research-article

Database system comparison based on spatiotemporal functionality

Authors:

Antonios Makris,

Konstantinos Tserpes,

Dimosthenis Anagnostopoulos,

Mara Nikolaidou,

Jose Antônio Fernandes de MacedoAuthors Info & Claims

IDEAS '19: Proceedings of the 23rd International Database Applications & Engineering Symposium

Article No.: 21, Pages 1 - 7

https://doi.org/10.1145/3331076.3331101

Published: 10 June 2019 Publication History

Abstract

The amount of sources and sheer volumes of spatiotemporal data have met an unprecedented growth during the last decade. As a consequence, a rapidly increasing number of applications are seeking to generate value by crunching those data. The development of a system that will tap into the potential value of the spatiotemporal big data analysis for a multitude of applications remains one of the biggest challenges in computer engineering. This paper delves into the key-characteristics of the most prominent suchlike systems. In particular, it provides a thorough analysis of NoSQL datastores as well as a traditional relational database system in terms of their geospatial querying capabilities.

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Index Terms

Database system comparison based on spatiotemporal functionality

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Information & Contributors

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Published In

cover image ACM Other conferences

IDEAS '19: Proceedings of the 23rd International Database Applications & Engineering Symposium

June 2019

364 pages

ISBN:9781450362498

DOI:10.1145/3331076

General Chairs:
Bipin C. Desai
Concordia University
,
Dimosthenis Anagnostopoulos
Harokopio University of Athens
,
Program Chairs:
Yannis Manolopoulos
Open University of Cyprus
,
Mara Nikolaidou
Harokopio University of Athens

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 10 June 2019

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European Union

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IDEAS 2019

IDEAS 2019: 23rd International Database Engineering & Applications Symposium

June 10 - 12, 2019

Athens, Greece

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Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

Liu RZhou SYin SShu YLiang M(2024)AIS-Based Vessel Trajectory Compression: A Systematic Review and Software DevelopmentIEEE Open Journal of Vehicular Technology10.1109/OJVT.2024.34436755(1193-1214)Online publication date: 2024
https://doi.org/10.1109/OJVT.2024.3443675
Guo YZhu QDing YLi YWu HHe YLi ZLi HZhang LZhao YPan YYang TLiu MZeng H(2024)Efficient distributed association management method of data, model, and knowledge for digital twin railwayInternational Journal of Digital Earth10.1080/17538947.2024.234008917:1Online publication date: 2-May-2024
https://doi.org/10.1080/17538947.2024.2340089
Liu ZYuan WLiang MZhang MLiu CLiu RLiu J(2024)An online method for ship trajectory compression using AIS dataJournal of Navigation10.1017/S0373463324000171(1-22)Online publication date: 31-May-2024
https://doi.org/10.1017/S0373463324000171
Liu YOuyang XZhou HPeng WMao JPan Y(2024)Leveraging Spatial Characteristics in Trajectory Compression: An Angle-Based Bounded-Error MethodData Science10.1007/978-981-97-8746-3_16(239-254)Online publication date: 31-Oct-2024
https://doi.org/10.1007/978-981-97-8746-3_16
Vitale VMartino SPeron ARusso MBattista E(2023)How to manage massive spatiotemporal dataset from stationary and non-stationary sensors in commercial DBMS?Knowledge and Information Systems10.1007/s10115-023-02009-y66:3(2063-2088)Online publication date: 20-Nov-2023
https://doi.org/10.1007/s10115-023-02009-y
Kontopoulos IMakris AXyalis STserpes K(2022)Benchmarking moving object functionalities of DBMSs using real-world spatiotemporal workload2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00087(388-393)Online publication date: Jun-2022
https://doi.org/10.1109/MDM55031.2022.00087
ElZonkoly MMadbouly MGurguis S(2022)Real Time Adaptive GPS Trajectory CompressionProceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 202210.1007/978-3-031-20601-6_32(354-369)Online publication date: 18-Nov-2022
https://doi.org/10.1007/978-3-031-20601-6_32
Kontopoulos IMakris ATserpes K(2021)A Deep Learning Streaming Methodology for Trajectory ClassificationISPRS International Journal of Geo-Information10.3390/ijgi1004025010:4(250)Online publication date: 8-Apr-2021
https://doi.org/10.3390/ijgi10040250
Bollen EHendrix RKuijpers BVaisman A(2021)Towards the Internet of Water: Using graph databases for hydrological analysis on the Flemish river systemTransactions in GIS10.1111/tgis.1280125:6(2907-2938)Online publication date: 16-Jul-2021
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Makris AKontopoulos IAlimisis PTserpes K(2021)A Comparison of Trajectory Compression Algorithms Over AIS DataIEEE Access10.1109/ACCESS.2021.30929489(92516-92530)Online publication date: 2021
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