Window-based tensor analysis on high-dimensional and multi-aspect streams

Jimeng Sun, Spiros Papadimitriou, Philip S. Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, tensor streams, and an effective algorithmic framework, window-based tensor analysis (WTA). Two variations of WTA, independentwindow tensor analysis (TW) and moving-window tensor analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, Multi-Aspect Correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Pages1076-1080
Number of pages5
DOIs
StatePublished - Dec 1 2006
Externally publishedYes
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: Dec 18 2006Dec 22 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other6th International Conference on Data Mining, ICDM 2006
Country/TerritoryChina
CityHong Kong
Period12/18/0612/22/06

ASJC Scopus subject areas

  • Engineering(all)

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