Amel Bouzeghoub

TISoN: Trust Inference in Trust-Oriented Social Networks

Trust systems represent a significant trend in decision support for social networks’ service provision. The basic idea is to allow users to rate each other even without being direct neighbours. In this case, the purpose is to derive a trust score for a given user, which could be of help to decide whether to trust other users or not. In this article, we investigate the properties of trust propagation within social networks, based on the notion of <i>transitivity</i>, and we introduce the <b><i>TISoN</i></b> model to generate and evaluate <b>T</b>rust <b>I</b>nference within online <b>So</b>cial <b>N</b>etworks. To do so, (<i>i</i>) we develop a novel <b><i>TPS</i></b> algorithm for <b>T</b>rust <b>P</b>ath <b>S</b>earching where we define neighbours’ priority based on their direct trust degrees, and then select trusted paths while controlling the path length; and, (<i>ii</i>) we develop different <b><i>TIM</i></b> algorithms for <b>T</b>rust <b>I</b>nference <b>M</b>easuring and build a trust network. In addition, we analyse existing algorithms and we demonstrate that our proposed model better computes transitive trust values than do the existing models. We conduct extensive experiments on a real online social network dataset, Advogato. Experimental results show that our work is scalable and generates better results than do the pioneering approaches of the literature.

FSCEP: A New Model for Context Perception in Smart Homes

With the emergence of the Internet of Things and smart devices, smart homes are becoming more and more popular. The main goal of this study is to implement an event driven system in a smart home and to extract meaningful information from the raw data collected by the deployed sensors using Complex Event Processing (CEP). These high-level events can then be used by multiple smart home applications in particular situation identification. However, in real life scenarios, low-level events are generally uncertain. In fact, an event may be outdated, inaccurate, imprecise or in contradiction with another one. This can lead to misinterpretation from CEP and the associated applications. To overcome these weaknesses, in this paper, we propose a Fuzzy Semantic Complex Event Processing (FSCEP) model which can represent and reason with events by including domain knowledge and integrating fuzzy logic. It handles multiple dimensions of uncertainty, namely freshness, accuracy, precision and contradiction. FSCEP has been implemented and compared with a well known CEP. The results show how some ambiguities are solved.

Graph-oriented load-shedding for semantic Data Stream processing

The continuous and progressive growth of the need for knowledge extraction from continuous data streams, in an exponential way, has favored the emergence of a new research axis from the semantic web community. In the few last years, many semantic data stream processing systems have been proposed by combining Data Stream Management Systems (DSMS) technologies and Semantic Web technologies (RDF1/SPARQL2) for annotation, publication and reasoning on these data streams. However, considering their infinite volume and unknown velocity, processing and storing their contents remain impossible, which leads to introduce techniques for reducing load and/or summarizing data. In this context, we propose a graph-oriented approach to reduce the semantic data streams volume. In order to validate our approach, we implemented it using Simple Random Sampling and Stratified Random Sampling and we experimented it using the CSRBench benchmark. Our approach allows to maintain the data consistency and their semantic level.

FreGraPaD: Frequent RDF graph patterns detection for semantic data streams

Nowadays, high volumes of data are generated and published at a very high velocity by real-time systems, such as social networks, e-commerce, weather stations and sensors, producing heterogeneous data streams. To take advantage of linked data and offer interoperable solutions, semantic Web technologies have been used. To analyze these huge volumes of data, different stream mining algorithms exist such as compression or load-shedding. Nevertheless, most of them need many passes through the data and often store part of it on disk. If we want to apply efficient compression on semantic data streams, we need to first detect frequent graph patterns in RDF streams. In this article, we present FreGraPaD, an algorithm that detects those patterns in a single pass, using exclusively internal memory and following a data structure oriented approach. Experimental results clearly confirm the good accuracy of FreGraPaD in detecting frequent graph patterns from semantic data streams.

Unified Modeling of Quality of Context and Quality of Situation for Context-Aware Applications in the Internet of Things

This paper discusses the requirements of situation identification in the Internet of Things and the necessity to consider the quality of the input context data during the inference process for deriving a situation and evaluating its resulting quality. We propose to extend previous works by integrating the QoCIM meta-model within the muSIC framework dedicated to situation identification. Situation identification is derived using an ontological approach and Quality criteria are aggregated using the fuzzy Choquet operator for computing the quality of a situation. This paper shows that QoCIM allows to model quality of context (QoC) as well as quality of situation in a unified approach.

