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Call No.	Ph.D. 2008
Author	Li, Ming, 1974-
Title	Group-aware stream filtering.
Date	2008
Abstract	Recent years have witnessed a new class of monitoring applications that need to continuously collect information from remote data sources. Those data sources, such as web click-streams, stock quotes, and sensor data, are often characterized as fast-rate high-volume "streams". Distributed stream-processing systems are thus designed to efficiently use system resources to serve the data-acquisition needs of the applications. Most of the state-of-the-art stream-processing systems assume an Ethernet-based network whose bandwidth is abundant, and focus on mechanisms to save computational power and memory. For applications involving wireless networks, particularly multi-hop mesh networks, we recognize that the most limiting factor in efficiently processing streams lies in the network's highly constrained bandwidth. Hence, this dissertation proposes a group-aware stream filtering approach that saves bandwidth at the cost of increased CPU time, for low-bandwidth data-streaming systems. This approach, used together with multicasting, exploits two overlooked properties of monitoring applications: (1) many of them can tolerate some degree of "slack" in their data quality requirements, and (2) there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the "best alternative" subset for each application to maximize the data overlap within the group to best benefit from multicasting. After proving the problem NP-hard, we introduce a suite of heuristics-based algorithms that ensure data quality, specifically data granularity and timeliness, in addition to preserving network bandwidth. Our framework for group-aware stream filtering is extensible and supports a diverse range of filtering needs of monitoring applications. We evaluate this approach with a prototype system based on real-world data sets. The results show that quality-managed group-aware filtering is effective in trading CPU time for bandwidth savings, compared with self-interested stream filtering. We also evaluate the effect of each algorithm on temporal freshness of the data. Finally, we discuss other application realms that might benefit from group-aware stream filtering.
Description	x, 115 leaves : ill. 28 cm.
Format	application/pdf
Advisor	David Kotz
Subject	Streaming technology (Telecommunications). Computer networks -- Monitoring. Multicasting (Computer networks). Electronic data processing -- Distributed processing.
College	Dartmouth College -- Department of Computer Science.
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Copyright	All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means without permission in writing from the author.
DOI	http://dx.doi.org/10.1349/ddlp.176

Title	Page 1
Format	application/pdf
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Full Text	GROUP-AWARE STREAM FILTERING A Dissertation Submitted to the Faculty in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science by Ming Li DARTMOUTH COLLEGE Hanover, New Hampshire May 2008 Examining Committee: (chair) David Kotz Andrew Campbell Paul Thompson Apratim Purakayastha Charles K. Barlowe, Ph.D. Dean of Graduate Studies

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Group-aware stream filtering.

Group-aware stream filtering.