Abstract
As near-infinite amount of data are becoming accessible on the Web, it is getting more and more important to support intelligent query mechanisms, to help each user to identify the ideal results of manageable size. As such mechanism, skyline queries have gained a lot of attention lately for its intuitive query formulation. This intuitiveness, however, has a side-effect of generating too many results, especially for high-dimensional data, to satisfy a wide range of user's needs. Our goal is to support personalized skyline queries as identifying "truly interesting" objects based on user-specific preference and retrieval size k. While this problem has been studied previously, the proposed solution identifies top-k results by navigating a "skycube", which incurs exponential storage overhead to data dimensionality and excessive one-time computational overhead for skycube construction. In contrast, we develop novel techniques to significantly reduce both storage and computation overhead. Our extensive evaluation results validate this framework on both real-life and synthetic data.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th Annual ACM International Workshop on Web Information and Data Management, WIDM '07, Co-located with the 16th ACM Conference on Information and Knowledge Management, CIKM '07 |
| Pages | 65-72 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2007 Dec 1 |
| Event | 9th Annual ACM International Workshop on Web Information and Data Management, WIDM '07, Co-located with the 16th ACM Conference on Information and Knowledge Management, CIKM '07 - Lisboa, Portugal Duration: 2007 Nov 6 → 2007 Nov 9 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|
Other
| Other | 9th Annual ACM International Workshop on Web Information and Data Management, WIDM '07, Co-located with the 16th ACM Conference on Information and Knowledge Management, CIKM '07 |
|---|---|
| Country | Portugal |
| City | Lisboa |
| Period | 07/11/6 → 07/11/9 |
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)
Cite this
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Supporting personalized top-k skyline queries using partial compressed skycube. / Lee, Jongwuk; You, Gae Won; Sohn, Ik Chan; Hwang, Seung Won; Ko, Kwangil; Lee, Zino.
Proceedings of the 9th Annual ACM International Workshop on Web Information and Data Management, WIDM '07, Co-located with the 16th ACM Conference on Information and Knowledge Management, CIKM '07. 2007. p. 65-72 (International Conference on Information and Knowledge Management, Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Supporting personalized top-k skyline queries using partial compressed skycube
AU - Lee, Jongwuk
AU - You, Gae Won
AU - Sohn, Ik Chan
AU - Hwang, Seung Won
AU - Ko, Kwangil
AU - Lee, Zino
PY - 2007/12/1
Y1 - 2007/12/1
N2 - As near-infinite amount of data are becoming accessible on the Web, it is getting more and more important to support intelligent query mechanisms, to help each user to identify the ideal results of manageable size. As such mechanism, skyline queries have gained a lot of attention lately for its intuitive query formulation. This intuitiveness, however, has a side-effect of generating too many results, especially for high-dimensional data, to satisfy a wide range of user's needs. Our goal is to support personalized skyline queries as identifying "truly interesting" objects based on user-specific preference and retrieval size k. While this problem has been studied previously, the proposed solution identifies top-k results by navigating a "skycube", which incurs exponential storage overhead to data dimensionality and excessive one-time computational overhead for skycube construction. In contrast, we develop novel techniques to significantly reduce both storage and computation overhead. Our extensive evaluation results validate this framework on both real-life and synthetic data.
AB - As near-infinite amount of data are becoming accessible on the Web, it is getting more and more important to support intelligent query mechanisms, to help each user to identify the ideal results of manageable size. As such mechanism, skyline queries have gained a lot of attention lately for its intuitive query formulation. This intuitiveness, however, has a side-effect of generating too many results, especially for high-dimensional data, to satisfy a wide range of user's needs. Our goal is to support personalized skyline queries as identifying "truly interesting" objects based on user-specific preference and retrieval size k. While this problem has been studied previously, the proposed solution identifies top-k results by navigating a "skycube", which incurs exponential storage overhead to data dimensionality and excessive one-time computational overhead for skycube construction. In contrast, we develop novel techniques to significantly reduce both storage and computation overhead. Our extensive evaluation results validate this framework on both real-life and synthetic data.
UR - http://www.scopus.com/inward/record.url?scp=77951135674&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951135674&partnerID=8YFLogxK
U2 - 10.1145/1316902.1316914
DO - 10.1145/1316902.1316914
M3 - Conference contribution
AN - SCOPUS:77951135674
SN - 9781595938299
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 65
EP - 72
BT - Proceedings of the 9th Annual ACM International Workshop on Web Information and Data Management, WIDM '07, Co-located with the 16th ACM Conference on Information and Knowledge Management, CIKM '07
ER -