Associative classifiers are a family of classification techniques that use association rules to classify a new instance. Many works were proposed in this context, with a focus on how to select and combine the relevant association rules for classification. However, most of them do not consider the imperfection of data. In this paper, we tackle the problem of multi-rules based classification for evidential data, i.e., data where imperfection is represented and managed through the Dempster-Shafer theory. In this context, a new algorithm called WEviRC is introduced. This method uses different pruning techniques to eliminate the irrelevant rules and then to combine the selected ones using the powerful combination rules of the Dempster-Shafer theory. To evaluate our introduced method, we conducted extensive experiments on several data sets. Experiments show positive results in terms of classification quality and compare well with the existing methods.

Nowadays, we are interconnected with people whether professionally or personally using different social networks. However, we sometimes receive messages or advertisements that are not correlated to the nature of the relation established between the persons. Therefore, it became important to be able to sort out our relationships. Thus, based on the type of links that connect us, we can decide if this last is spammed and should be deleted. Thereby, we propose in this paper a belief approach in order to detect the spammed links. Our method consists on modelling the belief that a link is perceived as spammed by taking into account the prior information of the nodes, the links and the messages that pass through them. To evaluate our method, we first add some noise to the messages, then to both links and messages in order to distinguish the spammed links in the network. Second, we select randomly spammed links of the network and observe if our model is able to detect them. The results of the proposed approach are compared with those of the baseline and to the k-nn algorithm. The experiments indicate the efficiency of the proposed model.

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.

This paper is about modeling a possible worlds' form of imperfect databases, where imperfection is handled with the evidence theory. The possible worlds' form is already developed for other types of imperfect databases such as probabilistic and possibilistic databases. Such a form is essential to prove that a compact form querying method is a strong representation system. For the same aim, we propose in this article an extended model of possible worlds's form treating the tuple-level uncertainty in addition to the attribute-level uncertainty.

Currently, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others can take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we can detect communities for graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes give better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.

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.

Online Question Answering Communities (Q& A C) provide a valuable amount of information in several topics. The major challenge with Q& A C is the detection of the authoritative users. When manipulating real world data, we have to deal with imperfections and uncertainty that can occur. In this paper, we propose a belief measure of expertise allowing us to detect users with the highest degree of expertise based on their attributes. Experiments on a dataset from a large online Q&A Community prove that the proposed model can be used to improve the identification of most expert users.

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.

Discovering useful patterns plays an essential role in data management and data mining. Frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied on (standard) precise transaction databases. Uncertain transaction databases consist of sets of existentially uncertain items. The uncertainty of items in transactions makes traditional techniques in applicable. Recent works propose interesting SAT-based encodings for the problem of discovering frequent itemsets in deterministic transaction databases. Our aim in this work is to extend the SAT-based encoding of frequent itemset mining to uncertain databases. Then, we propose a novel declarative mining frame-work for extracting uncertain frequent patterns from uncertain transaction databases. It makes an original use of constraints relaxation to obtain upper bounds to the expected support of frequent patterns, while guaranteeing the enumeration of all frequent itemsets with no false negatives. We experimentally evaluated our approach. The experimental results on real and synthetic data sets demonstrate the effectiveness of our proposal in mining frequent patterns.

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.

Frequent itemset mining (FIM) is a useful task for discovering frequent co-occurring items. Since its inception, a number of significant FIM algorithms have been developed to speed up mining performances. Unfortunately, for huge dataset, scalability remains an important issue. In this work, we propose a new propositional satisfiability (SAT) parallel approach, called PSATCFIM, to deal with closed frequent itemsets mining problem. It is designed to run on multicore machines and uses a divide and conquer approach to partition the enumeration process. Such partitioning based on guiding paths eliminates computational overlap between cores. Through empirical study, we demonstrate that PSATCFIM can achieve significant performance improvements with respect to the sequential based version.

The web plays an important role in people's social lives since the emergence of Web 2.0. It facilitates the interaction between users, gives them the possibility to freely interact, share and collaborate through social networks, online community forums, blogs, wikis and other online collaborative media. However, an other side of the web is negatively taken such as posting inflammatory messages. Thus, when dealing with the online community forums, the managers seek to always enhance the performance of such platforms. In fact, to keep the serenity and prohibit the disturbance of the normal atmosphere, managers always try to novice users against these malicious persons by posting such message (DO NOT FEED TROLLS). But, this kind of warning is not enough to reduce this phenomenon. In this context we propose a new approach for detecting malicious people also called 'Trolls' in order to allow community managers to take their ability to post online. To be more realistic, our proposal is defined within an uncertain framework. Based on the assumption consisting on the trolls' integration in the successful discussion threads, we try to detect the presence of such malicious users. Indeed, this method is based on a conflict measure of the belief function theory applied between the different messages of the thread. In order to show the feasibility and the result of our approach, we test it in different simulated data.

Several researches in Semantic Web are based on information search centered meaning. The common purpose of these researches is to improve current information search and retrieval methods. The last few years have seen a various number of developed semantic search systems. The requirement of a complete survey in this field is one of the main purpose of this paper. The approaches, methodologies and objectives of some recognized projects and their corresponding practical systems are exploited to constitute this overall view of semantic search. In this paper, we present and compare various research directions in semantic search. Further, we give discussion with regards to future research in this area.

Deals with the task of propagating new evidence through belief networks. An important feature of these graphical networks is that they implicitly define conditional independence relationships among the variables. The proposed algorithm permits us to directly use conditional belief functions in evidential reasoning systems and allows the propagation of several observations occurring at the same time.