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Laboratoire d'InfoRmatique en Image et Systèmes d'information

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Laboratoire d'InfoRmatique en Image et Systèmes d'information
UMR 5205 CNRS / INSA Lyon / Université Claude Bernard Lyon 1 / Université Lumière Lyon 2 / École Centrale de Lyon
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Asma Omri

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PhD student

Team Service Oriented Computing
 
Institution Claude Bernard University of Lyon 1
Location Nautibus (Université Lyon1)
 
E-mail asma.omri at liris.cnrs.fr
Contact details Publications Thesis
 
Subject Management approach of services, web resources and indexing in a context of uncertainty
Abstract In recent years, there has been a huge explosion in data production due to the proliferation of new technologies. This data explosion should continue and even accelerate. As data is becoming more varied, more complex and less structured, it has become imperative to process them quickly. In this thesis, we will focus on the uncertainty of data which is the origin of several reasons such as the integration of different data sources, data fusion by adapting a possibilistic or probabilistic approach.
This thesis focuses on the problem of web services in a context of distributed and heterogeneous systems. The main contribution to make in this thesis is to study the composition of services and / or web resources in a context of presence of uncertain information. Three main objectives are targeted: At first, this thesis will focus on the definition of the concept of uncertain web service by adopting the theory of possibilities. This will introduce the concept of possibilistic services and adapt service description standards to this concept.
This thesis also aims to study the impact of uncertainty on the representation and manipulation of web resources. It is therefore expected to define the concept of uncertain web resource and will offer mechanisms of composition of resources. This part includes: (1) A model of representation of uncertain resources. (2) A "Parser" that describes a programming language for these resources. (3) An Uncertain GET algorithm.
And finally, this thesis deals with the proposal of a method of indexing Big Data. This approach has three main phases: (1) An approach of syntactic indexing of uncertain Big Data. (2) A semantic indexing approach to Big Data in the presence of uncertain data. (3) A hybrid approach that indexes uncertain data.
Advisor Djamal Benslimane
Advisor Karim Benouaret

Last update : 2018-01-16 15:25:19