Bassem Barkallah’s scientific contributions

The emergence of large scale systems provides solutions and poses new challenges to a number of existing problems covering a broad spectrum of applications. Among these problems, we are interested by exploring new approaches to integrate data in large scale systems. For this purpose, we propose a data integration framework that is data-structure-independent, allowing a high level of semantic interoperability. We describe the methods used in our framework to build an index regrouping all the clusters published data descriptions that are designed to fit the structure of a large scale system. This framework focuses on the properties of large scale systems such as multi-level heterogeneity and high degree of dynamicity and evolution.

Several scientific projects focused on the creation of Peer-to-Peer data management system. The main objective of these systems is to allow data sharing and integration among a large set of distributed, heterogeneous data sources. The emergence of large scale systems provides solutions and brings to surface new challenging unsolved problems, among which, we address the data integration problem. In order to address this problem, we propose a new data integration approach that allows the semantic integration of heterogeneous and distributed data sources in a Peer-to-Peer environment with high distribution and evolution support. In this paper, we provide an introduction to the approaches; problems and research issues encountered when dealing with data integration.We present our approach and describe the several methods for constructing a global index that is the core of our approach by using semantic similarities. We end our work by an application example.