Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
InteMon: continuous mining of sensor data in large-scale self-infrastructures
ACM SIGOPS Operating Systems Review
Large Scale Detection of Irregularities in Accounting Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed pattern discovery in multiple streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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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 decomposition (CMD) [5] and GraphScope [4] for a 2nd-order TS; 3) the dynamic tensor analysis (DTA) [2], streaming tensor analysis (STA) and window-based tensor analysis (WTA) for a high-order TS. All the techniques are extensively evaluated for real applications such as network forensics, cluster monitoring.