LEMPEM

Web Graph: Learning Models for Prediction and Evolution Monitoring

Coordinatore	ATHENA RESEARCH AND INNOVATION CENTER IN INFORMATION COMMUNICATION & KNOWLEDGE TECHNOLOGIES    more  Organization address address: ARTEMIDOS 6 KAI EPIDAVROU city: MAROUSSI postcode: 151 25 contact info Titolo: Ms. Nome: Nelly Cognome: Apostolopoulou Email: send email Telefono: -6990702 Fax: -6990732
Nazionalità Coordinatore	Greece [EL]
Totale costo	45˙000 €
EC contributo	45˙000 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2007-2-2-ERG
Funding Scheme	MC-ERG
Anno di inizio	2008
Periodo (anno-mese-giorno)	2008-04-01   -   2011-03-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	ATHENA RESEARCH AND INNOVATION CENTER IN INFORMATION COMMUNICATION & KNOWLEDGE TECHNOLOGIES  Organization address address: ARTEMIDOS 6 KAI EPIDAVROU city: MAROUSSI postcode: 151 25 contact info Titolo: Ms. Nome: Nelly Cognome: Apostolopoulou Email: send email Telefono: -6990702 Fax: -6990732	EL (MAROUSSI)	coordinator	0.00

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 Obiettivo del progetto (Objective)

'Data intensive systems flourish in the last decades with an ever increasing rate of data production. A characteristic such case is the Web graph. Given the dynamism of the Web we aim to study the Web graph in terms of learning models and monitoring its evolution. The main problems we study are: • identification of trends and patterns in the web graph, using the spectral properties of the evolving web adjacency matrix. • monitoring of web pages’ ranking over time, and prediction of pages web ranking. • learn models for the evolving web graph with statistical learning techniques. The results of the proposed research will be a framework of approaches and algorithms that will enable effective and efficient: - Query based top-k list predictions (future and historical ones) - Prediction based crawling: based on our ranking predictive modeling, crawling resources can be optimized maintaining at the same time a satisfactory top-k quality All the above are profoundly beneficial for resource management in the context of large scale Web search, and the added value of the above will be the potential use of these techniques by the Web search industry.'

Introduzione (Teaser)

With the explosion of Web-based information, there is an ever-increasing need for methods that more quickly and accurately help users get the information they want and need. An EU-funded initiative has resulted in innovative advances in data collection techniques for use in Web searches, social networking and data mining for automated Web marketing, among other applications.