INforE: Interactive Cross-platform Analytics for Everyone

Nikos Giatrakos; David Arnu; Theodoros Bitsakis; Antonios Deligiannakis; Minos Garofalakis; Ralf Klinkenberg; Aris Konidaris; Antonis Kontaxakis; Yannis Kotidis; Vasilis Samoladas; Alkis Simitsis; George Stamatakis; Fabian Temme; Mate Torok; Edwin Yaqub; Arnau Montagud; Miguel Ponce de León; Holger Arndt; Stefan Burkard - CIKM 2020 - 2020


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.

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This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 825070.

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