International audience

This paper addresses the prediction of mobility and emphasizes the main steps of our context-based mobility prediction approach that are trajectory modelling, data processing and prediction process. First, we present our context-based and prediction - oriented trajectory model, which relies on the grid technique for trajectory description. Second, we describe a data processing architecture for data coming from Wifi networks. Third, we focus on our data mining-based prediction process used for the first experimentations. Evaluation of our contributions is performed on a real dataset. First results showed the positive impact of our trajectory model on our mobility prediction process.

Service selection is a challenging task, and a lot of effort has been devoted to tools that assist the user in choosing the service whose non-functional parameters better match her/his preferences. In many practical situations, the responsibility to decide which is the appropriate service is shared among multiple parties. A standard approach to this service selection problem is to discard services that are unanimously considered inappropriate and focus on the rest. However, as the involved parties may have colliding interests, only a few services may be eliminated. This work addresses this shortcoming and enables users to reach a “democratic” decision by means of a majority vote: a service is eliminated if the majority of the parties find it inappropriate. We formulate the problem using dominance relationships, and propose algorithms that return an appropriate subset of services for the parties, while being more efficient than standard techniques. Moreover, we consider the problem of defining an appropriate ranking for the non eliminated services, and formulate it as an instance of a group recommendation problem. Finally, we demonstrate the effectiveness and the efficiency of our approach through extensive experimental evaluation on real-based and synthetic datasets.

In many real applications, data are intrinsically uncertain due to measurement errors, interpretability issues, information incompleteness, etc. In those uncertain databases, users usually express quality requirements when the system evaluates their queries. However, as they may not be familiar with the contents of the queried database, their queries may be failing i.e., they may return no results or results that do not satisfy the expected degree of certainty. To provide users with relevant information in order to obtain alternative satisfactory results, we introduce a cooperative approach based on the dualization concept. This approach computes a set of meaningful subqueries (MFSs and XSSs) of the initial failing query, which is of paramount importance for query reformulation and relaxation purposes. The conducted experiments show that our proposition, a Mixed Dualization Matrix-Based approach (MDMB), outperforms existing algorithms, especially for large queries.

Data on the Web is often pervaded with uncertainty. This is due to the openness of the Web and variety of sources which makes reliability of collected data questionable. In this paper, we address the Resource Description Framework (RDF) data uncertainty problem. Uncertainty here is represented by the rich possibility theory. To this end, we describe a general framework for supporting SPARQL-like queries on possibilistic RDF data, that we denote Pi-SPARQL. To describe possibilistic requirements, Pi-SPARQL extends SPARQL in the following two ways. First, it allows expressing possibility degrees to RDF based applications in an easy manner by associating the solutions for graph patterns with possibility measures. Then, Pi-SPARQL proposes appropriate semantics of the solution mappings and evaluation, i.e., it enables users to deal with uncertain RDF data specifications and access the possibility measures associated to the solutions.

Decision support systems (DSS) become more and more complex to deal with decision-making problems related to the current conjuncture. Several agent-oriented methodologies have been proposed to design distributed decision support systems and improve the decision process. This paper presents a comparative study of these existing methodologies (based on a set of relevant criteria like the adaptability, reliability and decision objective) and identifies a set of issues, notably the lack of faults tolerance mechanism and the absence of the formal verification. Hence, we propose a work-in-progress approach (also based on multi-agent paradigm) called ADS2adapted for distributed decision support systems. We also present how ADS2will allow to remove the global control, to reinforce the adaptability and to reduce developers burden thanks to the utilization of model-driven engineering settings.

A large number of applications manage uncertain data. Usually, users expect high quality results when they pose queries with strict conditions over these data. However, as they may not be clear about the contents of the databases that contain such data, these queries may be failing i.e., they may return no result or results that do not satisfy the expected degree of certainty. In this paper, we deal with this problem, in the field of uncertain databases, by proposing an efficient approach that identifies the parts of the failing query, called Minimal Failing Subqueries (mFSs), that are responsible of its failure. Our approach also computes, in the same time, a set of Maximal Succeeding Subqueries (XSSs) that represent non failing queries with a maximal number of predicates of the initial query. We demonstrate the impact of our proposal with a set of experiments on synthetic and real datasets.

In the last decade, skyline queries have attracted the interest of several researchers in the database field due to their ability to retrieve interesting objects among a large set of objects. Skyline analysis is a powerful tool in a wide spectrum of real applications including multi-criteria optimal decision making, preference answering and many applications where uncertain, imprecise and noisy data inherently exist. As large amounts of distributed data over Internet are communicated and shared, an important problem is to retrieve the global skyline from all the distributed local sites. In addition, though the skyline queries can control selection, there exist not much works that can handle skyline queries under database updates. In this paper, based on the marginal points notions, we introduce new methods to efficiently compute the global skyline from distributed local sites and over frequently updated databases. The efficiency and effectiveness of our proposal are verified by extensive experimental results.

