Abstract
In the last few years, tremendous changes have occurred in the field data management, especially in the context of big data. Not only approaches for data analysis have changed, but also real–time data analyses gain in importance and support decision–making in various contexts. One of the most exciting approaches for processing and analyzing large amounts of data in nearly real–time are data stream systems.
In this paper, we will demonstrate how such developments in CS can be introduced in CS education by using data stream systems as an example. We will discuss these systems from a CS education point of view and describe an approach for carrying out data stream analysis by using the Twitter stream as data source. Also, we will show how the programming tool Snap ! can be extended for supporting teaching in this context.
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References
Data Mining. Encyclopdia Britannica, http://www.britannica.com/EBchecked/topic/1056150/data-mining
Dwoskin, E.: In a Single Tweet, as Many Pieces of Metadata as There Are Characters. The Washington Journal (2014), http://blogs.wsj.com/digits/2014/06/06/in-a-single-tweet-as-many-pieces-of-metadata-as-there-are-characters
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)
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)
Grillenberger, A., Romeike, R.: Big data – challenges for computer science education. In: Gülbahar, Y., Karataş, E. (eds.) ISSEP 2014. LNCS, vol. 8730, pp. 29–40. Springer, Heidelberg (2014)
Grillenberger, A., Romeike, R.: Teaching data management: key competencies and opportunities. In: Brinda, T., Reynolds, N., Romeike, R. (eds.) KEYCIT 2014 - Key Competencies in Informatics and ICT. Universitätsverlag Potsdam, Commentarii informaticae didacticae (2014)
Harvey, B., Mönig, J.: Snap! Reference Manual (2014), http://snap.berkeley.edu/SnapManual.pdf
Krikorian, R.: New Tweets per second record, and how (2013), https://blog.twitter.com/node/2845
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. The AAAI Press, Menlo Park (2010)
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Grillenberger, A., Romeike, R. (2015). Analyzing the Twitter Data Stream Using the Snap! Learning Environment. In: Brodnik, A., Vahrenhold, J. (eds) Informatics in Schools. Curricula, Competences, and Competitions. ISSEP 2015. Lecture Notes in Computer Science(), vol 9378. Springer, Cham. https://doi.org/10.1007/978-3-319-25396-1_14
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DOI: https://doi.org/10.1007/978-3-319-25396-1_14
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