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.
- 15th International World Wide Web Conference. 2006. Proceedings of the Collaborative Web Tagging Workshop. Available online at http://www.rawsugar.com/www2006/taggingworkshopschedule.html.Google Scholar
- Allan, J., Carbonell, J., Doddington, G., Yamron, J., and Yang, Y. 1998. Topic detection and tracking pilot study: Final report. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop. Defence Advanced Research Projects Agency, Arlington, VA. Google Scholar
- Chien, S. and Immorlica, N. 2005. Semantic similarities between search engine queries using temporal correlation. In Proceedings of the 14th International Conference on World Wide Web. ACM, New York, NY, 2--11. Google Scholar
- Fagin, R., Lotem, A., and Naor, M. 2003. Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66, 4, 614--656. Google Scholar
- Golder, S. and Huberman, B. 2006. The structure of collaborative tagging systems. J. Inform. Sci. 32, 198--208. Google Scholar
- Guttman, A. 1984. R-trees: A dynamic index structure for spatial searching. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, New York, NY, 47--57. Google Scholar
- Hadjieleftheriou, M., Kollios, G., Tsotras, V. J., and Gunopulos, D. 2006. Indexing spatiotemporal archives. VLDB J. 15, 2, 143--164. Google Scholar
- Havre, S., Hetzler, B., and Nowell, L. 2002. ThemeRiver: Visualzing thematic changes in large document collections. IEEE Trans. Visualiz. Comput. Graph. 8, 1, 9--20. Google Scholar
- Kleinberg, J. 2003. Bursty and hierarchical structure in streams. Data Min. Knowl. Disc. 7, 4, 373--397. Google Scholar
- Kleinberg, J. 2006. Temporal dynamics of on-line information systems. In Data Stream Management: Processing High-Speed Data Streams, M. Garofalakis, J. Gehrke, and R. Rastogi, Eds. Springer-Verlag, New York, NY.Google Scholar
- Korth, H., Silberschatz, A., and Sudarshan, S. 2005. Database System Concepts, 5th ed. McGraw-Hill, New York, NY. Google Scholar
- Kumar, R., Novak, J., Raghavan, P., and Tomkins, A. 2005. On the bursty evolution of blogspace. World Wide Web 8, 2, 159--178. Google Scholar
- Lin, J., Keogh, E. J., Lonardi, S., Lankford, J. P., and Nystrom, D. M. 2004. VizTree: A tool for visually mining and monitoring massive time series databases. In Proceedings of International Conference on Very Large Data Bases. Morgan Kaufmann, San Francisco, CA, 1269--1272. Google Scholar
- Milash, B., Plaisant, C., and Rose, A. 1996. Lifelines: Visualizing personal histories. In Proceedings of the International Conference Companion on Human Factors in Computing Systems. ACM, New York, NY, 392--393. Google Scholar
- Millen, D., Feinberg, J., and Kerr, B. 2005. Social bookmarking in the enterprise. ACM Queue 3, 9, 28--35. Google Scholar
- Robertson, S. and Walker, S. 2000. Okapi/Keenbow at Trec-8. In Proceedings of the 8th Text Retrieval Conference. National Institute of Standards and Technology, Gaithersburg, MD, 151--161.Google Scholar
- Roth, M. T., Arya, M., Haas, L., Carey, M., Cody, W., Fagin, R., Schwarz, P., Thomas, J., and Wimmers, E. 1996. The Garlic project. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, New York, NY, 557. Google Scholar
- Saltenis, S., Jensen, C., Leutenegger, S., and Lopez, M. A. 2000. Indexing the positions of continuously moving objects. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, New York, NY, 331--342. Google Scholar
- Shneiderman, B. 1992. Tree visualization with tree-maps: 2-D space-filling approach. ACM Trans. Graph. 11, 1, 92--99. Google Scholar
- Vlachos, M., Meek, C., Vagena, Z., and Gunopulos, D. 2004. Identifying similarities, periodicities, and bursts for online search queries. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, New York, NY, 131--142. Google Scholar
- Wattenberg, M. 1998. A map of the market. Available online at http://www.smartmoney.com/marketmap.Google Scholar
- Wattenberg, M. 2001. Shape of song. Available online at http://turbulence.org/works/song.Google Scholar
- Witten, I., Moffat, A., and Bell, T. 1999. Managing Gigabytes, 2nd ed. Morgan Kaufmann, San Francisco, CA. Google Scholar
- Yang, Y., Pierce, T., and Carbonell, J. 1998. A study on retrospective and on-line event detection. In Proceedings of the 21st Annual International ACM Conference on Research and Development in Information Retrieval. ACM, New York, NY, 28--36. Google Scholar
Index Terms
Visualizing tags over time
Recommendations
Visualizing tags over time
WWW '06: Proceedings of the 15th international conference on World Wide WebWe 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 ...
Visualizing Tags with Spatiotemporal References
IV '11: Proceedings of the 2011 15th International Conference on Information VisualisationNowadays, a great amount of data is created and distributed on the Internet. Tagging has become common practice to structure these data for easy access. Often the data and the associated tags contain spatial and temporal information. In this paper, we ...
Extracting Representative Tags for Flickr Users
ICDMW '10: Proceedings of the 2010 IEEE International Conference on Data Mining WorkshopsTags are very popular in online social communities (like You tube, Flickr) and provide valuable and crucial information for these communities. But at the same time, there exist a lot of noisy tags, which leads many researches to tag suggestion, tag ...






Comments