4064296
doi
10.1145/3340531.3417435
oai:zenodo.org:4064296
user-eu
user-infore-project
David Arnu
RapidMiner GmbH
Theodoros Bitsakis
Athena Research Center, Technical University of Crete
Antonios Deligiannakis
Athena Research Center, Technical University of Crete
Minos Garofalakis
Athena Research Center, Technical University of Crete
Ralf Klinkenberg
RapidMiner GmbH
Aris Konidaris
Athena Research Center, Technical University of Crete
Antonis Kontaxakis
Athena Research Center, Technical University of Crete
Yannis Kotidis
Athena Research Center, Athens University of Economics and Business
Vasilis Samoladas
Athena Research Center, Technical University of Crete
Alkis Simitsis
Athena Research Center, Technical University of Crete
George Stamatakis
Athena Research Center, Technical University of Crete
Fabian Temme
RapidMiner GmbH
Mate Torok
RapidMiner GmbH
Edwin Yaqub
RapidMiner GmbH
Arnau Montagud
Barcelona Supercomputing Center
Miguel Ponce de León
Barcelona Supercomputing Center
Holger Arndt
Spring Techno GmbH & Co. KG
Stefan Burkard
Spring Techno GmbH & Co. KG
INforE: Interactive Cross-platform Analytics for Everyone
Nikos Giatrakos
Athena Research Center, Technical University of Crete
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cross-platform analytics
interactive Big Data analytics
data streams
<p>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.</p>
Demo paper
Zenodo
2020-10-19
info:eu-repo/semantics/conferencePaper
4064295
user-eu
user-infore-project
award_title=Interactive Extreme-Scale Analytics and Forecasting; award_number=825070; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/825070; funder_id=00k4n6c32; funder_name=European Commission;
1602228876.84582
912598
md5:3fe96dbb4866a629e289b05f40f8ddce
https://zenodo.org/records/4064296/files/cikm2020b.pdf
public