SkyTree: Scalable skyline computation for sensor data

Lee Jongwuk, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Skyline queries have gained attention for supporting multicriteria analysis of large-scale datasets. While a lot of skyline algorithms have been proposed, most of the algorithms build upon pre-computed index structures that cannot generally be supported over sensor data of dynamically changing attribute values. We aim to design a scalable non-index skyline computation algorithm for sensor data. More specifi-cally, we propose Algorithm SkyTree constructing a dynamic lattice that divides a specific region into several subregions based on a pivot point maximizing dominance region. Such structure enables to perform region-wise dominance tests, which eliminates unnecessary point-wise dominance tests. In addition, we ensure the progressiveness that has not been supported by any non-index algorithm, where we can identify k points maximizing the sum of dominance regions as the greedy approximation method. The k points are used to reduce communication cost between sensors in computing global skyline. Our evaluation results validate the efficiency of Algorithm SkyTree, both in terms of response time and communication overhead, over existing algorithms.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09
Pages114-123
Number of pages10
DOIs
Publication statusPublished - 2009 Nov 25
Event3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09 - Paris, France
Duration: 2009 Jun 282009 Jun 28

Publication series

NameProceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09

Other

Other3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09
CountryFrance
CityParis
Period09/6/2809/6/28

Fingerprint

Sensors
Lattice vibrations
Communication
Costs

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Cite this

Jongwuk, L., & Hwang, S. W. (2009). SkyTree: Scalable skyline computation for sensor data. In Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09 (pp. 114-123). [1601985] (Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09). https://doi.org/10.1145/1601966.1601985
Jongwuk, Lee ; Hwang, Seung Won. / SkyTree : Scalable skyline computation for sensor data. Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09. 2009. pp. 114-123 (Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09).
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title = "SkyTree: Scalable skyline computation for sensor data",
abstract = "Skyline queries have gained attention for supporting multicriteria analysis of large-scale datasets. While a lot of skyline algorithms have been proposed, most of the algorithms build upon pre-computed index structures that cannot generally be supported over sensor data of dynamically changing attribute values. We aim to design a scalable non-index skyline computation algorithm for sensor data. More specifi-cally, we propose Algorithm SkyTree constructing a dynamic lattice that divides a specific region into several subregions based on a pivot point maximizing dominance region. Such structure enables to perform region-wise dominance tests, which eliminates unnecessary point-wise dominance tests. In addition, we ensure the progressiveness that has not been supported by any non-index algorithm, where we can identify k points maximizing the sum of dominance regions as the greedy approximation method. The k points are used to reduce communication cost between sensors in computing global skyline. Our evaluation results validate the efficiency of Algorithm SkyTree, both in terms of response time and communication overhead, over existing algorithms.",
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Jongwuk, L & Hwang, SW 2009, SkyTree: Scalable skyline computation for sensor data. in Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09., 1601985, Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09, pp. 114-123, 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09, Paris, France, 09/6/28. https://doi.org/10.1145/1601966.1601985

SkyTree : Scalable skyline computation for sensor data. / Jongwuk, Lee; Hwang, Seung Won.

Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09. 2009. p. 114-123 1601985 (Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Jongwuk L, Hwang SW. SkyTree: Scalable skyline computation for sensor data. In Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09. 2009. p. 114-123. 1601985. (Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09). https://doi.org/10.1145/1601966.1601985