Search Results - Database query processing graph database

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  1. 1

    Neuro-Symbolic AI in Database Systems: A Systematic Review of Query Processing, Knowledge Graph Reasoning, Data Storage and Integration by Nzenwata, Uchenna Jeremiah, Ayankoya, Folasade Yetunde, Kuyoro, Shade Oluwakemi, Edwin, Emokiniovo

    ISSN: 2581-8260, 2581-8260
    Published: 25.11.2025
    “… This review looks at how these two approaches work together to improve database systems in areas like query processing, knowledge graph reasoning, and data representation…”
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    Journal Article
  2. 2

    PimBeam: Efficient Regular Path Queries Over Graph Database Using Processing-in-Memory by Kong, Weihan, Zheng, Shengan, Hua, Yifan, Ma, Ruoyan, Wen, Yuheng, Wang, Guifeng, Zhou, Cong, Huang, Linpeng

    ISSN: 1045-9219, 1558-2183
    Published: IEEE 01.05.2025
    “…Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall…”
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    Journal Article
  3. 3

    A parallel query processing system based on graph-based database partitioning by Nam, Yoon-Min, Han, Donghyoung, Kim, Min-Soo

    ISSN: 0020-0255, 1872-6291
    Published: Elsevier Inc 01.04.2019
    Published in Information sciences (01.04.2019)
    “…—despite their high data redundancy. We elucidate upon the drawbacks originating from the tree-based partitioning schemes and propose a novel graph-based database partitioning method called GPT that both improves the query performance…”
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    Journal Article
  4. 4

    A Unified Narrative for Query Processing in Graph Databases by Pang, Yue, Zou, Lei, Ozsu, M. Tamer

    ISSN: 2375-026X
    Published: IEEE 19.05.2025
    Published in Data engineering (19.05.2025)
    “…With the advent of graph data, graph databases have garnered significant research interest and efforts in recent years, especially with respect to graph query processing…”
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    Conference Proceeding
  5. 5

    Fast Dual Simulation Processing of Graph Database Queries by Mennicke, Stephan, Kalo, Jan-Christoph, Nagel, Denis, Kroll, Hermann, Balke, Wolf-Tilo

    ISSN: 2375-026X
    Published: IEEE 01.04.2019
    Published in Data engineering (01.04.2019)
    “…Graph database query languages feature expressive yet computationally expensive pattern matching capabilities…”
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    Conference Proceeding
  6. 6

    Data allocation optimization for query processing in graph databases using Lucene by Mathew, Anita Brigit

    ISSN: 0045-7906, 1879-0755
    Published: Amsterdam Elsevier Ltd 01.08.2018
    Published in Computers & electrical engineering (01.08.2018)
    “… A novel storage mechanism for effective query processing must to be established in graph databases for minimizing time overhead…”
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    Journal Article
  7. 7

    Fast processing of graph queries on a large database of small and medium-sized data graphs by Pal, Dipali, Rao, Praveen, Slavov, Vasil, Katib, Anas

    ISSN: 0022-0000, 1090-2724
    Published: Elsevier Inc 01.09.2016
    Published in Journal of computer and system sciences (01.09.2016)
    “…We propose a new way of indexing a large database of small and medium-sized graphs and processing exact subgraph matching…”
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    Journal Article
  8. 8

    A Graph-Based Database Partitioning Method for Parallel OLAP Query Processing by Yoon-Min Nam, Min-Soo Kim, Donghyoung Han

    ISSN: 2375-026X
    Published: IEEE 01.04.2018
    “…As the amount of data to process increases, a scalable and efficient horizontal database partitioning method becomes more important for OLAP query processing in parallel database platforms…”
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    Conference Proceeding
  9. 9

    MSP: Multiple Sub-graph Query Processing using Structure-based Graph Partitioning Strategy and Map-Reduce by Shaik Fathimabi, R.B.V. Subramanyam, D.V.L.N. Somayajulu

    ISSN: 1319-1578
    Published: Springer 01.01.2019
    “… Sub-graph query processing is a challenging problem in distributed environment. Centralized approaches proposed many algorithms, they mine frequent subgraphs from the graph database and construct an index which is very expensive…”
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    Journal Article
  10. 10

