Henda Ben Ghezala

Publications

1 Improving Spatiotemporal Change Detection: A High Level Fusion Approach for Discovering Uncertain Knowledge from Satellite Image Database

Authors: Wadii Boulila, Imed Riadh Farah, Karim Saheb Ettabaa, Basel Solaiman, Henda Ben Ghezala

Abstract:

This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.

Keywords: Data Mining, Knowledge discovery in satellite databases, knowledge fusion, data imperfection, spatiotemporal change detection

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Abstracts

2 Extracting Actions with Improved Part of Speech Tagging for Social Networking Texts

Authors: Henda Ben Ghezala, Yassine Jamoussi, Ameni Youssfi

Abstract:

With the growing interest in social networking, the interaction of social actors evolved to a source of knowledge in which it becomes possible to perform context aware-reasoning. The information extraction from social networking especially Twitter and Facebook is one of the problems in this area. To extract text from social networking, we need several lexical features and large scale word clustering. We attempt to expand existing tokenizer and to develop our own tagger in order to support the incorrect words currently in existence in Facebook and Twitter. Our goal in this work is to benefit from the lexical features developed for Twitter and online conversational text in previous works, and to develop an extraction model for constructing a huge knowledge based on actions

Keywords: Social Networking, natural language processing, information extraction, part-of-speech tagging

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1 Survey of Web Service Composition

Authors: Henda Ben Ghezala, Khaled Ghedira, Wala Ben Messaoud, Youssef Ben Halima

Abstract:

A web service (WS) is called compound or composite when its execution involves interactions with other WS to use their features. The composition of WS specifies which services need to be invoked, in what order and how to handle exception conditions. This paper gives an overview of research efforts of WS composition. The approaches proposed in the literature are diverse, interesting and have opened important research areas. Based on many studies, we extracted the most important role of WS composition use in order to facilitate its introduction in WS concept.

Keywords: Web services, SOA, composition approach, composite WS

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