Continuous top-K monitoring on document streams (Extended abstract)
The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user prefe...
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sg-smu-ink.sis_research-51772018-12-07T02:39:18Z Continuous top-K monitoring on document streams (Extended abstract) U, Leong Hou ZHANG, Junjie MOURATIDIS, Kyriakos LI, Ye The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. The objective is to support large numbers of users and high stream rates, while refreshing the topk results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach, and follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a locally adaptive technique, our method offers (i) optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time than the state-of-the-art. 2018-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4174 info:doi/10.1109/ICDE.2018.00259 https://ink.library.smu.edu.sg/context/sis_research/article/5177/viewcontent/ICDE18_MRIO__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Data Storage Systems |
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Databases and Information Systems Data Storage Systems U, Leong Hou ZHANG, Junjie MOURATIDIS, Kyriakos LI, Ye Continuous top-K monitoring on document streams (Extended abstract) |
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The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. The objective is to support large numbers of users and high stream rates, while refreshing the topk results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach, and follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a locally adaptive technique, our method offers (i) optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time than the state-of-the-art. |
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text |
author |
U, Leong Hou ZHANG, Junjie MOURATIDIS, Kyriakos LI, Ye |
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U, Leong Hou ZHANG, Junjie MOURATIDIS, Kyriakos LI, Ye |
author_sort |
U, Leong Hou |
title |
Continuous top-K monitoring on document streams (Extended abstract) |
title_short |
Continuous top-K monitoring on document streams (Extended abstract) |
title_full |
Continuous top-K monitoring on document streams (Extended abstract) |
title_fullStr |
Continuous top-K monitoring on document streams (Extended abstract) |
title_full_unstemmed |
Continuous top-K monitoring on document streams (Extended abstract) |
title_sort |
continuous top-k monitoring on document streams (extended abstract) |
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Institutional Knowledge at Singapore Management University |
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2018 |
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https://ink.library.smu.edu.sg/sis_research/4174 https://ink.library.smu.edu.sg/context/sis_research/article/5177/viewcontent/ICDE18_MRIO__1_.pdf |
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