Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
ACM SIGMOD Record
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Using the Semantic Web for linking and reusing data across Web 2.0 communities
Web Semantics: Science, Services and Agents on the World Wide Web
A First Step Towards Stream Reasoning
Future Internet --- FIS 2008
It's a Streaming World! Reasoning upon Rapidly Changing Information
IEEE Intelligent Systems
UIC-ATC '10 Proceedings of the 2010 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing
Towards semantically-interlinked online communities
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Evaluating search in personal social media collections
Proceedings of the fifth ACM international conference on Web search and data mining
Dynamical user networking and profiling based on activity streams for enhanced social learning
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
Socialized ubiquitous personal study: Toward an individualized information portal
Journal of Computer and System Sciences
Learning activity sharing and individualized recommendation based on dynamical correlation discovery
ICWL'12 Proceedings of the 11th international conference on Advances in Web-Based Learning
User correlation discovery and dynamical profiling based on social streams
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Multimedia Tools and Applications
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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.