Abstract: Online social networks play a major role in the spread of information at very large scale. A lot of efforts have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.

Abstract: The construction of the ETL (Extract-Transform-Load) process is one of the biggest tasks of building a warehouse. ETL processes area has little research, because of its difficulty and lack of formal model for representing ETL activities that map the incoming data from different sources to be in a suitable format for loading into the warehouse. A main problem in data warehousing of multidimensional text databases is to deal with the content in its text cells. In this paper, we propose a model for textual data warehouse ETL processes called ETL-Text. It combines classical data warehousing tasks, information retrieval (IR) techniques, and information processing in particular the language modeling. Our approach is based on Wikipedia as external knowledge source to extract the semantics of the textual documents. To validate our approach, we develop a prototype composed of several processing modules that illustrate the different ETL-Text processes. Also, we use the 20 Newsgroups corpus to perform our experimentation.

Abstract: The objective of the thesis is to explore how complex data can be treated using unsupervised machine learning techniques, in which additional information is injected to guide the exploratory process. Starting from specific problems, our contributions take into account the different dimensions of the complex data: their nature (image, text), the additional information attached to the data (labels, structure, concept ontologies) and the temporal dimension. A special attention is given to data representation and how additional information can be leveraged to improve this representation.

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