A benchmark for evaluating moving object indexes

Author:

Chen Su1,Jensen Christian S.2,Lin Dan3

Affiliation:

1. National University of Singapore

2. Aalborg University

3. Purdue University

Abstract

Progress in science and engineering relies on the ability to measure, reliably and in detail, pertinent properties of artifacts under design. Progress in the area of database-index design thus relies on empirical studies based on prototype implementations of indexes. This paper proposes a benchmark that targets techniques for the indexing of the current and near-future positions of moving objects. This benchmark enables the comparison of existing and future indexing techniques. It covers important aspects of such indexes that have not previously been covered by any benchmark. Notable aspects covered include update efficiency, query efficiency, concurrency control, and storage requirements. Next, the paper applies the benchmark to half a dozen notable moving-object indexes, thus demonstrating the viability of the benchmark and offering new insight into the performance properties of the indexes.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 49 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analyzing the performance of different machine learning methods in determining the transportation mode using trajectory data;Journal of Geospatial Information Technology;2023-02-01

2. Waffle;Proceedings of the VLDB Endowment;2022-12

3. The LSM RUM-Tree: A Log Structured Merge R-Tree for Update-intensive Spatial Workloads;2021 IEEE 37th International Conference on Data Engineering (ICDE);2021-04

4. NEIST: A Neural-Enhanced Index for Spatio-Temporal Queries;IEEE Transactions on Knowledge and Data Engineering;2021-04-01

5. STILT: Unifying Spatial, Temporal and Textual Search using a Generalized Multi-dimensional Index;32nd International Conference on Scientific and Statistical Database Management;2020-07-07

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3