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- Publisher Website: 10.14778/1920841.1921066
- Scopus: eid_2-s2.0-79956032313
- WOS: WOS:000219672100049
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Conference Paper: Similarity Search and Mining in Uncertain Databases
Title | Similarity Search and Mining in Uncertain Databases |
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Authors | |
Issue Date | 2010 |
Publisher | Very Large Data Base (VLDB) Endowment Inc. The Journal's web site is located at http://vldb.org/pvldb/index.html |
Citation | The 36th International Conference on Very Large Data Bases (VLDB 2010), Singapore, 13-17 September 2010. In Proceedings of the VLDB Endowment, 2010, v. 3 n. 2, p. 1653-1654 How to Cite? |
Abstract | Managing, searching and mining uncertain data has achieved much attention in the database community recently due to new sensor technologies and new ways of collecting data. There is a number of challenges in terms of collecting, modelling, representing, querying, indexing and mining uncertain data. In its scope, the diversity of approaches addressing these topics is very high because the underlying assumptions of uncertainty are different across different papers. This tutorial provides a comprehensive and comparative overview of general techniques for the key topics in the fields of
querying, indexing and mining uncertain data. In particular, it identifies the most generic types of probabilistic similarity queries and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches probabilistic methods for important data mining applications in the context of uncertain data with special emphasis on probabilistic clustering and probabilistic pattern mining. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with uncertain data retrieval and mining. |
Description | Tutorial |
Persistent Identifier | http://hdl.handle.net/10722/241074 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 2.666 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Renz, MCL | - |
dc.contributor.author | Cheng, CK | - |
dc.contributor.author | Kriegel, HP | - |
dc.date.accessioned | 2017-05-23T03:41:33Z | - |
dc.date.available | 2017-05-23T03:41:33Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | The 36th International Conference on Very Large Data Bases (VLDB 2010), Singapore, 13-17 September 2010. In Proceedings of the VLDB Endowment, 2010, v. 3 n. 2, p. 1653-1654 | - |
dc.identifier.issn | 2150-8097 | - |
dc.identifier.uri | http://hdl.handle.net/10722/241074 | - |
dc.description | Tutorial | - |
dc.description.abstract | Managing, searching and mining uncertain data has achieved much attention in the database community recently due to new sensor technologies and new ways of collecting data. There is a number of challenges in terms of collecting, modelling, representing, querying, indexing and mining uncertain data. In its scope, the diversity of approaches addressing these topics is very high because the underlying assumptions of uncertainty are different across different papers. This tutorial provides a comprehensive and comparative overview of general techniques for the key topics in the fields of querying, indexing and mining uncertain data. In particular, it identifies the most generic types of probabilistic similarity queries and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches probabilistic methods for important data mining applications in the context of uncertain data with special emphasis on probabilistic clustering and probabilistic pattern mining. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with uncertain data retrieval and mining. | - |
dc.language | eng | - |
dc.publisher | Very Large Data Base (VLDB) Endowment Inc. The Journal's web site is located at http://vldb.org/pvldb/index.html | - |
dc.relation.ispartof | Proceedings of the VLDB Endowment | - |
dc.title | Similarity Search and Mining in Uncertain Databases | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Cheng, CK: ckcheng@cs.hku.hk | - |
dc.identifier.authority | Cheng, CK=rp00074 | - |
dc.identifier.doi | 10.14778/1920841.1921066 | - |
dc.identifier.scopus | eid_2-s2.0-79956032313 | - |
dc.identifier.hkuros | 176522 | - |
dc.identifier.volume | 3 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1653 | - |
dc.identifier.epage | 1654 | - |
dc.identifier.isi | WOS:000219672100049 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2150-8097 | - |