Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3340531.3412174acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
tutorial

Multi-Model Data Query Languages and Processing Paradigms

Published: 19 October 2020 Publication History
  • Get Citation Alerts
  • Abstract

    Specifying users' interests with a formal query language is a typically challenging task, which becomes even harder in the context of multi-model data management because we have to deal with data variety. It usually lacks a unified schema to help the users issuing their queries, or has an incomplete schema as data come from disparate sources. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating and querying the multi-model data in a single system. This tutorial aims to offer a comprehensive presentation of a wide range of query languages for MMDBs and to make comparisons of their properties from multiple perspectives. We will discuss the essence of cross-model query processing and provide insights on the research challenges and directions for future work. The tutorial will also offer the participants hands-on experience in applying MMDBs to issue multi-model data queries.

    Supplementary Material

    MP4 File (3340531.3412174.mp4)
    In this teaser video, we make a brief introduction to each section that will be discussed in our tutorial.

    References

    [1]
    ECMA-404 The JSON Data Interchange Standard. https://www.json.org/json-en.html.
    [2]
    Extensible Markup Language (XML). https://www.w3.org/XML/.
    [3]
    R. Angles, M. Arenas, P. Barceló, A. Hogan, J. L. Reutter, and D. Vrgoc. Foundations of modern query languages for graph databases. ACM Comput. Surv., 50(5):68:1--68:40, 2017.
    [4]
    E. F. Codd. A relational model of data for large shared data banks. Commun. ACM, 13(6):377--387, 1970.
    [5]
    E. F. Codd. Extending the database relational model to capture more meaning. ACM Trans. Database Syst., 4(4):397--434, Dec. 1979.
    [6]
    A. Deutsch and Y. Papakonstantinou. Graph data models, query languages and programming paradigms. Proc. VLDB Endow., 11(12):2106--2109, 2018.
    [7]
    J. Lu and I. Holubová. Multi-model data management: What's new and what's next? In Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, March 21-24, 2017, pages 602--605. Open Proceedings.org, 2017.
    [8]
    J. Lu and I. Holubová. Multi-model Databases: A new journey to handle the variety of data. ACM Computing Surveys, 52(3), 2019.
    [9]
    J. Lu, I. Holubová, and B. Cautis. Multi-model databases and tightly integrated polystores: Current practices, comparisons, and open challenges. In CIKM '18, pages 2301--2302, New York, NY, USA, 2018. ACM.
    [10]
    I. Robinson, J. Webber, and E. Eifrem. Graph Databases: New Opportunities for Connected Data. O'Reilly Media, Inc., 2nd edition, 2015.
    [11]
    M. Saeed, M. Villarroel, A. Reisner, G. Clifford, L.-w. Lehman, G. Moody, T. Heldt, T. Kyaw, B. Moody, and R. Mark. Multiparameter Intelligent Monitoring in Intensive Care II (Mimic-II): A Public-Access Intensive Care Unit Database. Critical care medicine, 39:952--60, 05 2011.
    [12]
    M. H. Scholl. Extensions to the Relational Data Model. In Conceptual Modelling, Databases and CASE: An Integrated View of Information Systems Development. Jon. Wiley & Sons, 1992.
    [13]
    M. H. Scholl, H. Paul, and H. Schek. Supporting flat relations by a nested relational kernel. In VLDB'87, September 1-4, 1987, Brighton, England, pages 137--146. Morgan Kaufmann, 1987.
    [14]
    P. T. Wood. Query languages for graph databases. SIGMOD Rec., 41(1):50--60, 2012.
    [15]
    C. Zhang and J. Lu. Holistic evaluation in multi-model databases benchmarking. Distributed and Parallel Databases, pages 1--33, 2019.
    [16]
    C. Zhang, J. Lu, P. Xu, and Y. Chen. UniBench: A Benchmark for Multi-model Database Management Systems. In TPCTC '18, Rio de Janeiro, Brazil, August 27-31, 2018, Revised Selected Papers, volume 11135 of Lecture Notes in Computer Science, pages 7--23. Springer, 2018.

    Cited By

    View all
    • (2024)HyBench: A New Benchmark for HTAP DatabasesProceedings of the VLDB Endowment10.14778/3641204.364120617:5(939-951)Online publication date: 1-Jan-2024
    • (2024)PACE: Poisoning Attacks on Learned Cardinality EstimationProceedings of the ACM on Management of Data10.1145/36392922:1(1-27)Online publication date: 26-Mar-2024
    • (2024)Advanced discovery mechanisms in model repositoriesSoftware: Practice and Experience10.1002/spe.3332Online publication date: 25-Apr-2024
    • Show More Cited By

    Index Terms

    1. Multi-Model Data Query Languages and Processing Paradigms

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
      October 2020
      3619 pages
      ISBN:9781450368599
      DOI:10.1145/3340531
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 19 October 2020

      Check for updates

      Author Tags

      1. data variety
      2. multi-model data management
      3. multi-model database
      4. query language

      Qualifiers

      • Tutorial

      Funding Sources

      • Finnish Academy
      • Huawei Canada

      Conference

      CIKM '20
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

      Upcoming Conference

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)51
      • Downloads (Last 6 weeks)9

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)HyBench: A New Benchmark for HTAP DatabasesProceedings of the VLDB Endowment10.14778/3641204.364120617:5(939-951)Online publication date: 1-Jan-2024
      • (2024)PACE: Poisoning Attacks on Learned Cardinality EstimationProceedings of the ACM on Management of Data10.1145/36392922:1(1-27)Online publication date: 26-Mar-2024
      • (2024)Advanced discovery mechanisms in model repositoriesSoftware: Practice and Experience10.1002/spe.3332Online publication date: 25-Apr-2024
      • (2023)Query Model Framework Design for Conservation History and Endowments Database: A Case Study on the Digitization of the Sumedang Larang Kingdom’s History and Endowments in IndonesiaHeritage10.3390/heritage61203946:12(7508-7530)Online publication date: 29-Nov-2023
      • (2021)Categorical Management of Multi-Model DataProceedings of the 25th International Database Engineering & Applications Symposium10.1145/3472163.3472166(134-140)Online publication date: 14-Jul-2021

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media