    On Processing Spatial Queries in Graph Database Management Systems by Sun, Yuhan

    ISBN: 9798728267737
    Published: ProQuest Dissertations & Theses 01.01.2021
    “…, graph or textual information, etc. Among all these compound spatial data, geospatial graph data is one of the most challenging for the complexity of graph data…”
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    Dissertation
  11. 11

    Accelerating Regular Path Queries over Graph Database with Processing-in-Memory by Ma, Ruoyan, Zheng, Shengan, Wang, Guifeng, Pu, Jin, Hua, Yifan, Wang, Wentao, Huang, Linpeng

    Published: ACM 23.06.2024
    “…Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall…”
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    Conference Proceeding
  12. 12

    Efficient algorithms for supergraph query processing on graph databases by Zhang, Shuo, Gao, Xiaofeng, Wu, Weili, Li, Jianzhong, Gao, Hong

    ISSN: 1382-6905, 1573-2886
    Published: Boston Springer US 01.02.2011
    Published in Journal of combinatorial optimization (01.02.2011)
    “…We study the problem of processing supergraph queries on graph databases. A graph database D is a large set of graphs…”
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    Journal Article
  13. 13

    Mining and indexing graph databases by Yuan, Dayu

    ISBN: 9781303782619, 1303782618
    Published: ProQuest Dissertations & Theses 01.01.2013
    “… There are two common search scenarios: subgraph search and supergraph search. In a subgraph search, given a query graph q, the algorithm searches for all graphs that have q as a subgraph, from a graph database…”
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    Dissertation
  14. 14

    Framework for constructing multimodal transport networks and routing using a graph database: A case study in London by Park, Seula, Cheng, Tao

    ISSN: 1361-1682, 1467-9671
    Published: Oxford Blackwell Publishing Ltd 01.08.2023
    Published in Transactions in GIS (01.08.2023)
    “… The constructed multimodal graph database showed stable performance in processing iterative queries, and efficient multi…”
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    Journal Article
  15. 15

    Accelerating Regular Path Queries over Graph Database with Processing-in-Memory by Ma, Ruoyan, Zheng, Shengan, Wang, Guifeng, Pu, Jin, Hua, Yifan, Wang, Wentao, Huang, Linpeng

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 15.03.2024
    Published in arXiv.org (15.03.2024)
    “…Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall…”
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    Paper
  16. 16

    The G graph database: efficiently managing large distributed dynamic graphs by Labouseur, Alan G., Birnbaum, Jeremy, Olsen, Paul W., Spillane, Sean R., Vijayan, Jayadevan, Hwang, Jeong-Hyon, Han, Wook-Shin

    ISSN: 0926-8782, 1573-7578
    Published: New York Springer US 01.12.2015
    “… Modern analytics require systems capable of storing and processing series of graphs. We present such a system. G…”
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    Journal Article
  17. 17

    SGIR-Tree: Integrating R-Tree Spatial Indexing as Subgraphs in Graph Database Management Systems by Kim, Juyoung, Hong, Seoyoung, Jeong, Seungchan, Park, Seula, Yu, Kiyun

    ISSN: 2220-9964, 2220-9964
    Published: Basel MDPI AG 01.10.2024
    “…Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data…”
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    Journal Article
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  19. 19

    Secrecy and performance models for query processing on outsourced graph data by Suntaxi, Gabriela, El Ghazi, Aboubakr Achraf, Böhm, Klemens

    ISSN: 0926-8782, 1573-7578
    Published: New York Springer US 01.03.2021
    “…Database outsourcing is a challenge concerning data secrecy. Even if an adversary, including the service provider, accesses the data, she should not be able to learn any information from the accessed data…”
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    Journal Article
  20. 20

    Fast Dual Simulation Processing of Graph Database Queries (Supplement) by Mennicke, Stephan, Jan-Christoph Kalo, Nagel, Denis, Kroll, Hermann, Wolf-Tilo Balke

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 22.10.2018
    Published in arXiv.org (22.10.2018)
    “…Graph database query languages feature expressive, yet computationally expensive pattern matching capabilities…”
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    Paper