POL: A Pattern Oriented Load-Shedding for Semantic Data Stream Processing

Nowadays, high volumes of data are generated and published at a very high velocity, producing heterogeneous data streams. This has led researchers to propose new systems named RDF Stream Processors RSP, to deal with this new kind of streams. Unfortunately, these systems are fallible when their maximum supported speed is reached especially in a limited system resources environment. To overcome these problems, recent efforts have been made in the field. Some of them decrease the volume of RDF data streams using compression or load-shedding techniques, mostly according to a probabilistic approach. In this paper we propose POL: a Pattern Oriented approach to Load-shed data from RDF streams based on a deterministic approach. As a pre-processing task through a unique pass, the approach extracts the exact needed semantic data from the stream. The conducted experiments on public available datasets have demonstrated the effectiveness of our approach.

LRS: A Novel Learning Routing Scheme for Query Routing on Unstructured P2P Systems

Query routing is a fundamental problem in unstructured Peer-to-Peer systems. Recently, researches in this area have focused on methods based on query-oriented routing indices. These methods use the historical information of past queries and query hits to build a local knowledge base per peer, which represents the user’s interests or profile. Existing approaches represent the user’s profile only by some statistics about past queries and they have not addressed two difficult challenging problems: (i) the bootstraping (ii) the unsuccessful relevant peers search. Indeed, when a peer selects an insufficient number of relevant peers from its local knowledge base, it floods the query through the network, which badly affects the routing efficiency and effectiveness. To tackle these problems, we introduce a novel Learning Routing Scheme (LRS). We implemented the proposed scheme and compared its routing efficiency and retrieval effectiveness with a broadcasting scheme (without learning) and a learning scheme taken from the literature. Experimental results show that our scheme carries out better than other ones with respect to accuracy.

ARCID: A New Approach to Deal with Imbalanced Datasets Classification

Classification is one of the most fundamental and well-known tasks in data mining. Class imbalance is the most challenging issue encountered when performing classification, i.e. when the number of instances belonging to the class of interest (minor class) is much lower than that of other classes (major classes). The class imbalance problem has become more and more marked while applying machine learning algorithms to real-world applications such as medical diagnosis, text classification, fraud detection, etc. Standard classifiers may yield very good results regarding the majority classes. However, this kind of classifiers yields bad results regarding the minority classes since they assume a relatively balanced class distribution and equal misclassification costs. To overcome this problem, we propose, in this paper, a novel associative classification algorithm called Association Rule-based Classification for Imbalanced Datasets (ARCID). This algorithm aims to extract significant knowledge from imbalanced datasets by emphasizing on information extracted from minor classes without drastically impacting the predictive accuracy of the classifier. Experimentations, against five datasets obtained from the UCI repository, have been conducted with reference to four assessment measures. Results show that ARCID outperforms standard algorithms. Furthermore, it is very competitive to Fitcare which is a class imbalance insensitive algorithm.

Patorc: Pattern Oriented Compression for Semantic Data Streams

Recently, semantic data streams were proposed as a solution to cope with the heterogeneity of the original streams. However, nowadays, huge volumes of data are produced on the web, at very high velocity. This may provoke bottleneck effect and decrease efficiency of RDF stream processing engines. One approach to address this issue is to compress the data in the stream to decrease the delays and costs of the RDF exchange on the network. In this paper, we propose Patorc: a PATern ORiented Compression approach, a lossless method for compressing semantic data stream. Our approach takes advantage of the RDF data streams key features, which are the regularity of their graph structure and the redundancy of part of data. Experiments carried on publicly available datasets have demonstrated the effectiveness of our approach.

A query based graph-oriented load-shedding for RDF stream processing

To cope with heterogeneity of data in streams, Semantic Web technologies (RDFVSPARQL2) have recently been used for annotation, publication and reasoning on these data. To deal with this new kind of streams, researchers have proposed new systems named RDF Stream Processing (RSP). Unfortunately, in limited system resources environment, these systems are fallible as soon as their maximum supported speed is reached. To overcome these problems, some efforts have been done in this area. Most of them, based on a triple-oriented approach and according to a probabilistic method, decrease the volume of RDF data stream using load-shedding techniques. In this paper we propose an enhancement of a Graph-Oriented approach for load-shedding semantic data streams, by considering the continuous query as input. Conducted experiments show that we can keep the RSPs recall at 100% even if we drop more than half of data.