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Group-aware stream filtering.

Dartmouth College., Computer Science.

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Group-aware stream filtering.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Group-aware stream filtering./
作者:	Li, Ming.
面頁冊數:	126 p.
附註:	Adviser: David Kotz.
Contained By:	Dissertation Abstracts International69-10B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3330640
ISBN:	9780549854050

Group-aware stream filtering.
Li, Ming.

Group-aware stream filtering. - 126 p.

Adviser: David Kotz.

Thesis (Ph.D.)--Dartmouth College, 2008.

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.

ISBN: 9780549854050Subjects--Topical Terms:

626642
Computer Science.

Group-aware stream filtering.
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500 $a Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6216.
502 $a Thesis (Ph.D.)--Dartmouth College, 2008.
520 $a 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.
520 $a 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.
520 $a 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.
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