Real-Time Data Analyses in Secondary Schools Using a Block-Based Programming Language

  • Andreas GrillenbergerEmail author
  • Ralf Romeike
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10696)


Data management is central to many CS innovations: Smart home technologies and the Internet of Things, for example, are based on processing data with high velocity. One of the most interesting topics emphasizing several challenges in this field is real-time data analysis. In secondary CS education, it is only considered marginally. So far, there are no tools suitable for general-purpose real-time data analysis in school. In this paper, we discuss this topic from a secondary CS education perspective. Besides central concepts and differences to traditional data analysis using relational databases, we describe the development of a general-purpose \(\textsf {Snap}{\textit{!}}\) extension that allows accessing and processing data from various sources. Thereby, students are enabled to conduct data analyses using, for example, sensor data or web APIs. With the example of a weather station, we outline how this tool can be used in school for analyzing sensor data generated in the classroom.


Real-time data analysis Data stream systems Data management Sensor data Physical computing Secondary CS education 


  1. 1.
    Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006)CrossRefGoogle Scholar
  2. 2.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proceedings of the 21th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 1–16. ACM, New York (2002)Google Scholar
  3. 3.
    Lee, E.A., Seshia, S.A.: Introduction to Embedded Systems. MIT Press Ltd., Cambridge (2017)zbMATHGoogle Scholar
  4. 4.
    Golab, L., Özsu, M.T.: Processing sliding window multi-joins in continuous queries over data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases (VLDB 2003), vol. 29, pp. 500–511. VLDB Endowment (2003)Google Scholar
  5. 5.
    Grillenberger, A., Romeike, R.: A Comparison of the field data management and its representation in secondary CS curricula. In: Proceedings of WiPSCE 2014. ACM, Berlin (2014)Google Scholar
  6. 6.
    Grillenberger, A., Romeike, R.: Analyzing the Twitter data stream using the snap! Learning environment. In: Brodnik, A., Vahrenhold, J. (eds.) ISSEP 2015. LNCS, vol. 9378, pp. 155–164. Springer, Cham (2015). CrossRefGoogle Scholar
  7. 7.
    Grillenberger, A., Romeike, R.: Teaching data management: key competencies and opportunities. In: Brinda, T., Reynolds, N., Romeike, R. (eds.) Proceedings of KEYCIT 2014. Universitätsverlag Potsdam (2014)Google Scholar
  8. 8.
    Harvey, B., Mönig, J.: Snap! Reference manual (2014). Accessed 09 July 2017
  9. 9.
    Krämer, J.: Continuous Queries over Data Streams - Semantics and Implementation. Philipps-Universität Marburg (2007)Google Scholar
  10. 10.
    Laney, D.: 3D data management: controlling data volume, velocity, and variety. Technical report, META Group, February 2001Google Scholar
  11. 11.
    Mäenpää, H., Varjonen, S., Hellas, A., Tarkoma, S., Männistö, T.: Assessing IoT projects in university education: a framework for problem-based learning. In: Proceedings of the 39th International Conference on Software Engineering, pp. 37–46. IEEE Press, Piscataway (2017)Google Scholar
  12. 12.
    Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt (2013)Google Scholar
  13. 13.
    Nittel, S.: Real-time sensor data streams. SIGSPATIAL Spec. 7(2), 22–28 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computing Education Research GroupFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany

Personalised recommendations