Flexible Relational Data Model – A Common Ground for Schema-Flexible Database Systems

  • Hannes Voigt
  • Wolfgang Lehner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8716)

Abstract

An increasing number of application fields represent dynamic and open discourses characterized by high mutability, variety, and pluralism in data. Data in dynamic and open discourses typically exhibits an irregular schema. Such data cannot be directly represented in the traditional relational data model. Mapping strategies allow representation but increase development and maintenance costs. Likewise, NoSQL systems offer the required schema flexibility but introduce new costs by not being directly compatible with relational systems that still dominate enterprise information systems. With the Flexible Relational Data Model (FRDM) we propose a third way. It allows the direct representation of data with irregular schemas. It combines tuple-oriented data representation with relation-oriented data processing. So that, FRDM is still relational, in contrast to other flexible data models currently in vogue. It can directly represent relational data and builds on the powerful, well-known, and proven set of relational operations for data retrieval and manipulation. In addition to FRDM, we present the flexible constraint framework FRDM-C. It explicitly allows restricting the flexibility of FRDM when and where needed. All this makes FRDM backward compatible to traditional relational applications and simplifies the interoperability with existing pure relational databases.

Keywords

data model flexibility relational irregular data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abadi, D.J., Marcus, A., Madden, S., Hollenbach, K.J.: Scalable Semantic Web Data Management Using Vertical Partitioning. In: VLDB 2007 (2007)Google Scholar
  2. 2.
    Acharya, S., Carlin, P., Galindo-Legaria, C.A., Kozielczyk, K., Terlecki, P., Zabback, P.: Relational support for flexible schema scenarios. The Proceedings of the VLDB Endowment 1(2) (2008)Google Scholar
  3. 3.
    Agrawal, R., Somani, A., Xu, Y.: Storage and Querying of E-Commerce Data. In: VLDB 2001 (2001)Google Scholar
  4. 4.
    Aulbach, S., Grust, T., Jacobs, D., Kemper, A., Rittinger, J.: Multi-Tenant Databases for Software as a Service: Schema-Mapping Techniques. In: SIGMOD 2008 (2008)Google Scholar
  5. 5.
    Aulbach, S., Seibold, M., Jacobs, D., Kemper, A.: Extensibility and Data Sharing in evolving multi-tenant databases. In: ICDE 2011 (2011)Google Scholar
  6. 6.
    Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R.C., Mellor, S., Schwaber, K., Sutherland, J., Thomas, D.: Manifesto for Agile Software Development (2001), http://agilemanifesto.org/
  7. 7.
    Beckmann, J.L., Halverson, A., Krishnamurthy, R., Naughton, J.F.: Extending RDBMSs To Support Sparse Datasets Using An Interpreted Attribute Storage Format. In: ICDE 2006 (2006)Google Scholar
  8. 8.
    Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge. In: SIGMOD 2008 (2008)Google Scholar
  9. 9.
    Brodie, M.: OTM”10 Keynote. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 2–3. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.: Bigtable: A Distributed Storage System for Structured Data. In: OSDI 2006 (2006)Google Scholar
  11. 11.
    Chu, E., Beckmann, J.L., Naughton, J.F.: The Case for a Wide-Table Approach to Manage Sparse Relational Data Sets. In: SIGMOD 2007 (2007)Google Scholar
  12. 12.
    Crockford, D.: The application/json Media Type for JavaScript Object Notation (JSON), RFC 4627 (July 2006), http://tools.ietf.org/html/rfc4627
  13. 13.
    Cunningham, C., Graefe, G., Galindo-Legaria, C.A.: PIVOT and UNPIVOT: Optimization and Execution Strategies in an RDBMS. In: VLDB 2004 (2004)Google Scholar
  14. 14.
    Franklin, M.J., Halevy, A.Y., Maier, D.: From Databases to Dataspaces: A New Abstraction for Information Management. SIGMOD Record 34(4) (2005)Google Scholar
  15. 15.
    Friedman, C., Hripcsak, G., Johnson, S.B., Cimino, J.J., Clayton, P.D.: A Generalized Relational Schema for an Integrated Clinical Patient Database. In: SCAMC 1990 (1990)Google Scholar
  16. 16.
    Gleick, J.: Faster: The Acceleration of Just About Everything. Pantheon Books, New York (1999)Google Scholar
  17. 17.
    Jacobs, D.: Enterprise Software as Service. ACM Queue 3(6) (2005)Google Scholar
  18. 18.
    Kiely, G., Fitzgerald, B.: An Investigation of the Use of Methods within Information Systems Development Projects. The Electronic Journal of Information Systems in Developing Countries 22(4) (2005)Google Scholar
  19. 19.
    Kurzweil, R.: The Law of Accelerating Returns (March 2001), http://www.kurzweilai.net/the-law-of-accelerating-returns
  20. 20.
    Nagarajan, S.: Guest Editor’s Introduction: Data Storage Evolution. Computing Now, Special Issue (March 2011)Google Scholar
  21. 21.
    Neo Technology: Neo4j (2013), http://neo4j.org/
  22. 22.
    Papakonstantinou, Y., Garcia-Molina, H., Widom, J.: Object Exchange Across Heterogeneous Information Sources. In: ICDE 1995 (1995)Google Scholar
  23. 23.
    Parsons, J., Wand, Y.: Emancipating Instances from the Tyranny of Classes in Information Modeling. ACM Transactions on Database Systems 25(2) (2000)Google Scholar
  24. 24.
    PostgreSQL Global Development Group: PostgreSQL 9.2.4 Documentation, chap. 56.6: Database Page Layout (2013)Google Scholar
  25. 25.
    Rodriguez, M.A., Neubauer, P.: Constructions from Dots and Lines. Bulletin of the American Society for Information Science and Technology 36(6) (August 2010)Google Scholar
  26. 26.
    Sarma, A.D., Dong, X., Halevy, A.Y.: Bootstrapping Pay-As-You-Go Data Integration Systems. In: SIGMOD 2008 (2008)Google Scholar
  27. 27.
    Steimann, F.: On the representation of roles in object-oriented and conceptual modelling. Data & Knowledge Engineering 35(1) (2000)Google Scholar
  28. 28.
    Vassiliou, Y.: Null Values in Data Base Management: A Denotational Semantics Approach. In: SIGMOD 1979 (1979)Google Scholar
  29. 29.
    W3C: RDF Vocabulary Description Language 1.0: RDF Schema (February 2004), http://www.w3.org/TR/2004/REC-rdf-schema-20040210/
  30. 30.
    W3C: Resource Description Framework (RDF): Concepts and Abstract Syntax (February 2004), http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/
  31. 31.
    W3C: Extensible Markup Language (XML) 1.0 (Fifth Edition). (November 2008), http://www.w3.org/TR/2008/REC-xml-20081126/
  32. 32.
    W3C: XML Schema Definition Language (XSD) 1.1 Part 1: Structures. (July 2011), http://www.w3.org/TR/2011/CR-xmlschema11-1-20110721/

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hannes Voigt
    • 1
  • Wolfgang Lehner
    • 1
  1. 1.Database Technology GroupTechnische Universität DresdenDresdenGermany

Personalised recommendations