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PandaDB: An AI-Native Graph Database for Unified Managing Structured and Unstructured Data

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13946))

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Abstract

In many applications, data are organized as graphs (e.g., social network and smart city). There could be unstructured data on such a graph, for example, the users’ avatars and images included in a post. It is natural to think of these unstructured data as attributes of nodes or relationships. Then the users would tend to query the semantic information of unstructured data on the graph, namely hybrid queries. To meet the demand of hybrid queries, this paper introduces PandaDB, an AI-native graph database, and it has the following characteristics: (1) Unified management of unstructured data and graph data. (2) Online extracting and indexing semantic information of unstructured data. (3) Optimization of hybrid queries. The system and its concept have been verified by multiple applications based on it. Users could deploy PandaDB to support hybrid queries and data mining.

This work was supported by the National Key R &D Program of China (Grant No. 2021YFF0704200) and Informatization Plan of Chinese Academy of Sciences (Grant No. CAS-WX2022GC-02).

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Notes

  1. 1.

    The project is open-sourced at: https://github.com/grapheco/pandadb-v0.1,.

  2. 2.

    http://moviedata.csuldw.com/.

  3. 3.

    https://github.com/grapheco/pandadb-v0.1/blob/master/demo.gif.

References

  1. Erling, O., et al.: The LDBC social network benchmark: interactive workload. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (2015)

    Google Scholar 

  2. Usman, M., et al.: A survey on big multimedia data processing and management in smart cities. ACM Comput. Surv. (CSUR) 52(3), 1–29 (2019)

    Article  Google Scholar 

  3. Francis, N., et al.: Cypher: an evolving query language for property graphs. In: Proceedings of the 2018 International Conference on Management of Data (2018)

    Google Scholar 

  4. Zhihong, S., Chang, Y., Hou Yanfei, W., Linhuan, L.Y.: Big linked data management: challenges, solutions and practices. Data Anal. Knowl. Disc. 2(1), 9–20 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Network Information Center, CAS, Beijing, China

    Zihao Zhao, Zhihong Shen, Along Mao, Huajin Wang & Chuan Hu

  2. University of Chinese Academy of Sciences, Beijing, China

    Zihao Zhao, Along Mao & Chuan Hu

Authors
  1. Zihao Zhao
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  2. Zhihong Shen
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  3. Along Mao
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  4. Huajin Wang
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  5. Chuan Hu
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Corresponding author

Correspondence to Zhihong Shen .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhao, Z., Shen, Z., Mao, A., Wang, H., Hu, C. (2023). PandaDB: An AI-Native Graph Database for Unified Managing Structured and Unstructured Data. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_53

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  • DOI: https://doi.org/10.1007/978-3-031-30678-5_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30677-8

  • Online ISBN: 978-3-031-30678-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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