Periscope/GQ: A Graph Querying Toolkit

被引:4
|
作者
Tian, Yuanyuan [1 ]
Patel, Jignesh M. [1 ]
Nair, Viji [2 ]
Martini, Sebastian [2 ]
Kretzler, Matthias [2 ]
机构
[1] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Dept Internal Med, Ann Arbor, MI 48109 USA
来源
PROCEEDINGS OF THE VLDB ENDOWMENT | 2008年 / 1卷 / 02期
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
D O I
10.14778/1454159.1454184
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Real life data can often be modeled as graphs, in which nodes represent objects and edges between nodes indicate their relationships. Large graph datasets are common in many emerging applications. Examples span from social networks, biological networks to computer networks. To fully exploit the wealth of information encoded in graphs, systems for managing and analyzing graph data are critical. To address this need, we have designed and developed a graph querying toolkit, called Periscope/GQ. This toolkit is built on top of a traditional RDBMS. It provides a uniform schema for storing graphs in the database and supports various graph query operations, especially sophisticated operations, such as approximate graph matching, large graph alignment and graph summarization. Users can easily combine several operations to perform complex analysis on graphs. In addition, Periscope/GQ employs several novel indexing techniques to speed up query execution. This demonstration will highlight the use of Periscope/GQ in two application domains: life science and social networking.
引用
收藏
页码:1404 / 1407
页数:4
相关论文
共 50 条
  • [31] Querying Process Models Repositories by Aggregated Graph Search
    Sakr, Sherif
    Awad, Ahmed
    Kunze, Matthias
    BUSINESS PROCESS MANAGEMENT WORKSHOPS (BPM), 2013, 132 : 573 - 585
  • [32] OPQL: Querying scientific workflow provenance at the graph level
    Lim, Chunhyeok
    Lu, Shiyong
    Chebotko, Artem
    Fotouhi, Farshad
    Kashlev, Andrey
    DATA & KNOWLEDGE ENGINEERING, 2013, 88 : 37 - 59
  • [33] Representing and querying disease networks using graph databases
    Lysenko, Artem
    Roznovat, Irina A.
    Saqi, Mansoor
    Mazein, Alexander
    Rawlings, Christopher J.
    Auffray, Charles
    BIODATA MINING, 2016, 9
  • [34] Representing and querying disease networks using graph databases
    Artem Lysenko
    Irina A. Roznovăţ
    Mansoor Saqi
    Alexander Mazein
    Christopher J Rawlings
    Charles Auffray
    BioData Mining, 9
  • [35] Decency and Decentralisation: Verifiable Decentralised Knowledge Graph Querying
    Third, Aisling
    Domingue, John
    COMPANION OF THE WORLD WIDE WEB CONFERENCE, WWW 2023, 2023, : 1432 - 1434
  • [36] Querying a graph database - language selection and performance considerations
    Holzschuher, Florian
    Peinl, Rene
    JOURNAL OF COMPUTER AND SYSTEM SCIENCES, 2016, 82 (01) : 45 - 68
  • [37] Automated Query Graph Generation for Querying Knowledge Graphs
    Zheng, Weiguo
    Zhang, Mei
    PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, CIKM 2021, 2021, : 2698 - 2707
  • [38] Interactive Knowledge Graph Querying Through Examples and Facets
    Amsterdamer, Yael
    Gaspar, Laura
    NEW TRENDS IN DATABASE AND INFORMATION SYSTEMS, ADBIS 2022, 2022, 1652 : 201 - 211
  • [39] Query Graph Visualizer: A Visual Collaborative Querying System
    Goh, Dion Hoe-Lian
    Chua, Alton Y. K.
    Lee, Chei Sian
    Luyt, Brendan
    2008 FIRST INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES, VOLS 1 AND 2, 2008, : 85 - 90
  • [40] Querying Wikidata: Comparing SPARQL, Relational and Graph Databases
    Hernandez, Daniel
    Hogan, Aidan
    Riveros, Cristian
    Rojas, Carlos
    Zerega, Enzo
    SEMANTIC WEB - ISWC 2016, PT II, 2016, 9982 : 88 - 103