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Incremental pattern discovery on str...

Sun, Jimeng.

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Incremental pattern discovery on streams, graphs and tensors.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Incremental pattern discovery on streams, graphs and tensors./
作者:	Sun, Jimeng.
面頁冊數:	179 p.
附註:	Adviser: Christos Faloutsos.
Contained By:	Dissertation Abstracts International68-12B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3292615
ISBN:	9780549360414

Incremental pattern discovery on streams, graphs and tensors.
Sun, Jimeng.

Incremental pattern discovery on streams, graphs and tensors. - 179 p.

Adviser: Christos Faloutsos.

Thesis (Ph.D.)--Carnegie Mellon University, 2007.

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?

ISBN: 9780549360414Subjects--Topical Terms:

626642
Computer Science.

Incremental pattern discovery on streams, graphs and tensors.
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520 $a 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?
520 $a As examples, (1) a sensor network monitors a large number of distributed streams (such as temperature and humidity); (2) network forensics monitor the Internet communication patterns to identify attacks; (3) cluster monitoring examines the system behaviors of a number of machines for potential failures; (4) social network analysis monitors a dynamic graph for communities and abnormal individuals; (5) financial fraud detection tries to find fraudulent activities from a large number of transactions.
520 $a 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 for Google over time), a first-order TS for multiple time-series (sensor measurement streams), a second-order TS for a matrix (graphs), and a high-order TS for a multiarray (Internet communication network, source-destination-port). Second, we develop different online algorithms on TS: (1) the centralized and distributed SPIRIT for mining a 1st-order TS, as well as its extensions for local correlation function and privacy preservation; (2) the compact matrix decomposition (CMD) and GraphScope for a 2nd-order TS; (3) the dynamic tensor analysis (DTA), 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.
520 $a In particular, this CMD achieves orders of magnitude improvements in space and time over the previous state of the art, and identifies interesting anomalies. GraphScope detects interesting communities and change-points on several time-evolving graphs such as Enron email graph and another network traffic flow graph. DTA, STA and WTA are all online methods for higherorder data that scale well with time, provide fundamental tradeoffs with each other, which have also been applied to a number of applications, such as social network community tracking, anomaly detection in data centers and network traffic monitoring.
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