Detalle de: INforE: Interactive Cross-platform Analytics for Everyone
| Title | INforE: Interactive Cross-platform Analytics for Everyone |
| Abstract |
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. |
| Apa Reference | Giatrakos, N., Arnu, D., Bitsakis, T., Deligiannakis, A., Garofalakis, M., Klinkenberg, R., Konidaris, A., Kontaxakis, A., Kotidis, Y., Samoladas, V., Simitsis, A., Stamatakis, G., Temme, F., Torok, M., Yaqub, E., Montagud, A., Ponce de León, M., Arndt, H., & Burkard, S. (2020). INforE. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 3389–3392. 10.1145/3340531.3417435 |
| Doi | 10.1145/3340531.3417435 |
| Publication Date | 19 Oct 2020 |
| Publication Type | Conference paper |
| Researcher | Arnau Montagud Aquino |
| Ontology | Artificial Intelligence, Machine Learning in Biology |
| Research Groups | Systems Biology Modeling |
| Tools |