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Conference Paper: Cleaning uncertain data for top-k queries
Title | Cleaning uncertain data for top-k queries |
---|---|
Authors | |
Keywords | Cleaning operations Data uncertainty Emerging applications Greedy algorithms Optimal solutions Possible world semantics Probabilistic database Temperature values |
Issue Date | 2013 |
Publisher | IEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178 |
Citation | The 29th International Conference on Data Engineering (ICDE 2013), Brisbane, Australia, 8-11 April 2013. In International Conference on Data Engineering Proceedings, 2013, p. 134-145 How to Cite? |
Abstract | The information managed in emerging applications, such as sensor networks, location-based services, and data integration, is inherently imprecise. To handle data uncertainty, probabilistic databases have been recently developed. In this paper, we study how to quantify the ambiguity of answers returned by a probabilistic top-k query. We develop efficient algorithms to compute the quality of this query under the possible world semantics. We further address the cleaning of a probabilistic database, in order to improve top-k query quality. Cleaning involves the reduction of ambiguity associated with the database entities. For example, the uncertainty of a temperature value acquired from a sensor can be reduced, or cleaned, by requesting its newest value from the sensor. While this 'cleaning operation' may produce a better query result, it may involve a cost and fail. We investigate the problem of selecting entities to be cleaned under a limited budget. Particularly, we propose an optimal solution and several heuristics. Experiments show that the greedy algorithm is efficient and close to optimal. © 2013 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/189637 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 1.306 |
DC Field | Value | Language |
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dc.contributor.author | Mo, L | en_US |
dc.contributor.author | Cheng, R | en_US |
dc.contributor.author | Li, X | en_US |
dc.contributor.author | Cheung, DWL | en_US |
dc.contributor.author | Yang, XS | en_US |
dc.date.accessioned | 2013-09-17T14:50:33Z | - |
dc.date.available | 2013-09-17T14:50:33Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 29th International Conference on Data Engineering (ICDE 2013), Brisbane, Australia, 8-11 April 2013. In International Conference on Data Engineering Proceedings, 2013, p. 134-145 | en_US |
dc.identifier.isbn | 978-1-4673-4910-9 | - |
dc.identifier.issn | 1084-4627 | - |
dc.identifier.uri | http://hdl.handle.net/10722/189637 | - |
dc.description.abstract | The information managed in emerging applications, such as sensor networks, location-based services, and data integration, is inherently imprecise. To handle data uncertainty, probabilistic databases have been recently developed. In this paper, we study how to quantify the ambiguity of answers returned by a probabilistic top-k query. We develop efficient algorithms to compute the quality of this query under the possible world semantics. We further address the cleaning of a probabilistic database, in order to improve top-k query quality. Cleaning involves the reduction of ambiguity associated with the database entities. For example, the uncertainty of a temperature value acquired from a sensor can be reduced, or cleaned, by requesting its newest value from the sensor. While this 'cleaning operation' may produce a better query result, it may involve a cost and fail. We investigate the problem of selecting entities to be cleaned under a limited budget. Particularly, we propose an optimal solution and several heuristics. Experiments show that the greedy algorithm is efficient and close to optimal. © 2013 IEEE. | - |
dc.language | eng | en_US |
dc.publisher | IEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178 | - |
dc.relation.ispartof | International Conference on Data Engineering Proceedings | en_US |
dc.subject | Cleaning operations | - |
dc.subject | Data uncertainty | - |
dc.subject | Emerging applications | - |
dc.subject | Greedy algorithms | - |
dc.subject | Optimal solutions | - |
dc.subject | Possible world semantics | - |
dc.subject | Probabilistic database | - |
dc.subject | Temperature values | - |
dc.title | Cleaning uncertain data for top-k queries | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Mo, L: lymo@cs.hku.hk | en_US |
dc.identifier.email | Cheng, R: ckcheng@cs.hku.hk | en_US |
dc.identifier.email | Li, X: xli@cs.hku.hk | - |
dc.identifier.email | Cheung, DWL: dcheung@cs.hku.hk | - |
dc.identifier.email | Yang, XS: xyang2@cs.hku.hk | - |
dc.identifier.authority | Cheng, R=rp00074 | en_US |
dc.identifier.authority | Cheung, DWL=rp00101 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICDE.2013.6544820 | - |
dc.identifier.scopus | eid_2-s2.0-84881328468 | - |
dc.identifier.hkuros | 222869 | en_US |
dc.identifier.spage | 134 | - |
dc.identifier.epage | 145 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 131023 | - |
dc.identifier.issnl | 1084-4627 | - |