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 diseaseoutbreaks, 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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