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Conference Paper: Context-aware Top-k Processing Using Views

TitleContext-aware Top-k Processing Using Views
Authors
Issue Date2013
PublisherACM Press.
Citation
Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM), San Francisco, USA, 27 October-1 November 2013, p. 1959-1968 How to Cite?
AbstractSearch applications where queries are dependent on their context are becoming increasingly relevant in today's online applications. For example, the context may be the location of the user in location- aware search or the social network of the query initiator in social-aware search. Processing such queries efficiently is inherently difficult, and requires techniques that go beyond the existing, context-agnostic ones. A promising direction for efficient, online answering -- especially in the case of top-k queries -- is to materialize and exploit previous query results (views). We consider context-aware query optimization based on views, focusing on two important sub-problems. First, handling the possible differences in context between the various views and an input query leads to view results having uncertain scores, i.e., score ranges valid for the new context. As a consequence, current top-k algorithms are no longer directly applicable and need to be adapted to handle such uncertainty in object scores. Second, adapted view selection techniques are needed, which can leverage both the descriptions of queries and statistics over their results. We present algorithms that address these two problems, and illustrate their practical use in two important application scenarios: location-aware search and social-aware search. We validate our approaches via extensive experiments, using both synthetic and real-world datasets.
Persistent Identifierhttp://hdl.handle.net/10722/201104
ISBN

 

DC FieldValueLanguage
dc.contributor.authorManiu, Sen_US
dc.contributor.authorCautis, Ben_US
dc.date.accessioned2014-08-21T07:13:34Z-
dc.date.available2014-08-21T07:13:34Z-
dc.date.issued2013en_US
dc.identifier.citationProceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM), San Francisco, USA, 27 October-1 November 2013, p. 1959-1968en_US
dc.identifier.isbn9781450322638-
dc.identifier.urihttp://hdl.handle.net/10722/201104-
dc.description.abstractSearch applications where queries are dependent on their context are becoming increasingly relevant in today's online applications. For example, the context may be the location of the user in location- aware search or the social network of the query initiator in social-aware search. Processing such queries efficiently is inherently difficult, and requires techniques that go beyond the existing, context-agnostic ones. A promising direction for efficient, online answering -- especially in the case of top-k queries -- is to materialize and exploit previous query results (views). We consider context-aware query optimization based on views, focusing on two important sub-problems. First, handling the possible differences in context between the various views and an input query leads to view results having uncertain scores, i.e., score ranges valid for the new context. As a consequence, current top-k algorithms are no longer directly applicable and need to be adapted to handle such uncertainty in object scores. Second, adapted view selection techniques are needed, which can leverage both the descriptions of queries and statistics over their results. We present algorithms that address these two problems, and illustrate their practical use in two important application scenarios: location-aware search and social-aware search. We validate our approaches via extensive experiments, using both synthetic and real-world datasets.-
dc.languageengen_US
dc.publisherACM Press.en_US
dc.relation.ispartofACM International Conference on Information and Knowledge Managementen_US
dc.titleContext-aware Top-k Processing Using Viewsen_US
dc.typeConference_Paperen_US
dc.identifier.emailManiu, S: smaniu@cs.hku.hken_US
dc.identifier.doi10.1145/2505515.2505759-
dc.identifier.scopuseid_2-s2.0-84889575925-
dc.identifier.hkuros232982en_US
dc.identifier.spage1959-
dc.identifier.epage1968-
dc.publisher.placeNew York, N.Y.-

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