語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Scaling continuous query services fo...
~
Gedik, Bugra.
FindBook
Google Book
Amazon
博客來
Scaling continuous query services for future computing platforms and applications.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Scaling continuous query services for future computing platforms and applications./
作者:
Gedik, Bugra.
面頁冊數:
272 p.
附註:
Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5189.
Contained By:
Dissertation Abstracts International67-09B.
標題:
Information Science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3233520
ISBN:
9780542861321
Scaling continuous query services for future computing platforms and applications.
Gedik, Bugra.
Scaling continuous query services for future computing platforms and applications.
- 272 p.
Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5189.
Thesis (Ph.D.)--Georgia Institute of Technology, 2006.
The ever increasing rate of digital information available from on-line sources drives the need for building information monitoring applications to assist users in tracking relevant changes in these sources and accessing information that is of interest to them in a timely manner. Continuous queries (CQs) are standing queries that are continuously evaluated over dynamic sources to track information changes that meet user specified thresholds and notify users of new results in real-time. CQ systems can be considered as powerful middleware for supporting information monitoring applications. A significant challenge in building CQ systems is scalability, caused by the large number of users and queries, and by the large and growing number of information sources with high update rates. In this thesis we use CQs to shepherd through and address the challenges involved in supporting information monitoring applications in future computing platforms. The focus is on P2P web monitoring in Internet systems, location monitoring in mobile systems, and environmental monitoring in sensor systems. Although different computing platforms require different software architectures for building scalable CQ services, there is a common design philosophy that this thesis advocates for making CQ services scalable and efficient. This can be summarized as "move computation close to the places where the data is produced." A common challenge in scaling CQ systems is the resource-intensive nature of query evaluation, which involves continuously checking updates in a large number of data sources and evaluating trigger conditions of a large number of queries over these updates, consuming both cpu and network bandwidth resources. If some part of the query evaluation can be pushed close to the sources where the data is produced, the resulting early filtering of updates will save both bandwidth and cpu resources. In summary, in this thesis we show that distributed CQ architectures that are designed to take advantage of the opportunities provided by ubiquitous computing platforms and pervasive networks, while at the same time recognizing and resolving the challenges posed by these platforms, lead to building scalable and effective CQ systems to better support the demanding information monitoring applications of the future.
ISBN: 9780542861321Subjects--Topical Terms:
1017528
Information Science.
Scaling continuous query services for future computing platforms and applications.
LDR
:03212nmm 2200277 4500
001
1834956
005
20071129070342.5
008
130610s2006 eng d
020
$a
9780542861321
035
$a
(UMI)AAI3233520
035
$a
AAI3233520
040
$a
UMI
$c
UMI
100
1
$a
Gedik, Bugra.
$3
1923591
245
1 0
$a
Scaling continuous query services for future computing platforms and applications.
300
$a
272 p.
500
$a
Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5189.
500
$a
Adviser: Ling Liu.
502
$a
Thesis (Ph.D.)--Georgia Institute of Technology, 2006.
520
$a
The ever increasing rate of digital information available from on-line sources drives the need for building information monitoring applications to assist users in tracking relevant changes in these sources and accessing information that is of interest to them in a timely manner. Continuous queries (CQs) are standing queries that are continuously evaluated over dynamic sources to track information changes that meet user specified thresholds and notify users of new results in real-time. CQ systems can be considered as powerful middleware for supporting information monitoring applications. A significant challenge in building CQ systems is scalability, caused by the large number of users and queries, and by the large and growing number of information sources with high update rates. In this thesis we use CQs to shepherd through and address the challenges involved in supporting information monitoring applications in future computing platforms. The focus is on P2P web monitoring in Internet systems, location monitoring in mobile systems, and environmental monitoring in sensor systems. Although different computing platforms require different software architectures for building scalable CQ services, there is a common design philosophy that this thesis advocates for making CQ services scalable and efficient. This can be summarized as "move computation close to the places where the data is produced." A common challenge in scaling CQ systems is the resource-intensive nature of query evaluation, which involves continuously checking updates in a large number of data sources and evaluating trigger conditions of a large number of queries over these updates, consuming both cpu and network bandwidth resources. If some part of the query evaluation can be pushed close to the sources where the data is produced, the resulting early filtering of updates will save both bandwidth and cpu resources. In summary, in this thesis we show that distributed CQ architectures that are designed to take advantage of the opportunities provided by ubiquitous computing platforms and pervasive networks, while at the same time recognizing and resolving the challenges posed by these platforms, lead to building scalable and effective CQ systems to better support the demanding information monitoring applications of the future.
590
$a
School code: 0078.
650
4
$a
Information Science.
$3
1017528
650
4
$a
Computer Science.
$3
626642
690
$a
0723
690
$a
0984
710
2 0
$a
Georgia Institute of Technology.
$3
696730
773
0
$t
Dissertation Abstracts International
$g
67-09B.
790
1 0
$a
Liu, Ling,
$e
advisor
790
$a
0078
791
$a
Ph.D.
792
$a
2006
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3233520
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9225976
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入