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Google fusion tables: data management, integration and collaboration in the cloud

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Warren ShenAuthors Info & Claims

SoCC '10: Proceedings of the 1st ACM symposium on Cloud computing

Pages 175 - 180

https://doi.org/10.1145/1807128.1807158

Published: 10 June 2010 Publication History

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Abstract

Google Fusion Tables is a cloud-based service for data management and integration. Fusion Tables enables users to upload tabular data files (spreadsheets, CSV, KML), currently of up to 100MB. The system provides several ways of visualizing the data (e.g., charts, maps, and timelines) and the ability to filter and aggregate the data. It supports the integration of data from multiple sources by performing joins across tables that may belong to different users. Users can keep the data private, share it with a select set of collaborators, or make it public and thus crawlable by search engines. The discussion feature of Fusion Tables allows collaborators to conduct detailed discussions of the data at the level of tables and individual rows, columns, and cells. This paper describes the inner workings of Fusion Tables, including the storage of data in the system and the tight integration with the Google Maps infrastructure.

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Google fusion tables: data management, integration and collaboration in the cloud

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Information

Published In

SoCC '10: Proceedings of the 1st ACM symposium on Cloud computing

June 2010

264 pages

ISBN:9781450300360

DOI:10.1145/1807128

General Chair:
Joseph M. Hellerstein
University of California, Berkeley, USA
,
Program Chairs:
Surajit Chaudhuri
Microsoft Research, USA
,
Mendel Rosenblum
Stanford University, USA

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

Publication History

Published: 10 June 2010

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SOCC '10

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SOCC '10: ACM SIGMOD-SIGOPS Workshop on Cloud Computing in conjunction with SIGMOD 2010

June 10 - 11, 2010

Indiana, Indianapolis, USA

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Overall Acceptance Rate 169 of 722 submissions, 23%

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Xiong WWu YOuyang XJia QChen HChen LXiong WWu YOuyang XJia QChen HChen L(2024)Multi-paradigm Geocomputing TechniquesHigh Performance Geographic Information System10.1007/978-981-97-7170-7_4(143-177)Online publication date: 16-Oct-2024
https://doi.org/10.1007/978-981-97-7170-7_4
Jin DMi RSong T(2024)Table Orientation Classification Model Based on BERT and TCSMNIntelligent Information Processing XII10.1007/978-3-031-57808-3_4(48-62)Online publication date: 6-Apr-2024
https://doi.org/10.1007/978-3-031-57808-3_4
Soni PDe Runz CBouali FVenturini G(2023)A Genetic Algorithm for Automatic Dashboard Generation: First Results2023 27th International Conference Information Visualisation (IV)10.1109/IV60283.2023.00023(77-82)Online publication date: 25-Jul-2023
https://doi.org/10.1109/IV60283.2023.00023
Suri SIlyas IRé CRekatsinas T(2022)EmberProceedings of the VLDB Endowment10.14778/3494124.349414915:3(699-712)Online publication date: 4-Feb-2022
https://dl.acm.org/doi/10.14778/3494124.3494149
Trabelsi MChen ZZhang SDavison BHeflin J(2022)StruBERT: Structure-aware BERT for Table Search and MatchingProceedings of the ACM Web Conference 202210.1145/3485447.3511972(442-451)Online publication date: 25-Apr-2022
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Zeng ZMoh PDu FHoffswell JLee TMalik SKoh EBattle L(2022)An Evaluation-Focused Framework for Visualization Recommendation AlgorithmsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311481428:1(346-356)Online publication date: 1-Jan-2022
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Abimbola B(2021)Trends in Data Warehousing TechnologyInternational Journal of Data Science and Big Data Analytics10.51483/IJDSBDA.1.3.2021.15-211:3(15)Online publication date: 5-Nov-2021
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Erlacher CAnders KJankowski PPaulus GBlaschke T(2021)A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria ModelsISPRS International Journal of Geo-Information10.3390/ijgi1004024410:4(244)Online publication date: 7-Apr-2021
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