%0 Journal Article %1 citeulike:1593118 %A Dubinko, Micah %A Kumar, Ravi %A Magnani, Joseph %A Novak, Jasmine %A Raghavan, Prabhakar %A Tomkins, Andrew %C New York, NY, USA %D 2007 %I ACM %J ACM Trans. Web %K tagging visualization %N 2 %P 7+ %R 10.1145/1255438.1255439 %T Visualizing tags over time %U http://dx.doi.org/10.1145/1255438.1255439 %V 1 %X We consider the problem of visualizing the evolution of tags within the Flickr (flickr.com) online image sharing community. Any user of the Flickr service may append a tag to any photo in the system. Over the past year, users have on average added over a million tags each week. Understanding the evolution of these tags over time is therefore a challenging task. We present a new approach based on a characterization of the most interesting tags associated with a sliding interval of time. An animation provided via Flash in a Web browser allows the user to observe and interact with the interesting tags as they evolve over time. New algorithms and data structures are required to support the efficient generation of this visualization. We combine a novel solution to an interval covering problem with extensions to previous work on score aggregation in order to create an efficient backend system capable of producing visualizations at arbitrary scales on this large dataset in real time.

@article{citeulike:1593118, abstract = {{We consider the problem of visualizing the evolution of tags within the Flickr (flickr.com) online image sharing community. Any user of the Flickr service may append a tag to any photo in the system. Over the past year, users have on average added over a million tags each week. Understanding the evolution of these tags over time is therefore a challenging task. We present a new approach based on a characterization of the most interesting tags associated with a sliding interval of time. An animation provided via Flash in a Web browser allows the user to observe and interact with the interesting tags as they evolve over time. New algorithms and data structures are required to support the efficient generation of this visualization. We combine a novel solution to an interval covering problem with extensions to previous work on score aggregation in order to create an efficient backend system capable of producing visualizations at arbitrary scales on this large dataset in real time.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Dubinko, Micah and Kumar, Ravi and Magnani, Joseph and Novak, Jasmine and Raghavan, Prabhakar and Tomkins, Andrew}, biburl = {https://www.bibsonomy.org/bibtex/2a865bb2d689e7e912f089d8d16ab5072/aho}, citeulike-article-id = {1593118}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1255438.1255439}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1255438.1255439}, doi = {10.1145/1255438.1255439}, interhash = {056517255326f2f00d8075e5f3317e3c}, intrahash = {a865bb2d689e7e912f089d8d16ab5072}, issn = {1559-1131}, journal = {ACM Trans. Web}, keywords = {tagging visualization}, month = aug, number = 2, pages = {7+}, posted-at = {2010-06-03 23:07:09}, priority = {2}, publisher = {ACM}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Visualizing tags over time}}, url = {http://dx.doi.org/10.1145/1255438.1255439}, volume = 1, year = 2007 }