Advertisement

Multi-model Database Management Systems - A Look Forward

  • Zhen Hua Liu
  • Jiaheng LuEmail author
  • Dieter Gawlick
  • Heli Helskyaho
  • Gregory Pogossiants
  • Zhe Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11470)

Abstract

The existence of the variety of data models and their associated data processing technologies make data management extremely complex. In this paper, we envision a single Multi-Model DataBase Management Systems (MMDBMS) providing declarative accesses to a variety of data models. We briefly review the history of the evolution of the DBMS technology to derive requirements of MMDBMSs and then we illustrate our ideas of building MMDBMSs satisfying those requirements. Since the relational algebra is not powerful enough to provide a mathematical foundation for MMDBMSs, we promote the category theory as a new theoretical foundation, which is a generalization of the set theory. We also suggest a set of shared data infrastructure services among data models to support “Just-In-Time” multi-model data access autonomously.

Notes

Acknowledgement

Jiaheng Lu is partially supported by the Academy of Finland (No. 310321).

References

  1. 1.
    Spoth, W., et al.: Adaptive schema databases. In: CIDR (2017)Google Scholar
  2. 2.
    Liu, Z.H., Gawlick, D.: Management of flexible schema data in RDBMSs-opportunities and limitations for NoSQL. In: CIDR (2015)Google Scholar
  3. 3.
    Lahiri, T., et al.: Oracle database in-memory: a dual format in-memory database. In: Data Engineering (ICDE) (2015)Google Scholar
  4. 4.
    Gawlick, D., Chan, E.S., Ghoneimy, A., Liu, Z.H.: Mastering situation awareness: the next big challenge? ACM SIGMOD Rec. 44(3), 19–24 (2015)CrossRefGoogle Scholar
  5. 5.
    Spivak, D.I.: Database queries and constraints via lifting problems. Math. Struct. Comput. Sci. 24(6) (2014)Google Scholar
  6. 6.
    Grosvenor, M.P., Clement, A., Hand, S.: Musketeer: all for one, one for all in data processing systems. In: EuroSys, pp. 1–16 (2015)Google Scholar
  7. 7.
    Lim, H., Han, Y., Babu, S.: How to fit when no one size fits. In: CIDR (2013)Google Scholar
  8. 8.
    Elmore, A., et al.: A demonstration of the BigDAWG polystore system. Proc. VLDB Endow. 8(12), 1908–1911 (2015)CrossRefGoogle Scholar
  9. 9.
    Schultz, P., et al.: Algebraic databases. CoRR abs/1602.03501 (2016)Google Scholar
  10. 10.
    Fleming, M., Gunther, R., Rosebrugh, R.: A database of categories. J. Symb. Comput. 35, 127–135 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wisnesky, R., Spivak, D.: A functorial query language. Presented at Boston Haskell (2014). http://categoricaldata.net/fql/haskell.pdf
  12. 12.
    Abiteboul, S., et al.: Research directions for principles of data management (Abridged). SIGMOD Rec. 45(4), 5–17 (2016)CrossRefGoogle Scholar
  13. 13.
    Liu, Z.H., et al.: Towards a physical XML independent XQuery/SQL/XML engine. PVLDB 1(2), 1356–1367 (2008)Google Scholar
  14. 14.
    Michael, B., Charles, W.: Category Theory for Computing Science. Reprints in Theory and Applications of Categories, vol. 22 (2012)Google Scholar
  15. 15.
    Yan, Da, et al.: Big graph analytics platforms. Found. Trends Databases 7(1–2), 1–195 (2017)CrossRefGoogle Scholar
  16. 16.
    Lu, J., Holubová, I.: Multi-model data management: what’s new and what’s next? In: EDBT 2017, pp. 602–605 (2017)Google Scholar
  17. 17.
    World Wide Web Consortium (W3C). https://www.w3.org/
  18. 18.
    Wisnesky, R., Spivak, D.I., Schultz, P., Subrahmanian, E.: Functorial data migration: from theory to practice. CoRR abs/1502.05947 (2015)Google Scholar
  19. 19.
    Riehl, E.: Category Theory in Context. Courier Dover Publications, Mineola (2017)zbMATHGoogle Scholar
  20. 20.
    Toman, D., Weddell, G.E.: Fundamentals of Physical Design and Query Compilation. Synthesis Lectures on Data Management. Morgan & Claypool Publishers, San Rafael (2011)CrossRefGoogle Scholar
  21. 21.
    Atzeni, P., Torlone, R.: A metamodel approach for the management of multiple models and translation of schemes. Inf. Syst. 18(6), 349–362 (1993)CrossRefGoogle Scholar
  22. 22.
    Property Graph Query Language 1.1 Specification. http://pgql-lang.org/spec/1.1/
  23. 23.
    Liu, Z.H., Hammerschmidt, B.C., McMahon, D., Liu, Y., Chang, H.J.: Closing the functional and performance gap between SQL and NoSQL. In: SIGMOD Conference 2016, pp. 227–238 (2016)Google Scholar
  24. 24.
    Girard, J.-Y.: Locus solum: from the rules of logic to logic of rules. Math. Struct. Comput. Sci. 11(3), 301–506 (2001)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Girard, J.-Y.: From foundations to ludics. Bull. Symb. Log. 9(2), 131–168 (2003)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Lecomte, A.: Meaning, Logic and Ludics. Imperial College Press, London (2011)CrossRefGoogle Scholar
  27. 27.
    Liu, Y., et al.: ProbeSim: scalable single-source and top-k SimRank computations on dynamic graphs. PVLDB 11(1), 14–26 (2017)Google Scholar
  28. 28.
    Lu, J.: Towards benchmarking multi-model databases. In: CIDR (2017)Google Scholar
  29. 29.
    Chen, J., et al.: Big data challenge: a data management perspective. Front. Comput. Sci. 7(2), 157–164 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhen Hua Liu
    • 1
  • Jiaheng Lu
    • 2
    Email author
  • Dieter Gawlick
    • 1
  • Heli Helskyaho
    • 2
    • 3
  • Gregory Pogossiants
    • 4
  • Zhe Wu
    • 1
  1. 1.Oracle CorporationRedwood CityUSA
  2. 2.University of HelsinkiHelsinkiFinland
  3. 3.Miracle Finland OyHelsinkiFinland
  4. 4.Soulmates.aiPasadenaUSA

Personalised recommendations