Sharaf, Mohamed
(2007)
Metrics and Algorithms for Processing Multiple Continuous Queries.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
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 BaseManagement 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 willhave 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 ofservice (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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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
27 September 2007 |
| Date Type: |
Completion |
| Defense Date: |
22 June 2007 |
| Approval Date: |
27 September 2007 |
| Submission Date: |
9 August 2007 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Computer Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
algorithms; continuous queries; data streams; databases; scheduling |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-08092007-134505/, etd-08092007-134505 |
| Date Deposited: |
10 Nov 2011 19:58 |
| Last Modified: |
15 Nov 2016 13:48 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/9019 |
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