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A flexible data mining architecture for monitoring data streams

Başlık çevirisi mevcut değil.

  1. Tez No: 400335
  2. Yazar: AHMET BULUT
  3. Danışmanlar: AMBUJ K. SINGH
  4. Tez Türü: Doktora
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2005
  8. Dil: İngilizce
  9. Üniversite: Unıversıty Of Calıfornıa Santa Barbara
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Bilgisayar Bilimleri Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

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Özet (Çeviri)

Data streams are ubiquitous: performance measurements in business process management,faults and alarms in network traffic management, transactions in retailchains, ATM operations in banks, log records generated by web servers, and sensornetwork data are some specific examples. In almost all of these applications,the data volume is massive, up to several terabytes. Data volume increases evenfurther with the rapid arrival of new tuples. Traditional DBMS?s are ill-equippedfor processing of data streams in real time, and do not provide adequate support forhandling continuous queries posed over these streams.This dissertation outlines models and issues towards designing an efficient DataStream Management System (DSMS) called Stardust. The system can handle adiverse set of continuous queries that fit naturally into the mold of data stream applications.We developed wavelet-based approximation schemes that maintain multiplelevels of information over streams of data in order to answer queries efficiently.In centralized DSMS models, a stream is summarized at a central site, and alluser queries are processed at this site. In data and query intensive environments, thecentral site can become a bottleneck. As a remedy to this problem, we developedadaptive replication algorithms for dissemination of stream summaries computed ata central site to interested clients. We tested the distributed version of the systemon a number of testbeds. In the first scenario, Stardust exploits the scalability andload balancing of communication provided by content-based routing schemes for efficientdistributed stream processing. In the second scenario, we integrated Stardustinto a real-time decision support system for nondestructive health monitoring using awireless network of sensors. The system trades off accuracy for efficient processingof sensor data in order to save the communication overhead and power-consumption.Finally, we built an event detection framework for monitoring a set of distributednetwork elements. The goal is to detect potentially interesting incidents specifiedby users in terms of a multitude of race conditions across a set of routers whilemaintaining a low monitoring overhead.

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