A graph-based meta-model for heterogeneous data management

  • Ernesto Damiani
  • Barbara OliboniEmail author
  • Elisa Quintarelli
  • Letizia Tanca
Regular Paper


The wave of interest in data-centric applications has spawned a high variety of data models, making it extremely difficult to evaluate, integrate or access them in a uniform way. Moreover, many recent models are too specific to allow immediate comparison with the others and do not easily support incremental model design. In this paper, we introduce GSMM, a meta-model based on the use of a generic graph that can be instantiated to a concrete data model by simply providing values for a restricted set of parameters and some high-level constraints, themselves represented as graphs. In GSMM, the concept of data schema is replaced by that of constraint, which allows the designer to impose structural restrictions on data in a very flexible way. GSMM includes GSL, a graph-based language for expressing queries and constraints that besides being applicable to data represented in GSMM, in principle, can be specialised and used for existing models where no language was defined. We show some sample applications of GSMM for deriving and comparing classical data models like the relational model, plain XML data, XML Schema, and time-varying semistructured data. We also show how GSMM can represent more recent modelling proposals: the triple stores, the BigTable model and Neo4j, a graph-based model for NoSQL data. A prototype showing the potential of the approach is also described.


Meta-modelling Heterogeneous data Graph-based data model Graph-based constraints 



  1. 1.
    Abiteboul S (1997) Querying semi-structured data. In: Proceedings of the international conference on database theory, vol 1186. Lecture notes in computer science, pp 262–275Google Scholar
  2. 2.
    Angles R (2012) A comparison of current graph database models. In: Proceedings of the 2012 IEEE 28th international conference on data engineering workshops, ICDEW ’12. IEEE Computer Society, Washington, DC, pp 171–177Google Scholar
  3. 3.
    Atzeni P, Cappellari P, Torlone R, Bernstein PA, Gianforme G (2008) Model-independent schema translation. VLDB J 17(6):1347–1370CrossRefGoogle Scholar
  4. 4.
    Atzeni P, Torlone R (2001) A unified framework for data translation over the web. In: Proceedings of the 2nd international conference on web information system engineering. IEEE Computer Society, pp 350–358Google Scholar
  5. 5.
    Bekiropoulos K, Keramopoulos E, Beza O, Mouratidis P (2010) A list of features that a graphical xml query language should support. Comput Syst Sci Eng 25(5):13–21Google Scholar
  6. 6.
    Benda S, Klímek J, Nečaský M (2013) Using schematron as schema language in conceptual modeling for xml. In: Proceedings of the ninth Asia-Pacific conference on conceptual modelling, vol 143, APCCM ’13. Australian Computer Society, Inc., Darlinghurst, pp 31–40Google Scholar
  7. 7.
    Bernstein PA, Halevy AY, Pottinger RA (2000) A vision for management of complex models. SIGMOD Rec 29(4):55–63CrossRefGoogle Scholar
  8. 8.
    Bernstein PA, Pottinger R (2003) Merging models based on given correspondences. Technical report UW-CSE-03-02-03. University of WashingtonGoogle Scholar
  9. 9.
    Bowers S, Delcambre L (2000) Representing and transforming model-based information. In: Proceedings of International workshop on the semantic web at the 4th European conference on research and advanced technology for digital libraries (SemWeb)Google Scholar
  10. 10.
    Bunemann P, Fan W, Siméon J, Weinstein S (2001) Constraints for semistructured data and XML. SIGMOD Rec 30:47–54CrossRefGoogle Scholar
  11. 11.
    Bunemann P, Fan W, Weinstein S (1998) Path constraints on semistructured and structured data. In: Proceedings of 17th symposium on principles of database system. ACM Press, pp 129–138Google Scholar
  12. 12.
    Cattell R (2011) Scalable SQL and NoSQL data stores. SIGMOD Rec 39(4):12–27CrossRefGoogle Scholar
  13. 13.
    Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):4:1–4:26CrossRefGoogle Scholar
  14. 14.
    Chawathe SS, Abiteboul S, Widom J (1998) Representing and querying changes in semistructured data. In: Proceedings of the fourteenth international conference on data engineering. IEEE Computer Society, pp 4–13Google Scholar
  15. 15.
    Chawathe SS, Abiteboul S, Widom J (1999) Managing historical semistructured data. Theory Pract Object Syst 5(3):143–162CrossRefGoogle Scholar
  16. 16.
