μ-Bench: Real-world Micro Benchmarking for SPARQL Query Processing over Knowledge Graphs

被引:0
|
作者
Saleem, Muhammad [1 ]
Akhter, Adnan [1 ]
Vahdati, Sahar [2 ]
Ngomo, Axel Cyrille Ngonga [1 ]
机构
[1] Univ Paderborn, Data Sci DICE Grp, Paderborn, Germany
[2] Inst Appl Informat InfAI, Dresden, Germany
关键词
D O I
10.1145/3579051.3579054
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Real-world SPARQL querying benchmarks, which make use of the real-world RDF datasets and/or SPARQL queries, are the key element in testing the performance of different RDF Knowledge graph management systems in real-world settings. Over the last years, various real-world SPARQL querying benchmarks have been proposed. Although useful for general purpose SPARQL benchmarking, they do not allow generating microbenchmarks, i.e., small customized benchmarks according to the user specified criteria for a specific use case. These microbenchmarks are important to perform component-based testing, hence pinpoint pros and cons of the systems at micro level. We propose..-Bench, a microbenchmarking framework for SPARQL query processing over RDF knowledge graphs. The framework makes use of the real-world (collected from query logs of public SPARQL endpoints) SPARQL queries to generate customized benchmarks according to the user defined criteria. The framework utilizes various clustering algorithms to select diverse benchmarks from the given input query log. We generated various microbenchmarks and evaluated state-of-the-art knowledge graph engines. The evaluation results show that specialized microbenchmarking is crucial for identifying the limitations of the various SPARQL query processing engines and other corresponding components.
引用
收藏
页码:39 / 47
页数:9
相关论文
共 50 条
  • [31] Group knowledge: a real-world approach
    Klausen, Soren Harnow
    SYNTHESE, 2015, 192 (03) : 813 - 839
  • [32] KNOWLEDGE AND THE REAL-WORLD, HARSA AND THE PRAMANAS
    RAMPRASAD, C
    JOURNAL OF INDIAN PHILOSOPHY, 1993, 21 (02) : 169 - 203
  • [33] Question Answering over Knowledge Graphs with Query Path Generation
    Yang, Linqing
    Guo, Kecen
    Liu, Bo
    Gong, Jiazheng
    Zhang, Zhujian
    Zhao, Peiyu
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT I, 2022, 13368 : 146 - 158
  • [34] Complex Query Augmentation for Question Answering over Knowledge Graphs
    Abdelkawi, Abdelrahman
    Zafar, Hamid
    Maleshkova, Maria
    Lehmann, Jens
    ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2019 CONFERENCES, 2019, 11877 : 571 - 587
  • [35] LinE: Logical Query Reasoning over Hierarchical Knowledge Graphs
    Huang, Zijian
    Chiang, Meng-Fen
    Lee, Wang-Chien
    PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 615 - 625
  • [36] Graph Embedding based Query Construction over Knowledge Graphs
    Wang, Ruijie
    Wang, Meng
    Liu, Jun
    Yao, Siyu
    Zheng, Qinghua
    2018 9TH IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (ICBK), 2018, : 1 - 8
  • [37] Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search
    Hu, Xin
    Duan, Jiangli
    Dang, Depeng
    KNOWLEDGE AND INFORMATION SYSTEMS, 2021, 63 (04) : 819 - 844
  • [38] Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search
    Xin Hu
    Jiangli Duan
    Depeng Dang
    Knowledge and Information Systems, 2021, 63 : 819 - 844
  • [39] ArchimedesOne: Query Processing over Probabilistic Knowledge Bases
    Zhou, Xiaofeng
    Chen, Yang
    Wang, Daisy Zhe
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2016, 9 (13): : 1461 - 1464
  • [40] Modeling Real-World Load Patterns for Benchmarking in Clouds and Clusters
    Qazi, Kashifuddin
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (06) : 1 - 11