Abstract
Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model.
| Original language | English |
|---|---|
| Title of host publication | SIGMOD 2006 - Proceedings of the ACM SIGMOD International Conference on Management of Data |
| Pages | 635-646 |
| Number of pages | 12 |
| DOIs | |
| Publication status | Published - 2006 |
| Externally published | Yes |
| Event | 2006 ACM SIGMOD International Conference on Management of Data - Chicago, IL, United States Duration: 27 Jun 2006 → 29 Jun 2006 |
Other
| Other | 2006 ACM SIGMOD International Conference on Management of Data |
|---|---|
| Country | United States |
| City | Chicago, IL |
| Period | 27/6/06 → 29/6/06 |
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Keywords
- Continuous queries
- Sliding windows
- Top-k processing
ASJC Scopus subject areas
- Computer Science(all)
Cite this
Continuous monitoring of top-k queries over sliding windows. / Mouratidis, Kyriakos; Bakiras, Spiridon; Papadias, Dimitris.
SIGMOD 2006 - Proceedings of the ACM SIGMOD International Conference on Management of Data. 2006. p. 635-646.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Continuous monitoring of top-k queries over sliding windows
AU - Mouratidis, Kyriakos
AU - Bakiras, Spiridon
AU - Papadias, Dimitris
PY - 2006
Y1 - 2006
N2 - Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model.
AB - Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model.
KW - Continuous queries
KW - Sliding windows
KW - Top-k processing
UR - http://www.scopus.com/inward/record.url?scp=34250656636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34250656636&partnerID=8YFLogxK
U2 - 10.1145/1142473.1142544
DO - 10.1145/1142473.1142544
M3 - Conference contribution
SN - 1595934340
SN - 9781595934345
SP - 635
EP - 646
BT - SIGMOD 2006 - Proceedings of the ACM SIGMOD International Conference on Management of Data
ER -