Mouna Chebbah

A New Formalism for Evidential Databases

This paper is about modeling and querying evidential databases. This kind of databases copes with imperfect data which are modeled via the evidence theory. Existing works on such data deal only with the compact form of the database. In this article, we propose a new formalism for modeling and querying evidential databases based on the possible worlds form. This work is a first step toward the definition of a strong representation system.

Object-relational implementation of evidential databases

Due to the exploding number of information stored and shared over Internet, and the introduction of new technologies to capture and transit data, managing imperfect data is an important issue in many applications. An important tool for reasoning with imperfect data is the evidence theory, which is a generalization of the Bayesian inference. We call databases whose data imperfection are processed thanks to the evidence theory, the evidential databases. In this paper, we design the evidential database meta-model using an Oriented-Object modelling language (UML) and we implement it using an Object-Relational database. Although the implementation is not native, it showed an acceptable scalability.

Independence of Sources in Social Networks

Online social networks are more and more studied. The links between users of a social network are important and have to be well qualified in order to detect communities and find influencers for example. In this paper, we present an approach based on the theory of belief functions to estimate the degrees of cognitive independence between users in a social network. We experiment the proposed method on a large amount of data gathered from the Twitter social network.

On Top-K Queries Over Evidential Data.

Uncertain data are obvious in a lot of domains such as sensor networks, multimedia, social media, etc. Top-k queries provide ordered results according to a defined score. This kind of queries represents an important tool for exploring uncertain data. Most of works cope with certain data and with probabilistic top-k queries. However, at the best of our knowledge there is no work that exploits the Top-k semantics in the Evidence Theory context. In this paper, we introduce a new score function suitable for Evidential Data. Since the result of the score function is an interval, we adopt a comparison method for ranking intervals. Finally we extend the usual semantics/interpretations of top-k queries to the evidential scenario.

Skyline Operator over Tripadvisor Reviews Within the Belief Functions Framework

The crowdsourcing Tripadvisor platform do not offer a multi-criteria filtering functionality for their users. Thus, these users are obliged to choose only one criteria to filter a query’s results. In this paper, we introduce a new skyline operator, in the context of belief functions theory, to meet the multi-criteria filtering objective. The queried data, modeled with the theory of belief functions, takes into account all reviews and also reviewers’ reliabilities. Experiments show interesting results of the proposed skyline operator in terms of size and performance.

Evidential Top-k Queries Evaluation: Algorithms and Experiments

Top-k queries represent a vigorous tool to rank-order answers and return only the most interesting ones. ETop-k queries were introduced to discriminate answers in the context of evidential databases. Due to their interval degrees, such answers seem to be difficult to rank-order and to interpret. Two methods of ranking intervals were proposed in the evidential context. This paper presents an efficient implementation of these methods and discusses the experimental results obtained.

Positive and Negative Dependence for Evidential Database Enrichment

Uncertain databases are used in some fields to store both certain and uncertain data. When uncertainty is represented with the theory of belief functions, uncertain databases are assumed to be evidential. In this paper, we suggest a new method to quantify the source degree of dependence in order to enrich its evidential database by adding this dependence information. Enriching evidential databases with its sources degree of dependence can help user when making his decision. We used some generated mass functions to test the proposed method.