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Continuous Top-K monitoring on document streams (extended Abstract)
Leong Hou,U.1; Zhang,Junjie1; Mouratidis,Kyriakos2; Li,Ye1
2018-10-24
Source PublicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Pages1803-1804
AbstractThe 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 top-k 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.
KeywordContinuous query Document stream Top k query
DOI10.1109/ICDE.2018.00259
URLView the original
Language英語English
Scopus ID2-s2.0-85057095454
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Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.Department of Computer and Information Science,University of Macau,Macau SAR,Macao
2.School of Information Systems,Singapore Management University,Singapore,Singapore
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Leong Hou,U.,Zhang,Junjie,Mouratidis,Kyriakos,et al. Continuous Top-K monitoring on document streams (extended Abstract)[C], 2018, 1803-1804.
APA Leong Hou,U.., Zhang,Junjie., Mouratidis,Kyriakos., & Li,Ye (2018). Continuous Top-K monitoring on document streams (extended Abstract). Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018, 1803-1804.
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