[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/2676536.2676538acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article

High performance integrated spatial big data analytics

Published: 04 November 2014 Publication History

Abstract

The growth of spatial big data has been explosive thanks to cost-effective and ubiquitous positioning technologies, and the generation of data from multiple sources in multi-forms. Such emerging spatial data has high potential to create new insights and values for our life through spatial analytics. However, spatial data analytics faces two major challenges. First, spatial data is both data-and compute-intensive due to the massive amounts of data and the multi-dimensional nature, which requires high performance spatial computing infrastructure and methods. Second, spatial big data sources are often isolated, for example, OpenStreetMap, census data and Twitter tweets are independent data sources. This leads to incompleteness of information and sometimes limited data accuracy, thus limited values from the data. Integrating spatial big data analytics by consolidating multiple data sources provides significant potential for data quality improvement in terms of completeness and accuracy, and much increased values derived from the data. In this paper, we present our vision of a high performance integrated spatial big data analytics framework. We provide a scalable spatial query based data integration engine with MapReduce, and demonstrate integrated spatial data analytics through a few use cases in our preliminary work. We then present our future plan on integrated spatial big data analytics for improving public health research and applications.

References

[1]
A. Aji, X. Sun, H. Vo, Q. Liu, R. Lee, X. Zhang, J. Saltz, and F. Wang. Demonstration of hadoop-gis: A spatial data warehousing system over mapreduce. In SIGSPATIAL/GIS, pages 518--521, 2013.
[2]
A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, and J. Saltz. Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce. Proc. VLDB Endow., 6(11):1009--1020, Aug. 2013.
[3]
M. F. Goodchild. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4):211--221, 2007.
[4]
B. Hecht and M. Stephens. A tale of cities: Urban biases in volunteered geographic information. 2014.
[5]
W. Liu, F. Al Zamal, and D. Ruths. Using social media to infer gender composition of commuter populations. In ICWSM, 2012.
[6]
D. J. McIver and J. S. Brownstein. Wikipedia usage estimates prevalence of influenza-like illness in the united states in near real-time. PLoS computational biology, 10(4):e1003581, 2014.
[7]
E. Mohammady and A. Culotta. Using county demographics to infer attributes of twitter users. ACL Joint Workshop on Social Dynamics and Personal Attributes in Social Media, 2014.
[8]
C. on the Analysis of Massive Data. Frontiers in massive data analysis, 2013.
[9]
A. Sadilek and H. Kautz. Modeling the impact of lifestyle on health at scale. In WSDM, pages 637--646, 2013.
[10]
H. Vo, A. Aji, and F. Wang. SATO: A Spatial Data Partitioning Framework for Scalable Query Processing. In SIGSPATIAL/GIS, 2014.
[11]
J. Xu, T. L. Wickramarathne, N. V. Chawla, E. K. Grey, K. Steinhaeuser, R. P. Keller, J. M. Drake, and D. M. Lodge. Improving management of aquatic invasions by integrating shipping network, ecological, and environmental data: Data mining for social good. In SIGKDD 2014, 2014.
[12]
Y. Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: concepts, methodologies, and applications. TIST, 2014.

Cited By

View all
  • (2023)A comparative study of big data use in Egyptian agricultureJournal of Electrical Systems and Information Technology10.1186/s43067-023-00090-510:1Online publication date: 4-Apr-2023
  • (2021)Use of fractals to measure anisotropy in point patterns extracted with the DPT of an imageSpatial Statistics10.1016/j.spasta.2020.10045242(100452)Online publication date: Apr-2021
  • (2019)Review of Big Data and Processing Frameworks for Disaster Response ApplicationsISPRS International Journal of Geo-Information10.3390/ijgi80903878:9(387)Online publication date: 3-Sep-2019
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
BigSpatial '14: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
November 2014
69 pages
ISBN:9781450331326
DOI:10.1145/2676536
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2014

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. GIS
  2. MapReduce
  3. data warehouse
  4. database
  5. spatial analytics

Qualifiers

  • Research-article

Funding Sources

Conference

SIGSPATIAL '14
Sponsor:

Acceptance Rates

BigSpatial '14 Paper Acceptance Rate 8 of 13 submissions, 62%;
Overall Acceptance Rate 32 of 58 submissions, 55%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)1
Reflects downloads up to 06 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)A comparative study of big data use in Egyptian agricultureJournal of Electrical Systems and Information Technology10.1186/s43067-023-00090-510:1Online publication date: 4-Apr-2023
  • (2021)Use of fractals to measure anisotropy in point patterns extracted with the DPT of an imageSpatial Statistics10.1016/j.spasta.2020.10045242(100452)Online publication date: Apr-2021
  • (2019)Review of Big Data and Processing Frameworks for Disaster Response ApplicationsISPRS International Journal of Geo-Information10.3390/ijgi80903878:9(387)Online publication date: 3-Sep-2019
  • (2018)Spatiotemporal Aspects of Big DataApplied Computer Systems10.2478/acss-2018-001223:2(90-100)Online publication date: 31-Dec-2018
  • (2017)Effective Scalable and Integrative Geocoding for Massive Address DatasetsProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139986(1-10)Online publication date: 7-Nov-2017
  • (2017)A large-scale spatio-temporal data analytics system for wildfire risk managementProceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3080546.3080549(1-6)Online publication date: 14-May-2017
  • (2017)Clustering of Geospatial Big Data in a Distributed EnvironmentEncyclopedia of GIS10.1007/978-3-319-17885-1_1625(236-246)Online publication date: 12-May-2017
  • (2016)ORANGE: Spatial big data analysis platform2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7841076(3963-3965)Online publication date: Dec-2016
  • (2016)Clustering of Geospatial Big Data in a Distributed EnvironmentEncyclopedia of GIS10.1007/978-3-319-23519-6_1625-1(1-11)Online publication date: 10-Feb-2016
  • (2015)Big data analytics: a literature reviewJournal of Management Analytics10.1080/23270012.2015.10824492:3(175-201)Online publication date: 13-Oct-2015

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media