%0 Conference Paper %1 Diaz-Aviles:2013:LAM:2487788.2488169 %A Diaz-Aviles, Ernesto %B Proceedings of the 22nd international conference on World Wide Web companion %C Republic and Canton of Geneva, Switzerland %D 2013 %I International World Wide Web Conferences Steering Committee %K 2013 L3S WWW myown vedax web_observatory %P 1321--1324 %T Living analytics methods for the web observatory %U http://dl.acm.org/citation.cfm?id=2487788.2488169 %X The collective effervescence of social media production has been enjoying a great deal of success in recent years. The hundred of millions of users who are actively participating in the Social Web are exposed to ever-growing amounts of sites, relationships, and information. In this paper, we report part of the efforts towards the realization of a Web Observatory at the L3S Research Center (www.L3S.de). In particular, we present our approach based on Living Analytics methods, whose main goal is to capture people interactions in real-time and to analyze multidimensional relationships, metadata, and other data becoming ubiquitous in the social web, in order to discover the most relevant and attractive information to support observation, understanding and analysis of the Web. We center the discussion on two areas: (i) Recommender Systems for Big Fast Data and (ii) Collective Intelligence, both key components towards an analytics toolbox for our Web Observatory. %@ 978-1-4503-2038-2

@inproceedings{Diaz-Aviles:2013:LAM:2487788.2488169, abstract = {The collective effervescence of social media production has been enjoying a great deal of success in recent years. The hundred of millions of users who are actively participating in the Social Web are exposed to ever-growing amounts of sites, relationships, and information. In this paper, we report part of the efforts towards the realization of a Web Observatory at the L3S Research Center (www.L3S.de). In particular, we present our approach based on Living Analytics methods, whose main goal is to capture people interactions in real-time and to analyze multidimensional relationships, metadata, and other data becoming ubiquitous in the social web, in order to discover the most relevant and attractive information to support observation, understanding and analysis of the Web. We center the discussion on two areas: (i) Recommender Systems for Big Fast Data and (ii) Collective Intelligence, both key components towards an analytics toolbox for our Web Observatory.}, acmid = {2488169}, added-at = {2013-10-13T22:02:04.000+0200}, address = {Republic and Canton of Geneva, Switzerland}, author = {Diaz-Aviles, Ernesto}, biburl = {https://www.bibsonomy.org/bibtex/289734ec782109468aecedbba4ef9cd6c/diaz.l3s.de}, booktitle = {Proceedings of the 22nd international conference on World Wide Web companion}, description = {Living analytics methods for the web observatory}, interhash = {70a5a9552e86cd40c8348f81861de78e}, intrahash = {89734ec782109468aecedbba4ef9cd6c}, isbn = {978-1-4503-2038-2}, keywords = {2013 L3S WWW myown vedax web_observatory}, location = {Rio de Janeiro, Brazil}, numpages = {4}, pages = {1321--1324}, publisher = {International World Wide Web Conferences Steering Committee}, series = {WWW '13 Companion}, timestamp = {2013-12-26T16:45:15.000+0100}, title = {Living analytics methods for the web observatory}, url = {http://dl.acm.org/citation.cfm?id=2487788.2488169}, year = 2013 }