Queries posed by a user over a database do not always return the desired responses. It may sometimes result an empty set of answers especially when data are pervaded with uncertainty and imprecision. Thus, to address this problem, we propose an approach for relaxing a failing query in the context of evidential databases. The uncertainty in such databases is expressed within the belief function theory. The key idea of our approach is to use a machine learning method more precisely the belief K-modes clustering technique to relax the failing queries by modifying the constraints in order to provide successful alternatives which may be of interest to the user.

Uncertain, imprecise and noisy data arise in a number of domains including sensor networks and data integration. Skyline analysis is a powerful tool in a wide spectrum of real applications involving multi-criteria optimal decision making. The skyline operator aims at returning the most interesting objects in a database. Previous researches showed that the skyline size over uncertain data is too large to be exploited. In this paper, we propose an advanced skyline analysis over uncertain databases where uncertainty is modelled by the evidence theory. We particularly tackle the following two important issues: (1) model the skyline query over an evidential database (2) rank the evidential skyline result and retrieve k skyline objects that are expected to have the highest score with considering the confidence level of the objects. We also study its impact on the top-k result. The efficiency and effectiveness of our proposal are verified by extensive experimental results.

In many applications domains, time modeling has a fundamental role. Allen temporal relations are one of the most used and known formalisms for modeling and handling temporal data. However, classical Allen relations deal only with crisp time information, but time is often subjective and fuzzy. This paper discusses a disjunctive view of temporal relations between fuzzy time intervals. This approach is mostly based on an extension of Allen relations allowing to compare two fuzzy time intervals. By leveraging some particular fuzzy comparison indices, this extension is implemented in the Fuzz - TIME tool developed in our previous works.

Thanks to their ability to return interesting objects in a database, the skyline queries have received considerable attention from the database community over the last few years. Skyline analysis is a powerful tool in a wide spectrum of real applications including multi-criteria optimal decision making, preference answering and many applications where uncertain, imprecise and noisy data inherently exist. As large amounts of distributed data over an Internet are communicated and shared, an important problem is to retrieve the global skyline from all the distributed local sites. In this paper, based on the skyline query over centralised imperfect data where imperfection is modeled by the evidence theory, we propose to efficiently compute the global skyline from distributed local sites. The efficiency and effectiveness of our proposal are verified by extensive experimental results.

In recent years, crowdsourcing has become essential in a wide range of Web applications. Human factors play a key role in achieving high quality answers in crowdsourcing-based solving tasks. The most major factor is pertained to the uncertainty of workers about the responses that they provide to resolve the task at hand. On the other hand, workers may have diverse levels of expertise and skill. It is then important to take into account both the degrees of uncertainty and expertise when aggregating the set of worker answers. In this paper, we investigate some advanced crowdsourcing aggregation methods to find the correct answers by leveraging both expertise and uncertainty of workers in a unified way. Workers' uncertainty is represented in a possibilistic way, while a fine-grained scale for interpreting the degrees of skill is introduced. Finally, we present some comprehensive experiments to validate the effectiveness of our proposal.

This paper presents a Query-and-Relax System (QaRS) designed to facilitate the exploitation of large knowledge bases. QaRS proposes a graphical interface to construct a SPARQL query and use different cooperative answering techniques. The proposed cooperative techniques help users in finding alternative answers when their queries fail or do not return the expected number of answers. The present demonstration includes three main relaxation strategies: (1)- automatic where the system automatically relaxes the query based on similarity measures, (2)- manual where the user can specify the conditions that can or cannot be relaxed as well as the tolerance values and (3)- interactive where QaRS computes the causes of the query failure as a set of Minimal Failing Subqueries (MFSs )a nd then the user chooses the relaxation operators according to these MFSs.

While uncertainty can't be ignored in real-world problems, there is almost no research work addressing this issue in the recommender systems framework, especially all that relates to user ratings preferences. Indeed, the subjectivity of user's rating and his/her changing preferences over time, make them subject to uncertainty. Usually, user's imprecise rating for an item (product or service) is time-dependent information and generally provided much later. Meantime the item may change either by degrading or improving its inherent quality. The rating therefore may deviate, since it doesn't describe faithfully the actual current state of the item. This deviation leads to a form of uncertainty on user preferences that we handle in this paper. We show that uncertainty is an ubiquitous aspect in building recommender systems and its taking into account can help predicting the most accurate items by improving their certainty degrees.

