Mohamed Salah Gouider

Towards a new mechanism of extracting cyclic association rules based on partition aspect

Never before a such abundant volume of data is collected as the one which we attend nowadays. Thus, its exploration becomes increasingly difficult, especially if we highlight the temporal aspect during the extraction of association rules. Therefore, several works were devoted to this problematic by introducing the temporal association rules mining. In this paper, we focus on cyclic association rules, classified as a category of the temporal association rules. Indeed, this class aims to discover new relationships between items that display regular cyclic variation over time. As a response to the anomalies characterizing the classical approaches addressing this issue, i.e., SEQUENTIAL and INTERLEAVED algorithms, we introduce in this paper, a new algorithm called PCAR ALGORITHM. In fact, the major advantages characterizing our approach consist on its performance and its incremental aspect. The experiments were carried out to prove the robustness and the efficiency of our proposed algorithm vs the pioneering approaches in the same trend.

Enhancing Spatial Datacube Exploitation: A Spatio-semantic Similarity Perspective

Due to the enormous amount of data stored in spatial multidimensional databases (also called spatial datacubes) and the complexity of multidimensional structures, extracting interesting information by exploiting spatial data cubes becomes more and more difficult. Users might overlook what part of the cube contains the relevant information and what the next query should be. This could affect their exploitation of spatial datacubes.

A Hybrid Approach for Drug Abuse Events Extraction from Twitter

Since their emergence, social media have become a reliable source of social events which attracted the interest of research community to extract them for many business requirements. However, unlike formal sources like news articles, social data exploitation for events extraction is much harder regarding the complex character of social text. Many approaches, ranging from linguistic techniques to learning algorithms, were proposed to succeed this task. Nevertheless, achieved results are weak regarding the complexity and completeness of the task.In this paper, we focus on private events extraction from Twitter by tracking digital drug abusers. We propose a hybrid approach in which we combine strengths of linguistic rules and learning techniques looking for better performance. In fact, we use linguistic rules to build an automatically annotated training set and extract a set of features as well, to be used in a learning process in order to improve obtained results. The proposed approach outperforms the baseline by 24,8% thanks to combination of techniques.

Large Scale Microblogging Intentions Analysis with Pattern Based Approach

In recent years, social networks have become very popular. Twitter, a micro-blogging service, is estimated to have about 200 million registered users and these users create approximately 65 million tweets a day. Twitter constitutes a powerful medium today that people use to express their thoughts and intentions. The challenge is that each tweet is limited in 140 characters, and is hence very short. It may contain slang and misspelled words. Thus, it is difficult to apply traditional NLP techniques which are designed for working with formal languages, into Twitter domain. Another challenge is that the total volume of tweets is extremely high, and it takes a long time to process. In this paper, we describe a large-scale distributed system for intentions analysis process based on lexico semantic patterns using Hadoop Distributed File System (HDFS) and MapReduce functions. We conduct a case study of user intentions in the commercial field. The proposed method has stably performed data gathering and data loading. Besides, it has maintained stable load balancing of memory and CPU resources during data processing by the HDFS system. The proposed MapReduce functions have effectively performed intentions analysis in the experiments. Finally, obtained results show the importance and effectiveness of intentions detection using semantic patterns.

New Algorithm for Frequent Itemsets Mining from Evidential Data Streams

Mining frequent itemsets is a very interesting issue in Data Streams handling, useful for several real world applications. This task reveals many challenges such the one-pass principle as well as performance problems due to the huge volumes of Data Streams. Performance is defined in terms of CPU and main memory consumption in terms of uncertainty management issues. In this paper, we introduce the concept of Evidential Data Streams and we present a new innovative algorithm for mining frequent itemsets from evidential data streams, based on the evidence theory concepts.

Personnalisation OLAP et SIG : Etude comparative et perspectives de personnalisation SOLAP

Les systèmes SOLAP (spatial online analytical processing) gèrent de grandes quantités de données spatiales permettant leur analyse en ligne. Ces systèmes couplent les fonctionnalités avancées des systèmes OLAP (online analytical processing) et des systèmes d’information géographique (SIG). Afin d’adapter ces systèmes aux besoins et préférences de l’utilisateur, plusieurs travaux se sont intéressés à leur personnalisation. Dans ce papier, nous présentons un cadre conceptuel pour l’évaluation des travaux existants concernant la personnalisation dans les systèmes OLAP et les SIG. En plus, nous détaillons des nouvelles pistes de recherche pour la personnalisation des systèmes SOLAP. SOLAP (spatial online analytical processing) systems store spatial data, and allow online analysis of such data. These systems combine the advanced features of online analytical processing (OLAP) and geographic information systems (GIS). The personalisation of these systems aims at adapting them to users’ needs and preferences. Various studies are interested in the personalisation of OLAP systems and GIS by defining different tools and methods. In this paper, we present a conceptual framework to evaluate existing work dealing with the personalisation of OLAP systems and GIS. In addition, several evaluation criteria are used to identify the existence of trends as well as potential needs for further investigation.

BUILDING A DATA WAREHOUSE FOR NATIONAL SOCIAL SECURITY FUND OF THE REPUBLIC OF TUNISIA

The amounts of data available to decision makers are increasingly important, given the network availability, low cost storage and diversity of applications. To maximize the potential of these data within the National Social Security Fund (NSSF) in Tunisia, we have built a data warehouse as a multidimensional database, cleaned, homogenized, historicized and consolidated. We used Oracle Warehouse Builder to extract, transform and load the source data into the Data Warehouse, by applying the KDD process. We have implemented the Data Warehouse as an Oracle OLAP. The knowledge extraction has been performed using the Oracle Discoverer tool. This allowed users to take maximum advantage of knowledge as a regular report or as ad hoc queries. We started by implementing the main topic for this public institution, accounting for the movements of insured persons. The great success that has followed the completion of this work has encouraged the NSSF to complete the achievement of other topics of interest within the NSSF. We suggest in the near future to use Multidimensional Data Mining to extract hidden knowledge and that are not predictable by the OLAP.