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As more and more data is available through the Web, mediation of information from multiple autonomous and heterogeneous data sources becomes a crucial task for future Web information systems. We describe the features of our mediation framework WASSIT (frameWork d'intégrAtion de reSSources de données par la médIaTion), which provides the(More)
Nowadays, e-government has emerged as a government policy to improve the quality and efficiency of public administrations. By exploiting the potential of new information and communication technologies, government agencies are providing a wide spectrum of online services. These services are composed of several web services that comply with well defined(More)
Missing data is an inevitable problem in wireless sensor network and the way missing values are handled can significantly affect the analysis results involving such data. To address data missing issues, spatial correlation and temporal correlation modeling can be applied. This paper aims at reviewing some popular spatial and temporal correlation based(More)
Date sources integration is one of the major challenges for nowadays enterprise information system. Indeed, because of growing heterogeneous systems, access to information is more and more complex. In this context, we have implemented a prototype of WASSIT, a data integration system based on mediation-adaptation architecture. This paper briefly describes(More)
The development of an efficient e-Government for offering information and electronic services (e-services) to citizen and enterprise is the main objective of many Governments. However, the Public administrations (PA) are characterized by their heterogeneous and distributed environment, which make their cooperation and integration under the umbrella of(More)
A smart home is a building in which ubiquitous computing and information technology are deployed to expect and respond to the occupants' needs and to enhance their everyday's life. To achieve this goal, smart homes rely on Wireless Sensor Networks (WSN) for collecting all kind of personal data. Nevertheless, information privacy is one of the most sensitive(More)
Feature selection is an important step in many Machine Learning classification problems. It reduces the dimensionality of the feature space by removing noisy, irrelevant and redundant data, such that classification accuracy is enhanced while computational time remains affordable. In this paper, we present a new wrapper feature subset selection model based(More)