Selectivity estimation is one of the common research problems for big spatial data, where the objective is to quickly estimate the number of records in a given query range. Euler histogram has been used to answer the selectivity estimation queries for objects with extents such as rectangles in constant time. However, it is only accurate when the query range is aligned with the histogram grid lines. In this paper, we improve the Euler histogram to accurately answer arbitrary queries, i.e., even if they do not align with the histogram grid lines. The improved histogram, called Euler++, has the same space and time complexity as the regular Euler histogram and provides a better accuracy for objects with extents. We use both real and synthetic datasets for extensive experiments, and show that the proposed technique, Euler++, consistently outperforms the existing ones, while still providing answer in constant time.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Editors||Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye|
|Number of pages||5|
|State||Published - Dec 2019|
|Event||2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States|
Duration: Dec 9 2019 → Dec 12 2019
|Name||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Conference||2019 IEEE International Conference on Big Data, Big Data 2019|
|Period||12/9/19 → 12/12/19|
Bibliographical noteFunding Information:
This work is supported in part by the National Science Foundation (NSF) under grants IIS-1838222, IIS-1619463, and IIS-1447826.
© 2019 IEEE.
- Spatial data synopsis
- big spatial data
- query optimization
- selectivity estimation
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management