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GraphMiner: a structural pattern-mining system for large disk-based graph databases and its applications

Authors:

Jiawei HanAuthors Info & Claims

SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data

Pages 879 - 881

https://doi.org/10.1145/1066157.1066273

Published: 14 June 2005 Publication History

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Abstract

Mining frequent structural patterns from graph databases is an important research problem with broad applications. Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques. The techniques have been integrated into a research prototype system--- GraphMiner. In this paper, we describe a demo of GraphMiner which showcases the technical details of the index structure and the mining algorithms including their efficient implementation, the mining performance and the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications.

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Cited By

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Dao HRavikumar PLikhitha PRage UWatanobe YPaik I(2023)Finding Stable Periodic-Frequent Itemsets in Big Columnar DatabasesIEEE Access10.1109/ACCESS.2023.324131311(12504-12524)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3241313
Fu TWan ZDai GWang YYang H(2020)LessMine: Reducing Sample Space and Data Access for Dense Pattern Mining2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286187(1-7)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286187
Giarelis NKanakaris NKaracapilidis N(2020)On a Novel Representation of Multiple Textual Documents in a Single GraphIntelligent Decision Technologies10.1007/978-981-15-5925-9_9(105-115)Online publication date: 12-Jun-2020
https://doi.org/10.1007/978-981-15-5925-9_9
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GraphMiner: a structural pattern-mining system for large disk-based graph databases and its applications
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Published In

SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data

June 2005

990 pages

ISBN:1595930604

DOI:10.1145/1066157

Conference Chair:
Fatma Ozcan
IBM Almaden Research Center

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

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Published: 14 June 2005

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SIGMOD/PODS05: International Conference on Management of Data and Symposium on Principles Database and Systems

June 14 - 16, 2005

Maryland, Baltimore

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Dao HRavikumar PLikhitha PRage UWatanobe YPaik I(2023)Finding Stable Periodic-Frequent Itemsets in Big Columnar DatabasesIEEE Access10.1109/ACCESS.2023.324131311(12504-12524)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3241313
Fu TWan ZDai GWang YYang H(2020)LessMine: Reducing Sample Space and Data Access for Dense Pattern Mining2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286187(1-7)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286187
Giarelis NKanakaris NKaracapilidis N(2020)On a Novel Representation of Multiple Textual Documents in a Single GraphIntelligent Decision Technologies10.1007/978-981-15-5925-9_9(105-115)Online publication date: 12-Jun-2020
https://doi.org/10.1007/978-981-15-5925-9_9
Ayed RHacid MHaque RJemai A(2019)An updated dashboard of complete search FSM implementations in centralized graph transaction databasesJournal of Intelligent Information Systems10.1007/s10844-019-00579-4Online publication date: 20-Dec-2019
https://doi.org/10.1007/s10844-019-00579-4
Borah ANath B(2018)Rare pattern mining: challenges and future perspectivesComplex & Intelligent Systems10.1007/s40747-018-0085-9Online publication date: 10-Nov-2018
https://doi.org/10.1007/s40747-018-0085-9
Tie JChen WSun CMao TXing G(2018)The application of agglomerative hierarchical spatial clustering algorithm in tea blendingCluster Computing10.1007/s10586-018-1813-zOnline publication date: 30-Jan-2018
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Joshi ABansal ASai Sabitha AChoudhury T(2017)An Efficient Way to Find Frequent Patterns Using Graph Mining and Network Analysis Techniques on United States Airports NetworkSmart Computing and Informatics10.1007/978-981-10-5547-8_32(301-316)Online publication date: 29-Oct-2017
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Sun CCai XHu YChen WTie J(2016)Clustering-Based Algorithms to Semantic Summarizing Graph with Multi-attributes’ Hierarchical Structures2016 IEEE 13th International Conference on e-Business Engineering (ICEBE)10.1109/ICEBE.2016.021(64-70)Online publication date: Nov-2016
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Grossi VPedreschi DTurini F(2016)Data Mining and Constraints: An OverviewData Mining and Constraint Programming10.1007/978-3-319-50137-6_2(25-48)Online publication date: 3-Dec-2016
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Kimmett BThomo AVenkatesh S(2014)Three-way joins on MapReduce: An experimental studyIISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications10.1109/IISA.2014.6878811(227-232)Online publication date: Jul-2014
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