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...

• Type: Journal
• Year: 2008
• Where: SIGKDD
• Authors: Jimeng Sun
• Keywords: SIGKDD 2008, Tensor, Tensor Analysis, Zero-order Ts