A Domain-Specific Language for Do-It-Yourself Analytical Mashups

  • Julian Eberius
  • Maik Thiele
  • Wolfgang Lehner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7059)

Abstract

The increasing amount and variety of data available in the web leads to new possibilities in end-user focused data analysis. While the classic data base technologies for data integration and analysis (ETL and BI) are too complex for the needs of end users, newer technologies like web mashups are not optimal for data analysis. To make productive use of the data available on the web, end users need easy ways to find, join and visualize it.

We propose a domain specific language (DSL) for querying a repository of heterogeneous web data. In contrast to query languages such as SQL, this DSL describes the visualization of the queried data in addition to the selection, filtering and aggregation of the data. The resulting data mashup can be made interactive by leaving parts of the query variable. We also describe an abstraction layer above this DSL that uses a recommendation-driven natural language interface to reduce the difficulty of creating queries in this DSL.

Keywords

data analytics data mashups natural language queries 

References

  1. 1.
    Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W., Goldberg-Kidon, J.: Google fusion tables: web-centered data management and collaboration. In: SIGMOD 2010 (2010)Google Scholar
  2. 2.
    Grammel, L., Storey, M.-A.: A Survey of Mashup Development Environments. In: Chignell, M., Cordy, J., Ng, J., Yesha, Y. (eds.) The Smart Internet. LNCS, vol. 6400, pp. 137–151. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Greenshpan, O., Milo, T., Polyzotis, N.: Autocompletion for mashups. In: VLDB 2009 (2009)Google Scholar
  4. 4.
    Kaufmann, E., Bernstein, A.: Evaluating the usability of natural language query languages and interfaces to semantic web knowledge bases. In: Web Semantics: Science, Services and Agents on the World Wide Web (2010)Google Scholar
  5. 5.
    Picozzi, M., Rodolfi, M., Cappiello, C., Matera, M.: Quality-Based Recommendations for Mashup Composition. In: Daniel, F., Facca, F.M. (eds.) ICWE 2010. LNCS, vol. 6385, pp. 360–371. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julian Eberius
    • 1
  • Maik Thiele
    • 1
  • Wolfgang Lehner
    • 1
  1. 1.Faculty of Computer Science, Database Technology GroupTechnische Universität DresdenDresdenGermany

Personalised recommendations