research-article

BBoxDB - A Scalable Data Store for Multi-Dimensional Big Data

Authors:

Jan Kristof Nidzwetzki,

Ralf Hartmut GütingAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 1867 - 1870

https://doi.org/10.1145/3269206.3269208

Published: 17 October 2018 Publication History

Abstract

BBoxDB is a distributed and highly available key-bounding-box-value store which enhances the classical key-value data model with an axis-parallel bounding box. The bounding box describes the location of the values in an n-dimensional space, and enables BBoxDB to efficiently distribute multi-dimensional data across a cluster of nodes. Well-known geometric algorithms (such as the K-D Tree) are used to create distribution regions (multi-dimensional shards). Distribution regions are created dynamically, based on the stored data. BBoxDB stores data of multiple tables co-partitioned, which enables efficient distributed spatial joins. Spatial joins on co-partitioned tables can be executed without data shuffling between nodes. A two-level index structure is employed to retrieve stored data quickly. We demonstrate the interaction with the system, the dynamic creation of distribution regions and the data redistribution feature of BBoxDB.

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Cited By

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https://doi.org/10.1109/TBDATA.2023.3342619
He HXu ZLi RBao JLi TZheng Y(2024)TMan: A High-Performance Trajectory Data Management System Based on Key-Value Stores2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00376(4951-4964)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00376
Wu HWang BZhang MLi GLi RLiu Y(2024)CUPID: An efficient spatio-temporal data engineFuture Generation Computer Systems10.1016/j.future.2024.07.031161(531-544)Online publication date: Dec-2024
https://doi.org/10.1016/j.future.2024.07.031
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Index Terms

BBoxDB - A Scalable Data Store for Multi-Dimensional Big Data
1. Information systems
  1. Data management systems

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Published In

cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Published: 17 October 2018

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Liang HZhang ZHu CGong YCheng D(2024)A Survey on Spatio-Temporal Big Data Analytics Ecosystem: Resource Management, Processing Platform, and ApplicationsIEEE Transactions on Big Data10.1109/TBDATA.2023.334261910:2(174-193)Online publication date: Apr-2024
https://doi.org/10.1109/TBDATA.2023.3342619
He HXu ZLi RBao JLi TZheng Y(2024)TMan: A High-Performance Trajectory Data Management System Based on Key-Value Stores2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00376(4951-4964)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00376
Wu HWang BZhang MLi GLi RLiu Y(2024)CUPID: An efficient spatio-temporal data engineFuture Generation Computer Systems10.1016/j.future.2024.07.031161(531-544)Online publication date: Dec-2024
https://doi.org/10.1016/j.future.2024.07.031
Yang XGuan XPang ZKui XWu H(2024)GridMesaFuture Generation Computer Systems10.1016/j.future.2024.02.010155:C(324-339)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.future.2024.02.010
Liu HYan JWang JChen BChen MHuang X(2023)HGST: A Hilbert-GeoSOT Spatio-Temporal Meshing and Coding Method for Efficient Spatio-Temporal Range Query on Massive Trajectory DataISPRS International Journal of Geo-Information10.3390/ijgi1203011312:3(113)Online publication date: 7-Mar-2023
https://doi.org/10.3390/ijgi12030113
Chen ZCong GAref W(2023)STAR: A Cache-based Stream Warehouse System for Spatial DataACM Transactions on Spatial Algorithms and Systems10.1145/36059449:4(1-27)Online publication date: 27-Jun-2023
https://dl.acm.org/doi/10.1145/3605944
Liu KTong PLi MWu YHuang J(2023)ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at ScaleProceedings of the ACM on Management of Data10.1145/35889411:1(1-28)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588941
Vu TEldawy AHristidis VTsotras V(2022)Incremental partitioning for efficient spatial data analyticsProceedings of the VLDB Endowment10.14778/3494124.349415015:3(713-726)Online publication date: 4-Feb-2022
https://dl.acm.org/doi/10.14778/3494124.3494150
Alam MTorgo LBifet A(2022)A Survey on Spatio-temporal Data Analytics SystemsACM Computing Surveys10.1145/350790454:10s(1-38)Online publication date: 10-Nov-2022
https://dl.acm.org/doi/10.1145/3507904
Li RHe HWang RRuan SHe TBao JZhang JHong LZheng Y(2022)TrajMesa: A Distributed NoSQL-Based Trajectory Data Management SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3079880(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2021.3079880
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