Data-driven Web services build on service-oriented technologies to provide an interoperable method of interacting with data sources on top of the Web. Data Web services composition has emerged as a flexible solution to answer users’ complex queries on the fly. However, as the number of Web services on the Web grows quickly, a large number of candidate compositions that would use different (most likely competing) services may be used to answer the same query. User preferences are a key factor that can be used to rank candidate services/compositions and retain only the best ones. In this article, we present a novel approach for computing the top- k data service compositions based on user preferences. In our approach, we model user preferences using fuzzy sets and incorporate them into the composition query. We use an efficient RDF query rewriting algorithm to determine the relevant services that may be used to answer the composition query. We match the (fuzzy) constraints of the relevant services to those of the query and determine their matching degrees using a set of matching methods. We then rank-order the candidate services based on a fuzzification of Pareto dominance and compute the top- k data service compositions. In addition, we introduce a new method for increasing the diversity of returned top- k compositions while maintaining as much as possible the compositions with the highest scores. Finally, we describe the architecture of our system and present a thorough experimental study of our proposed techniques and algorithms. The experimental study demonstrates the efficiency and the effectiveness of our techniques in different settings.

In this paper, we introduce a novel concept called collective skyline to deal with the problem of multiple users preferences. We then conduct a set of experiments that demonstrate the effectiveness of the introduced concept.

Nowadays, the exploding number of functionally similar Web services has led to a new challenge of selecting the most relevant services using quality of service (QoS) aspects. Traditionally, the relevance of a service is determined by computing an overall score that aggregates individual QoS values. Users are required to assign weights to QoS attributes. This is a rather demanding task and an imprecise specification of the weights could result in missing some user desired services. Recent approaches focus on computing service skyline over a set of QoS aspects. This can completely free users from assigning weights to QoS attributes. However, two main drawbacks characterize such approaches. First, the service skyline often privileges services with a bad compromise between different QoS attributes. Second, as the size of the service skyline may be quite large, users will be overwhelmed during the service selection process. In this paper, we introduce a new concept, called alpha-dominant service skyline, to address the above issues and we develop a suitable algorithm for computing it efficiently. Experimental evaluation conducted on synthetically generated datasets, demonstrates both the effectiveness of the introduced concept and the efficiency of the proposed algorithm.

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.

Fuzzy modifiers are unary operators on fuzzy sets aiming at transforming a fuzzy set into another. In this paper, we propose a new approach to construct representation for fuzzy modifiers. The approach relies on a tolerance relation modeled by a suitable parameterized proximity relation. The newly introduced fuzzy modifiers are not only endowed with clear semantics, but they result in significant changes on the constituent parts of a fuzzy set. We also show how this class of fuzzy modifiers can be applied for modeling weakening and intensifying linguistic hedges.

Uncertain relations between temporal points are represented by means of possibility distributions over the three basic relations precedes, equals, and follows. Operations for computing inverse relation, for composing relations, for combining relations coming from different sources and pertaining to the same temporal points, or for representing negative information are defined. An illustrative example of representation and reasoning with uncertain temporal relations is provided. This article shows how possibilistic temporal uncertainty can be handled in the setting of point algebra. Moreover, the article emphasizes the advantages of the possibilistic approach over a probabilistic approach previously proposed. This work does for the temporal point algebra what the authors previously did for the temporal interval algebra. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 157–179, 2007.

Uncertain relations between temporal points are represented by means of possibility distributions over the three basic relations "smaller than", "equal to", and "greater than". Operations for computing inverse relations, for composing relations, for combining relations coming from different sources and pertaining to the same temporal points, or for representing negative information, are defined. An illustrative example of representing and reasoning with uncertain temporal relations is given. This paper shows how possibilistic temporal uncertainty can be handled in the setting of point algebra. Moreover, the paper emphasizes the advantages of the possibilistic approach over a probabilistic approach previously proposed. This work does for the temporal point algebra what the authors previously did for the temporal interval algebra.

This paper proposes a fuzzy set-based approach for handling relative orders of magnitude stated in terms of closeness and negligibility relations. At the semantic level, these relations are represented by means of fuzzy relations controlled by tolerance parameters. A set of sound inference rules, involving the tolerance parameters, is provided, in full accordance with the combination/projection principle underlying the approximate reasoning method of Zadeh. These rules ensure a local propagation of fuzzy closeness and negligibility relations. A numerical semantics is then attached to the symbolic computation process. Required properties of the tolerance parameter are investigated, in order to preserve the validity of the produced conclusions. The effect of the chaining of rules in the inference process can be controlled through the gradual deterioration of closeness and negligibility relations involved in the produced conclusions. Finally, qualitative reasoning based on fuzzy closeness and negligibility relations is used for simplifying equations and solving them in an approximate way, as often done by engineers who reason about a mathematical model. The problem of handling qualitative probabilities in reasoning under uncertainty is also investigated in this perspective.

Deals with relative orders of magnitude reasoning that handles notions such as closeness (Cl) and negligibility (Ne), by means of fuzzy relations. A set of inference rules describes how these relations can be composed and how they behave with respect to addition and product. Fuzzy numbers play the role of parameters underlying the semantics of Cl and Ne. Some of rules lead to conclusions involving closeness relations which are no longer symmetric. We propose symmetric variants of these rules. The results provided by these variants are sound but not complete; although the symbolic reasoning is made easier. We show that this type of reasoning can be used for proving the incoherence of set of equations or finding approximate solutions thereof.