Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting distance-based outliers in streams of data
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Neighbor-based pattern detection for windows over streaming data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A shared execution strategy for multiple pattern mining requests over streaming data
Proceedings of the VLDB Endowment
Interactive visual exploration of neighbor-based patterns in data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
CLUES: a unified framework supporting interactive exploration of density-based clusters in streams
Proceedings of the 20th ACM international conference on Information and knowledge management
Summarization and matching of density-based clusters in streaming environments
Proceedings of the VLDB Endowment
Shared execution strategy for neighbor-based pattern mining requests over streaming windows
ACM Transactions on Database Systems (TODS)
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We will demonstrate the visual analytics system V istreamT, that supports interactive mining of complex patterns within and across live data streams and stream pattern archives. Our system is equipped with both computational pattern mining and visualization techniques, which allow it to not only efficiently discover and manage patterns but also effectively convey the mining results to human analysts through visual displays. In our demonstration, we will illustrate that with V istreamT, analysts can easily submit, monitor and interact with a broad range of query types for pattern mining. This includes novel strategies for extracting complex patterns from streams in real time, summarizing neighbour-based patterns using multi-resolution compression strategies, selectively pushing patterns into the stream archive, validating the popularity or rarity of stream patterns by stream archive matching, and pattern evolution tracking to link patterns across time.