developed, their use by end users, generally not familiar with formal languages, is limited. Keyword-based search is considered as a convenient and intuitive way for users to express their information needs. Keyword search over structured data is thus an interesting alternative but which raises challenging issues. The main challenge is to determine the meaning of a keyword query in order to translate it into a target formal query language, SPARQL in our case. In this paper, we address this challenge and propose a novel approach that relies on user interactions to determine the correct interpretation of the keyword query. The principle is to first ask the user to define a coarse keyword query, then to suggest candidate interpretations expressed in an explicit, thus unambiguous, and human readable form. Once the correct interpretation has been selected, the query may be refined with aggregate functions and comparatives. Experiments conducted on a large knowledge base show the effectiveness and the efficiency of the proposed approach.

AGGREGO SEARCH offers a novel keyword-based query solu-tion for end users in order to retrieve precise answers from se-mantic data sources. Contrary to existing approaches, AGGREGO SEARCH suggests grammatical connectors from natural languages during the query formulation step in order to specify the meaning of each keyword, thus leading to a complete and explicit definition of the intent of the search. An example of such a query is name of person at the head of company and author of article about “busi-ness intelligence". In order to help users formulate such connected keywords queries, a specific autocompletion strategy has been de-veloped. A translation of the user keyword query into SPARQL is performed on-the-fly during the interactive query construction pro-cess. For this demonstration, we show how AGGREGO SEARCH has been integrated on top of a mediation system to let users intu-itively define explicit and precise keyword queries in order to ex-tract knowledge distributed in heterogeneous large semantic data sources. 1.

In this demonstration we present a complete fuzzy-set-based approach to preference queries that tackles the two main questions raised by the introduction of flexibility and personalization when querying relational databases: i) how to efficiently execute preference queries? and, ii) how to help users define preferences and queries? As an answer to the first question, we propose PostgreSQL_f, a module implemented on top of PostgreSQL to handle fuzzy queries. To answer the second question, we propose ReqFlex an intuitive user interface to the definition of preferences and the construction of fuzzy queries.

This paper introduces a new type of database queries involving preferences. The idea is to consider competitive conditional preference clauses structured as a tree, of the type “preferably P1 or ⋅⋅⋅ or Pn; if P1 then preferably P1,1 or …; if P2 then preferably P2,1 or …,” where the Pis are not exclusive (thus the notion of competition). The paper defines two possible interpretations of such queries and outlines two evaluation techniques which follow from them. © 2010 Wiley Periodicals, Inc.

Introducing preferences inside user queries has gained more and more acceptance during the past decade. Besides, it turns out that the concept of bipolarity is of interest for expressing queries in the sense that some requirements are mandatory and play the role of constraints, whereas other are solely desirable. In this paper, we investigate how bipolarity may impact the division operator in the context of relational databases. Various forms of bipolar divisions can indeed be devised, each of them conveying a specific semantics. © 2011 Wiley Periodicals, Inc.

This article is concerned with the handling of imprecise information in databases. The need for dealing with imprecise data is more and more acknowledged in order to cope with real data, even if commercial systems are most of the time unable to manage them. Here, the possibilistic setting is taken into consideration because it is less demanding than the probabilistic one. Then, any imprecise piece of information is modeled as a possibility distribution intended for constraining the more or less acceptable values. Such a possibilistic database has a natural interpretation in terms of a set of regular databases, which provides the basic gateway to interpret queries. However, if this approach is sound, it is not realistic, and it is necessary to consider restricted queries for which a calculus grounded on the possibilistic database, that is, where the operators work directly on possibilistic relations, is feasible. Extended yes/no queries are dealt with here, where their general form is: “to what extent is it possible and certain that tuple t (given) belongs to the answer to Q,” where Q is an algebraic relational query. A strategy for processing such queries efficiently is proposed under some assumptions as to the operators appearing in Q. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 691–721, 2007.

In this paper, we are concerned with the querying of databases that may contain ill-known attribute values represented by possibility distributions. While the operations of selection and projection can be straightforwardly extended in such a context, this is not the case for the equi-join operator for which several semantics are possible. These different semantics are pointed out, as well as the corresponding situations in terms of information need. The issue of query compositionality is also examined in each case. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In traditional database management systems, queries are intended to retrieve data which satisfy crisp criteria. In some cases, this lack of flexibility leads to empty answers. That is one of the reasons why we have been investigating the extension of these systems so that they become able to support imprecise querying capabilities. In this article, the introduction of imprecise queries in a particular nonrelational system (Information Warehouse) is presented. One of the main interesting aspects of this work resides in the specific data model for which the semantics of fuzzy queries firstly has to be defined. © 1994 John Wiley & Sons, Inc.