Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases

Author:

Aydin Berkay1,Akkineni Vijay1,Angryk Rafal A1

Affiliation:

1. Georgia State University, USA

Abstract

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.

Publisher

IGI Global

Reference29 articles.

1. ERMO-DG: Evolving Region Moving Object Dataset Generator.;B.Aydin;Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference FLAIRS 2014,2014

2. Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns

3. Towards robust distributed systems (abstract)

4. Similarity of trajectories taking into account geographic context

Cited by 7 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. Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data;ISPRS International Journal of Geo-Information;2024-06-12

2. A Literature Review on Teaching and Learning Database Normalisation: Approaches and Tools;Lecture Notes in Networks and Systems;2024

3. Efficient Data Management for Intelligent Urban Mobility Systems;Proceedings of the Workshop on Data-Driven and Intelligent Cyber-Physical Systems;2021-05-18

4. Flower Master Index for Relational Database Selection and Joining;Towards Digital Intelligence Society;2020-12-22

5. Spatiotemporal Interpolation Methods for Solar Event Trajectories;The Astrophysical Journal Supplement Series;2018-05-11

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