Incremental pattern discovery on streams, graphs and tensors

13 years 2 months ago
Incremental pattern discovery on streams, graphs and tensors
Incremental pattern discovery targets streaming applications where the data continuously arrive incrementally. The questions are how to find patterns (main trends) incrementally; or how to efficiently update the old patterns when new data arrive; or how to utilize the patterns to solve other problems such as anomaly detection? We first investigate a powerful data model, tensor stream (TS), where there is one tensor per timestamp. To capture diverse data formats, we have a zero-order TS for a single time-series (e.g., the stock price over time), a first-order TS for multiple time-series (sensor measurement streams), a second-order TS for matrices (graphs), and a high-order TS for multi-arrays (Internet communication network, source-destination-port). Second, we develop different online algorithms on TS: 1) the centralized and distributed SPIRIT [7] for mining a 1st-order TS, as well as its extensions for local correlation function and privacy preservation; 2) the compact matrix decompo...
Jimeng Sun
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Authors Jimeng Sun
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