ABSTRACT
Recently, the vast dialog in the microblog, such as twitter, Facebook has become increasingly popular. As we post more messages in microblogs, information is spreading more quickly and widely. These widely spread and diversified contents could be viewed as data streams, which have become an important part of the Internet resources. However, these separated data streams are littery and meaningless, so we need to collect and organize them together to provide us with meaningful information. It is hard to imagine that we could find useful information by simply inputting a few keywords into a search engine in such a stream environment. In this study, we try to find a way to seek the information related to users' personal and current interests and needs among these data streams and provide users with other more relevant information. We introduce a set of metaphors to represent a variety of data streams in different levels, and define two new metaphors: heuristic stone and associative ripple to assist the seeking process and describe the results. Based on these, we further propose two algorithms for the information seeking and processing, and discuss a scenario of the information seeking process that utilizes the proposed metaphors and algorithms.
- Breslin J. G., et. al. Towards semantically interlinked online communities. In Proceedings of ESWC2005, Heraklion, Greece, 500--514, 2005. Google ScholarDigital Library
- Bojars, U, et. al. Using the Semantic Web for Linking and Reusing Data Across Web 2.0 Communities. The Journal of Web Semantics, 6, 1 (Feb. 2008), 21--28. Google ScholarDigital Library
- Passant, A., et. al. Micro-blogging: A semantic web and distributed approach. In Proceedings of ESWC/SFSW2008, Tenerife, Spain, 2008.Google Scholar
- Reinhardt, W., et. al. How people are using Twitter during conferences. In Proceedings of 5th EduMedia conference, Salzburg, Austria, 2009, 145--156.Google Scholar
- Ebner, M., et al. Microblogs in higher education---a chance to facilitate informal and process-oriented learning? Computers & Education, 55, 1 (2010), 92--100,. Google ScholarDigital Library
- Passant A., et. al. An Overview of SMOB 2: Open, Semantic and Distributed Micro-blogging. In Proceedings of AAAI/ICWSM 2010.Google Scholar
- Mohamed Medhat Gaber, Arkady Zaslavsky and Shonali Krishnaswamy. Mining data streams: a review. ACM SIGMOD Record archive, 34, 2 (Jun. 2005), 18--26. Google ScholarDigital Library
- Aggarwal C., Han J., Wang J., Yu P. S. A Framework for Clustering Evolving Data Streams. In Proceedings of 2003 Int. Conf. on Very Large Data Bases, Berlin, Germany, Sept. 2003, 81--92. Google ScholarDigital Library
- Guha S., Mishra N., Motwani R., and O'Callaghan L. Clustering data streams. In Proceedings of the Annual Symposium on Foundations of Computer Science, California, USA, Nov. 2000. Google ScholarDigital Library
- Guha S., Meyerson A., Mishra N., Motwani R., and O'Callaghan L. Clustering Data Streams: Theory and Practice TKDE special issue on clustering, 15, 3 (May/Jun. 2003), 515--528. Google ScholarDigital Library
- Ordonez C. Clustering Binary Data Streams with K-means In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, ACM, San Diego, California, USA, 2003, 12--19. Google ScholarDigital Library
- Della Valle, E., et al. A First Step towards Stream Reasoning, In Proceedings of Future Internet Symp. (FIS2008), Cardiff, Wales, UK, May 2008, 72--81. Google ScholarDigital Library
- Della Valle, E., et al. It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems, 24, 6 (Nov./Dec. 2009), 83--89. Google ScholarDigital Library
- Chen H., Zhou X. K., Man H. F., Wu Y., Ahmed A. U. and Jin Q. A Framework of Organic Streams: Integrating Dynamically Diversified Contents into Ubiquitous Personal Study, In Proceedings of 2010 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing (ATC-UIC 2010), Xi'an, China, Oct. 2010. Google ScholarDigital Library
- Kanaegami A., Koike K, Taki H., Ohgashi H. Text Search System for Locating on the Basis of Keyword Matching and Keyword Relationship Matching, US Patent 5,297,039, 1994, Google PatentsGoogle Scholar
Index Terms
- Generating associative ripples of relevant information from a variety of data streams by throwing a heuristic stone
Recommendations
Enriching user search experience by mining social streams with heuristic stones and associative ripples
Recently, social networking sites such as Facebook and Twitter are becoming increasingly popular. The high accessibility of these sites has allowed the so-called social streams being spread across the Internet more quickly and widely, as more and more ...
A heuristic approach to discovering user correlations from organized social stream data
Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling, experience and knowledge with each other on Internet. The high accessibility of these web sites ...
Socialized ubiquitous personal study: Toward an individualized information portal
Recently, SNS (Social Network Service), blog and microblog have become very popular. Stream data, a large collection of diverse contents that are created dynamically in the form of streams, have become an important part of the Internet resources. At the ...
Comments