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INforE: interactive cross-platform analytics for everyone

Giatrakos Nikolaos, Arnu David, Bitsakis Theodoros, Deligiannakis Antonios, Garofalakis Minos, Klinkenberg Ralf, Konidaris Vissarion-Bertcholnt, Kontaxakis Antonios, Kotidis, Yannis, Samoladas Vasilis, Simitsis Alkis, Stamatakis Georgios, Temme Fabian, Torok Mate, Yaqub Edwin, Montagud Arnau, Ponce-de-Leon Miguel, Arndt Holger, Burkard Stefan

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Year 2020
Type of Item Conference Demonstration
Bibliographic Citation N. Giatrakos, D. Arnu, T. Bitsakis, A. Deligiannakis, M. Garofalakis, R. Klinkenberg, A. Konidaris, A. Kontaxakis, Y. Kotidis, V. Samoladas, A. Simitsis, G. Stamatakis, F. Temme, M. Torok, E. Yaqub, A. Montagud, M. Ponce De León, H. Arndt, and S. Burkard, “INforE: interactive cross-platform analytics for everyone,” in Proc. 29th ACM Int. Conf. Inf. Knowl. Manage. (CIKM 2020), virtual event, 2020, pp. 3389–3392, doi: 10.1145/3340531.3417435.
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We present INforE, a prototype supporting non-expert programmers in performing optimized, cross-platform, streaming analytics at scale. INforE offers: a) a new extension to the RapidMiner Studio for graphical design of Big streaming Data workflows, (b) a novel optimizer to instruct the execution of workflows across Big Data platforms and clusters, (c) a synopses data engine for interactivity at scale via the use of data summaries, (d) a distributed, online data mining and machine learning module. To our knowledge INforE is the first holistic approach in streaming settings. We demonstrate INforE in the fields of life science and financial data analysis.