DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tao, Yufei | ko |
dc.contributor.author | Sheng, Cheng | ko |
dc.contributor.author | Chung, Chin-Wan | ko |
dc.contributor.author | Lee, Jongryul | ko |
dc.date.accessioned | 2014-08-27T02:45:14Z | - |
dc.date.available | 2014-08-27T02:45:14Z | - |
dc.date.created | 2013-07-19 | - |
dc.date.created | 2013-07-19 | - |
dc.date.issued | 2014-05 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.26, no.5, pp.1240 - 1252 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | http://hdl.handle.net/10203/187406 | - |
dc.description.abstract | In the classic range aggregation problem, we have a set S of objects such that, given an interval I, a query counts how many objects of S are covered by I. Besides COUNT, the problem can also be defined with other aggregate functions, e. g., SUM, MIN, MAX and AVERAGE. This paper studies a novel variant of range aggregation, where an object can belong to multiple sets. A query (at runtime) picks any two sets, and aggregates on their intersection. More formally, let S-1, ..., S-m be m sets of objects. Given distinct set ids i, j and an interval I, a query reports how many objects in S-i boolean AND S-j are covered by I. We call this problem range aggregation with set selection (RASS). Its hardness lies in that the pair (i, j) can have ((m)(2)) choices, rendering effective indexing a non-trivial task. The RASS problem can also be defined with other aggregate functions, and generalized so that a query chooses more than 2 sets. We develop a system called RASS to power this type of queries. Our system has excellent efficiency in both theory and practice. Theoretically, it consumes linear space, and achieves nearly-optimal query time. Practically, it outperforms existing solutions on real datasets by a factor up to an order of magnitude. The paper also features a rigorous theoretical analysis on the hardness of the RASS problem, which reveals invaluable insight into its characteristics. | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.subject | SPATIAL DATABASES | - |
dc.subject | INTERSECTION | - |
dc.subject | QUERIES | - |
dc.title | Range Aggregation with Set Selection | - |
dc.type | Article | - |
dc.identifier.wosid | 000337965900016 | - |
dc.identifier.scopusid | 2-s2.0-84901036399 | - |
dc.type.rims | ART | - |
dc.citation.volume | 26 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 1240 | - |
dc.citation.endingpage | 1252 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.identifier.doi | 10.1109/TKDE.2013.125 | - |
dc.contributor.localauthor | Chung, Chin-Wan | - |
dc.contributor.nonIdAuthor | Tao, Yufei | - |
dc.contributor.nonIdAuthor | Sheng, Cheng | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Range aggregation | - |
dc.subject.keywordAuthor | index | - |
dc.subject.keywordAuthor | theory | - |
dc.subject.keywordPlus | SPATIAL DATABASES | - |
dc.subject.keywordPlus | INTERSECTION | - |
dc.subject.keywordPlus | QUERIES | - |
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