tutorial

Parasol: An Architecture for Cross-Cloud Federated Graph Querying

Authors:

Michael D. Lieberman,

Sutanay Choudhury,

Dennis Patrone,

Robert T. Hider, Jr.,

Christine D. Piatko,

Matthew Chapman,

David SilberbergAuthors Info & Claims

DanaC'14: Proceedings of Workshop on Data analytics in the Cloud

Pages 1 - 4

https://doi.org/10.1145/2627770.2627771

Published: 22 June 2014 Publication History

Abstract

Large scale data fusion of multiple datasets can often provide insights that individual datasets cannot. However, when these datasets reside in different data centers and cannot be collocated due to technical, administrative, or policy barriers, a unique set of problems arise that hamper querying and data fusion. To address these problems, a system and architecture named Parasol is presented that enables federated queries over graph databases residing in multiple clouds. Parasol's design is flexible and requires only minimal assumptions for client clouds. Query optimization techniques are also described that are compatible with Parasol's lightweight architecture. Experiments on a prototype implementation of Parasol indicate its suitability for cross-cloud federated graph queries.

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Cited By

Miao XZhang WJiang YFu FShao YChen LTao YCao GCui B(2022)P$$^2$$CG: a privacy preserving collaborative graph neural network training frameworkThe VLDB Journal10.1007/s00778-022-00768-832:4(717-736)Online publication date: 4-Nov-2022
https://doi.org/10.1007/s00778-022-00768-8

Index Terms

Parasol: An Architecture for Cross-Cloud Federated Graph Querying

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Published In

cover image ACM Conferences

DanaC'14: Proceedings of Workshop on Data analytics in the Cloud

June 2014

30 pages

ISBN:9781450329972

DOI:10.1145/2627770

Editors:
Asterios Katsifodimos
Technische Universität Berlin
,
Kostas Tzoumas
Technische Universität Berlin
,
Shivnath Babu
Duke University

Copyright © 2014 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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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2014

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SIGMOD/PODS'14

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SIGMOD

SIGMOD/PODS'14: International Conference on Management of Data

June 22 - 27, 2014

UT, Snowbird, USA

Acceptance Rates

DanaC'14 Paper Acceptance Rate 6 of 12 submissions, 50%;

Overall Acceptance Rate 19 of 34 submissions, 56%

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Cited By

Miao XZhang WJiang YFu FShao YChen LTao YCao GCui B(2022)P$$^2$$CG: a privacy preserving collaborative graph neural network training frameworkThe VLDB Journal10.1007/s00778-022-00768-832:4(717-736)Online publication date: 4-Nov-2022
https://doi.org/10.1007/s00778-022-00768-8

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