Mohamed Anis Bach Tobji

Computing Skyline from Evidential Data

The skyline operator is a powerful means in multi-criteria decision-making since it retrieves the most interesting objects according to a set of attributes. On the other hand, uncertainty is inherent in many real applications. One of the most powerful approaches used to model uncertainty is the evidence theory. Databases that manage such type of data are called evidential databases. In this paper, we tackle the problem of skyline analysis on evidential databases. We first introduce a skyline model that is appropriate to the evidential data nature. We then develop an efficient algorithm to compute this kind of skyline. Finally, we present a thorough experimental evaluation of our approach.

Incremental Maintenance of Frequent Itemsets in Evidential Databases

In the last years, the problem of Frequent Itemset Mining (FIM) from imperfect databases has been sufficiently tackled to handle many kinds of data imperfection. However, frequent itemsets discovered from databases describe only the current state of the data. In other words, when data are updated, the frequent itemsets could no longer reflect the data, i.e., the data updates could invalidate some frequent itemsets and vice versa, some infrequent ones could become valid. In this paper, we try to resolve the problem of Incremental Maintenance of Frequent Itemsets (IMFI) in the context of evidential data. We introduce a new maintenance method whose experimentations show efficiency compared to classic methods.

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.

Selecting skyline stars over uncertain databases: Semantics and refining methods in the evidence theory setting

In recent years, a great attention has been paid to skyline computation over uncertain data. In this paper, we study how to conduct advanced skyline analysis over uncertain databases where uncertainty is modeled thanks to the evidence theory (a.k.a., belief functions theory). We particularly tackle an important issue, namely the skyline stars (denoted by SKY2) over the evidential data. This kind of skyline aims at retrieving the best evidential skyline objects (or the stars). Efficient algorithms have been developed to compute the SKY2. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches that considerably refine the huge skyline. In addition, the conducted experiments have shown that our algorithms significantly outperform the basic skyline algorithms in terms of CPU and memory costs.

Efficient Skyline Maintenance over Frequently Updated Evidential Databases

In many recent applications, data are intrinsically uncertain, noisy and error-prone. That is why, uncertain database management has attracted the attention of several researchers. Data uncertainty can be modeled in the evidence theory setting. On the other hand, skyline analysis is a powerful tool in a wide spectrum of real applications involving multi-criteria optimal decision making. It relies on Pareto dominance relationship. However, the skyline maintenance is not an easy task when the queried database is updated. This paper addresses the problem of the maintenance of the skyline objects of frequently updated evidential databases. In particular, we propose algorithms for maintaining evidential skyline in the case of object insertion or deletion. Extensive experiments are conducted to demonstrate the efficiency and scalability of our proposal.

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.

Frequent Itemset Mining from Databases Including One Evidential Attribute

Frequent Itemset Mining (FIM) problem has been extensively tackled in the context of perfect data. However, real applications showed that data are often imperfect (incomplete and/or uncertain) which leads to the need of FIM algorithms that process imperfect databases. In this paper we propose a new algorithm for mining frequent itemsets from databases including exactly one evidential attribute. An evidential attribute is an attribute that could have uncertain values modelled via the evidence theory, i.e., a basic belief assignment. We introduce in this paper a variant of the structure Belief Itemset Tree (BIT) for mining frequent itemsets from evidential data and we lead some experiments that showed efficiency of our mining algorithm compared to the existing ones.

Skyline queries over possibilistic RDF data

Abstract Volume and veracity of data on the Web are two main issues in managing information. In this paper, we tackle these two issues, with a particular interest to Resource Description Framework (RDF) data. For veracity management, we rely on a powerful uncertainty theory, namely possibility theory. Therefore, we propose a model for representing and managing possibilistic RDF data. Alongside, to filter the massive amount of RDF data, we use the skyline operator to find out a small set of resources that satisfy predefined user preferences. To this aim, we also propose a skyline operator to extract possibilistic RDF resources that are possibly dominated by no other resources according to Pareto dominance definition. We introduce a dominance operator and a skyline model adopted to the aforementioned kind of data. In addition, we propose an efficient algorithm to compute the skyline with a reasonable performance. Experiments led on the skyline computation showed satisfying results.

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 Modeling and Computing over Trust RDF Data.

Resource Description Framework (RDF) data come from various sources whose reliability is sometimes questionable. Therefore, several researchers enriched the basic RDF data model with trust information. New methods to represent and reason with trust RDF data are introduced. In this paper, we are interested in querying trust RDF data. We particularly tackle the skyline problem, which consists in extracting the most interesting trusted resources according to user-defined criteria. To this end, we first redefined the dominance relationship in the context of trust RDF data. Then, we proposed an appropriate semantics of the trust-skyline; the set of most interesting resources in a trust RDF dataset. Efficient methods to compute the trust-skyline are provided and compared to some existing approaches as well. Experiments led on the algorithms’ implementations showed promising results.