Title page for ETD etd-08092007-134505 ( Browse

Type of Document

Dissertation

Author

Sharaf, Mohamed

URN

etd-08092007-134505

Title

Metrics and Algorithms for Processing Multiple Continuous Queries

Degree

Doctor of Philosophy

Program

Computer Science

School

School of Arts and Sciences

Advisory Committee

Advisor Name	Title
Panos K. Chrysanthis	Committee Chair
Alexandros Labrinidis	Committee Co-Chair
Christos Faloutsos	Committee Member
Kirk Pruhs	Committee Member
Walid Aref	Committee Member

Keywords

algorithms
databases
scheduling
data streams
continuous queries

Date of Defense

2007-06-22

Availability

unrestricted

Abstract

Data streams processing is an emerging research area that is driven by the growing need for monitoring applications. A monitoring application continuously processes streams of data for interesting, significant, or anomalous events. Such applications include tracking the stock market, real-time detection of disease
outbreaks, and environmental monitoring via sensor networks.

Efficient employment of those monitoring applications requires advanced data processing techniques that can support the continuous processing of unbounded rapid data streams. Such techniques go beyond the capabilities of the traditional store-then-query Data Base
Management Systems. This need has led to a new data processing paradigm and created a new generation of data processing systems,
supporting continuous queries (CQ) on data streams.

Primary emphasis in the development of first generation Data Stream Management Systems (DSMSs) was given to basic functionality. However, in order to support large-scale heterogeneous applications that are envisioned for subsequent generations of DSMSs, greater attention will
have to be paid to performance issues. Towards this, this thesis introduces new algorithms and metrics to the current design of DSMSs.

This thesis identifies a collection of quality of
service (QoS) and quality of data (QoD) metrics that are suitable for a wide range of monitoring applications. The establishment of well-defined metrics aids in the development of novel algorithms that are optimal with respect to a particular metric.

Our proposed algorithms exploit the valuable chances for optimization that arise in the presence of multiple applications. Additionally, they aim to balance the trade-off between the DSMS's overall performance and the performance perceived by individual applications. Furthermore, we provide efficient implementations of the proposed algorithms and we also extend them to exploit sharing in optimized multi-query plans and multi-stream CQs. Finally, we experimentally show that our algorithms consistently outperform the current state of the art.

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