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Microblogging Queries on Graph Databases: An Introspection

Authors:

Oshini Goonetilleke,

Saket Sathe,

Timos Sellis,

Xiuzhen ZhangAuthors Info & Claims

GRADES'15: Proceedings of the GRADES'15

Article No.: 5, Pages 1 - 6

https://doi.org/10.1145/2764947.2764952

Published: 31 May 2015 Publication History

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Abstract

Microblogging data is growing at a rapid pace. This poses new challenges to the data management systems, such as graph databases, that are typically suitable for analyzing such data. In this paper, we share our experience on executing a wide variety of micro-blogging queries on two popular graph databases: Neo4j and Sparksee. Our queries are designed to be relevant to popular applications of micro-blogging data. The queries are executed on a large real graph data set comprising of nearly 50 million nodes and 326 million edges.

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Cited By

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Ataullah ATompa F(2020)Business policy modeling and enforcement in databasesProceedings of the VLDB Endowment10.14778/3402707.34027304:11(921-931)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402707.3402730
Levandoski JEkstrand MLudwig MEldawy AMokbel MRiedl J(2020)RecBenchProceedings of the VLDB Endowment10.14778/3402707.34027294:11(911-920)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402707.3402729
Balegas VDuarte SFerreira CRodrigues RPreguiça N(2018)IPAProceedings of the VLDB Endowment10.14778/3297753.329776012:4(404-418)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.14778/3297753.3297760
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Microblogging Queries on Graph Databases: An Introspection

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GRADES'15: Proceedings of the GRADES'15

May 2015

54 pages

ISBN:9781450336116

DOI:10.1145/2764947

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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Published: 31 May 2015

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SIGMOD/PODS'15

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SIGMOD

SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

VIC, Melbourne, Australia

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Overall Acceptance Rate 29 of 61 submissions, 48%

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Ataullah ATompa F(2020)Business policy modeling and enforcement in databasesProceedings of the VLDB Endowment10.14778/3402707.34027304:11(921-931)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402707.3402730
Levandoski JEkstrand MLudwig MEldawy AMokbel MRiedl J(2020)RecBenchProceedings of the VLDB Endowment10.14778/3402707.34027294:11(911-920)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402707.3402729
Balegas VDuarte SFerreira CRodrigues RPreguiça N(2018)IPAProceedings of the VLDB Endowment10.14778/3297753.329776012:4(404-418)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.14778/3297753.3297760
Lissandrini MBrugnara MVelegrakis Y(2018)Beyond macrobenchmarksProceedings of the VLDB Endowment10.14778/3297753.329775912:4(390-403)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.14778/3297753.3297759
Huang EPeng LPalma LAbdelkafi ALiu ADiao Y(2018)Optimization for active learning-based interactive database explorationProceedings of the VLDB Endowment10.14778/3275536.327554212:1(71-84)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.14778/3275536.3275542
Yang JFan JWei ZLi GLiu TDu X(2018)Cost-effective data annotation using game-based crowdsourcingProceedings of the VLDB Endowment10.14778/3275536.327554112:1(57-70)Online publication date: 1-Sep-2018
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Kim SLee THwang SElnikety S(2018)List intersection for web searchProceedings of the VLDB Endowment10.14778/3275536.327553712:1(1-13)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.14778/3275536.3275537
Palkar SAbuzaid FBailis PZaharia M(2018)Filter before you parseProceedings of the VLDB Endowment10.14778/3236187.323620711:11(1576-1589)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.14778/3236187.3236207
Iwabuchi KSallinen SPearce REssen BGokhale MMatsuoka S(2016)Towards a Distributed Large-Scale Dynamic Graph Data Store2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2016.189(892-901)Online publication date: May-2016
https://doi.org/10.1109/IPDPSW.2016.189

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Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries

Analyzing graph databases by aggregate queries

Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB

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