    Chen L, Oughtred R, Berman HM, Westbrook J (2004) Targetdb: a target registration database for structural genomics projects. Bioinform Appl Notes 20(16):2860–2862CrossRefGoogle Scholar
  17. 17.
    Combi C, Oliboni B, Quintarelli E (2012) Modeling temporal dimensions of semistructured data. J Intell Inf Syst 38(3):601–644CrossRefGoogle Scholar
  18. 18.
    Cortesi A, Dovier A, Quintarelli E, Tanca L (2002) Operational and abstract semantics of a query language for semi-structured information. Theor Comput Sci 275(1–2):521–560CrossRefGoogle Scholar
  19. 19.
    Damiani E, Oliboni B, Quintarelli E, Tanca L (2003) Modeling semistructured data by using graph-based constraints. In: OTM workshops proceedings. Lecture notes in computer science. Springer, Berlin, pp 20–21Google Scholar
  20. 20.
    Damiani E, Tanca L (1997) Semantic approches to structuring and querying web sites. In: Proceedings of 7th IFIP working conference on database semantics (DS-97)Google Scholar
  21. 21.
    Fan W, Lu P (2017) Dependencies for graphs. In: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems, PODS ’17. ACM, pp 403–416Google Scholar
  22. 22.
    Indrawan-Santiago M (2012) Database research: Are we at a crossroad? reflection on NoSQL. In: Proceedings of the 2012 15th international conference on network-based information systems, NBIS ’12. IEEE Computer Society, Washington, DC, pp 45–51Google Scholar
  23. 23.
    Kaur K, Rani R (2013) Modeling and querying data in NoSQL databases. In: Proceedings of the IEEE international conference on Big Data, pp 1 – 7Google Scholar
  24. 24.
    Lee KK-Y, Tang W-C, Choi K-S (2013) Alternatives to relational database: comparison of NoSQL and XML approaches for clinical data storage. Comput Methods Progr Biomed 110(1):99–109CrossRefGoogle Scholar
  25. 25.
    Levy AY, Rajaraman A, Ordille JJ (1996) Querying heterogeneous information sources using source descriptions. In: Proceedings of the twenty-second international conference on very large databases. VLDB Endowment, Saratoga, Calif., Bombay, India, pp 251–262Google Scholar
  26. 26.
    Makoto M, Lee D, Mani M, Kawaguchi K (2005) Taxonomy of XML schema languages using formal language theory. ACM Trans Internet Technol 5(4):660–704CrossRefGoogle Scholar
  27. 27.
    McBrien P, Poulovassilis A (1999) A uniform approach to inter-model transformations. In: Conference on advanced information systems engineering, pp 333–348Google Scholar
  28. 28.
    Oliboni B, Quintarelli E, Tanca L (2001) Temporal aspects of semistructured data. In: Proceedings of the eighth international symposium on temporal representation and reasoning (TIME-01). IEEE Computer Society, pp 119–127Google Scholar
  29. 29.
    Papakonstantinou Y, Garcia-Molina H, Widom J (1995) Object exchange across heterogeneous information sources. In: Proceedings of the eleventh international conference on data engineering. IEEE Computer Society, pp 251–260Google Scholar
  30. 30.
    Paredaens J, Peelman P, Tanca L (1995) G-Log: a declarative graphical query language. IEEE Trans Knowl Data Eng 7(3):436–453CrossRefGoogle Scholar
  31. 31.
    Vicknair C, Macias M, Zhao Z, Nan X, Chen Y, Wilkins D (2010) A comparison of a graph database and a relational database: a data provenance perspective. In: Proceedings of the 48th annual southeast regional conference, ACM SE ’10. ACM, New York, NY, pp 42:1–42:6Google Scholar
  32. 32.
    Virgilio RD, Maccioni A, Torlone R (2014) Graph-driven exploration of relational databases for efficient keyword search. In: Candan KS, Amer-Yahia S, Schweikardt N, Christophides V, Leroy V (eds) Proceedings of the workshops of the EDBT/ICDT 2014 joint conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014, Vol. 1133 of CEUR workshop proceedings,, pp 208–215Google Scholar
  33. 33.
    W3C (1998) World wide web consortium. Extensible Markup Language (XML) 1.0.
  34. 34.
    W3C (2001) World wide web consortium. XML schema.
  35. 35.
    Zang T, Calinescu R, Kwiatkowska MZ (2011) Metamodel-driven SOA for collaborative e-science application. Comput Syst Sci Eng 26(3):215–226Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità degli Studi di MilanoMilanItaly
  2. 2.Etisalat British, Telecom Innovation CenterKhalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates
  3. 3.Dipartimento di InformaticaUniversità degli Studi di VeronaVeronaItaly
  4. 4.Dipartimento di Elettronica, Informazione e BioignegneriaPolitecnico di MilanoMilanItaly

Personalised recommendations