Mining and Linking Patterns across Live Data Streams and Stream Archives

نویسندگان

  • Di Yang
  • Kaiyu Zhao
  • Maryam Hasan
  • Hanyuan Lu
  • Elke A. Rundensteiner
  • Matthew O. Ward
چکیده

We will demonstrate the visual analytics system V istream , 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 istream , 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 neighbourbased 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.

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عنوان ژورنال:
  • PVLDB

دوره 6  شماره 

صفحات  -

تاریخ انتشار 2013