Modal Schema Graphs for Graph Databases

  • Stephan MennickeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11788)


Although graph databases are conceived schema-less, additional knowledge about the data’s structure and/or semantics is beneficial in many graph database management tasks, from efficient storage, over query optimization, up to data integration. Today’s commonly used graph data models do not represent primal suspects regarding their lack of schema prior to data population. More than 20 years ago, also semistructured data has been introduced without an a-priori conceptual modeling phase. Neat models, called schema graphs, have been proposed and proven useful, yet heavily relying on the employed data model, which had been rooted labeled graphs. We generalize schema graphs in two respects: (1) Our notions are based on labeled graphs because the root node assumption is invalid in the spirit of modern graph data models. (2) We propose and study modal schema graphs to increase the expressive power of the original model. Modal schema graphs allow for (conditional) structural requirements without an otherwise necessary logical device. Furthermore, we elaborate on the consequences of our expressiveness enhancement with respect to applications and algorithmic complexity.


Graph databases Schema graphs Modal specifications 


  1. 1.
    Abiteboul, S.: Querying semi-structured data. In: Afrati, F., Kolaitis, P. (eds.) ICDT 1997. LNCS, vol. 1186, pp. 1–18. Springer, Heidelberg (1997). Scholar
  2. 2.
    Abiteboul, S., Buneman, P., Suciu, D.: Data on the Web: From Relations to Semistructured Data and XML. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar
  3. 3.
    Akhtar, W., Cortés-Calabuig, Á., Paredaens, J.: Constraints in RDF. In: Schewe, K.-D., Thalheim, B. (eds.) SDKB 2010. LNCS, vol. 6834, pp. 23–39. Springer, Heidelberg (2011). Scholar
  4. 4.
    Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., Vrgoč, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. 50(5), 68:1–68:40 (2017). Scholar
  5. 5.
    Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 1:1–1:3 (2008). Scholar
  6. 6.
    Beeri, C., Milo, T.: Schemas for integration and translation of structured and semi-structured data. In: Beeri, C., Buneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 296–313. Springer, Heidelberg (1999). Scholar
  7. 7.
    Buneman, P.: Semistructured data. In: Proceedings of the Sixteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 1997, pp. 117–121. ACM, New York (1997).
  8. 8.
    Buneman, P., Davidson, S., Fernandez, M., Suciu, D.: Adding structure to unstructured data. In: Afrati, F., Kolaitis, P. (eds.) ICDT 1997. LNCS, vol. 1186, pp. 336–350. Springer, Heidelberg (1997). Scholar
  9. 9.
    Calvanese, D., Giacomo, G.D., Lenzerini, M.: Extending semi-structured data. In: SEBD (1998)Google Scholar
  10. 10.
    Fan, W., Fan, Z., Tian, C., Dong, X.L.: Keys for graphs. Proc. VLDB Endow. 8(12), 1590–1601 (2015). Scholar
  11. 11.
    Fan, W., Hu, C., Tian, C.: Incremental graph computations: doable and undoable. In: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD 2017, pp. 155–169. ACM, New York (2017).
  12. 12.
    Fan, W., Lu, P.: Dependencies for graphs. In: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2017, Chicago, IL, USA, 14–19 May 2017, pp. 403–416 (2017).
  13. 13.
    Fernández, M.F., Suciu, D.: Optimizing regular path expressions using graph schemas. In: ICDE (1997).
  14. 14.
    Goldman, R., Widom, J.: Dataguides: enabling query formulation and optimization in semistructured databases. In: Proceedings of the 23rd International Conference on Very Large Data Bases, VLDB 1997, pp. 436–445. Morgan Kaufmann Publishers Inc., San Francisco (1997)Google Scholar
  15. 15.
    Gutiérrez, C., Hidders, J., Wood, P.T.: Graph data models. In: Sakr, S., Zomaya, A.Y. (eds.) Encyclopedia of Big Data Technologies, pp. 830–835. Springer, Cham (2019). Scholar
  16. 16.
    Henzinger, M., Henzinger, T., Kopke, P.: Computing simulations on finite and infinite graphs. In: FOCS 1995, pp. 453–462. IEEE Computer Society (1995).
  17. 17.
    Knublauch, H., Kontokostas, D.: Shapes constraint language (SHACL). W3C Recommendation (2017).
  18. 18.
    Larsen, K.G., Thomsen, B.: A modal process logic. In: Proceedings of the Third Annual Symposium on Logic in Computer Science, pp. 203–210 (1988).
  19. 19.
    Larsen, K.G., Nyman, U., Wąsowski, A.: On modal refinement and consistency. In: Caires, L., Vasconcelos, V.T. (eds.) CONCUR 2007. LNCS, vol. 4703, pp. 105–119. Springer, Heidelberg (2007). Scholar
  20. 20.
    Larsen, K.G.: Modal specifications. In: Sifakis, J. (ed.) CAV 1989. LNCS, vol. 407, pp. 232–246. Springer, Heidelberg (1990). Scholar
  21. 21.
    Ma, S., Cao, Y., Fan, W., Huai, J., Wo, T.: Strong simulation: capturing topology in graph pattern matching. ACM Trans. Database Syst. 39(1), 4:1–4:46 (2014). Scholar
  22. 22.
    Mennicke, S., Kalo, J.-C., Balke, W.-T.: Querying graph databases: what do graph patterns mean? In: Mayr, H.C., Guizzardi, G., Ma, H., Pastor, O. (eds.) ER 2017. LNCS, vol. 10650, pp. 134–148. Springer, Cham (2017). Scholar
  23. 23.
    Mennicke, S., Kalo, J.C., Balke, W.T.: Using queries as schema-templates for graph databases. Datenbank-Spektrum 18(2), 89–98 (2018). Scholar
  24. 24.
    Mennicke, S., Kalo, J.C., Nagel, D., Kroll, H., Balke, W.T.: Fast dual simulation processing of graph database queries. In: 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 8–12 April 2019 (2019)Google Scholar
  25. 25.
    Schreiber, G., Raimond, Y.: RDF 1.1 primer. Technical report, W3C (2014)Google Scholar
  26. 26.
    Tajima, K.: Schemaless semistructured data revisited. In: Tannen, V., Wong, L., Libkin, L., Fan, W., Tan, W.-C., Fourman, M. (eds.) In Search of Elegance in the Theory and Practice of Computation. LNCS, vol. 8000, pp. 466–482. Springer, Heidelberg (2013). Scholar
  27. 27.
    Tran, T., Ladwig, G., Rudolph, S.: Managing structured and semistructured RDF data using structure indexes. IEEE Trans. Knowl. Data Eng. 25(9), 2076–2089 (2013). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technische Universität Braunschweig, Institut für InformationssystemeBraunschweigGermany

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