Abstract
Histograms have been widely used for fast estimation of query result sizes in query optimization. In this paper, we propose a new histogram method, called the Skew-Tolerant Histogram (STHistogram) for two or three dimensional geographic data objects that are used in many real-world applications in practice. The proposed method provides a significantly enhanced accuracy in a robust manner even for the data set that has a highly skewed distribution. Our method detects hotspots present in various parts of a data set and exploits them in organizing histogram buckets. For this purpose, we first define the concept of a hotspot, and provide an algorithm that efficiently extracts hotspots from the given data set. Then, we present our histogram construction method that utilizes hotspot information. We also describe how to estimate query result sizes by using the proposed histogram. We show through extensive performance experiments that the proposed method provides better performance than other existing methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the ACM SIGMOD International Conference on Management of Data |
| Pages | 627-638 |
| Number of pages | 12 |
| DOIs | |
| Publication status | Published - 2010 Jul 23 |
| Event | 2010 International Conference on Management of Data, SIGMOD '10 - Indianapolis, IN, United States Duration: 2010 Jun 6 → 2010 Jun 11 |
Other
| Other | 2010 International Conference on Management of Data, SIGMOD '10 |
|---|---|
| Country | United States |
| City | Indianapolis, IN |
| Period | 10/6/6 → 10/6/11 |
Fingerprint
Keywords
- histograms
- query optimization
- spatial databases
ASJC Scopus subject areas
- Information Systems
- Software
Cite this
Hierarchically organized skew-tolerant histograms for geographic data objects. / Roh, Yohan J.; Kim, Jae H.; Chung, Yon Dohn; Son, Jin Hyun; Kim, Myoung H.
Proceedings of the ACM SIGMOD International Conference on Management of Data. 2010. p. 627-638.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hierarchically organized skew-tolerant histograms for geographic data objects
AU - Roh, Yohan J.
AU - Kim, Jae H.
AU - Chung, Yon Dohn
AU - Son, Jin Hyun
AU - Kim, Myoung H.
PY - 2010/7/23
Y1 - 2010/7/23
N2 - Histograms have been widely used for fast estimation of query result sizes in query optimization. In this paper, we propose a new histogram method, called the Skew-Tolerant Histogram (STHistogram) for two or three dimensional geographic data objects that are used in many real-world applications in practice. The proposed method provides a significantly enhanced accuracy in a robust manner even for the data set that has a highly skewed distribution. Our method detects hotspots present in various parts of a data set and exploits them in organizing histogram buckets. For this purpose, we first define the concept of a hotspot, and provide an algorithm that efficiently extracts hotspots from the given data set. Then, we present our histogram construction method that utilizes hotspot information. We also describe how to estimate query result sizes by using the proposed histogram. We show through extensive performance experiments that the proposed method provides better performance than other existing methods.
AB - Histograms have been widely used for fast estimation of query result sizes in query optimization. In this paper, we propose a new histogram method, called the Skew-Tolerant Histogram (STHistogram) for two or three dimensional geographic data objects that are used in many real-world applications in practice. The proposed method provides a significantly enhanced accuracy in a robust manner even for the data set that has a highly skewed distribution. Our method detects hotspots present in various parts of a data set and exploits them in organizing histogram buckets. For this purpose, we first define the concept of a hotspot, and provide an algorithm that efficiently extracts hotspots from the given data set. Then, we present our histogram construction method that utilizes hotspot information. We also describe how to estimate query result sizes by using the proposed histogram. We show through extensive performance experiments that the proposed method provides better performance than other existing methods.
KW - histograms
KW - query optimization
KW - spatial databases
UR - http://www.scopus.com/inward/record.url?scp=77954701875&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954701875&partnerID=8YFLogxK
U2 - 10.1145/1807167.1807236
DO - 10.1145/1807167.1807236
M3 - Conference contribution
AN - SCOPUS:77954701875
SN - 9781450300322
SP - 627
EP - 638
BT - Proceedings of the ACM SIGMOD International Conference on Management of Data
ER -