| Bibtype | Article |
|---|---|
| Bibkey | Morik/etal/2017a |
| Author | Morik, Katharina and Bockermann, Christian and Buschjäger, Sebastian |
| Ls8autor |
Bockermann, Christian
Buschjäger, Sebastian Morik, Katharina |
| Title | Big Data Science |
| Booktitle | German journal on Artificial Intelligence (KI 2017) |
| Volume | 32 |
| Number | 1 |
| Pages | 27--36 |
| Abstract | In ever more disciplines, science is driven by data, which leads to data analytics becoming a primary skill for researchers. This includes the complete process from data acquisition at sensors, over pre-processing and feature extraction to the use and application of machine learning. Sensors here often produce a plethora of data that needs to be dealt with in near-realtime, which requires a combined effort of implementations at the hardware level to high-level design of data flows. In this paper we outline two use-cases of this wide span of data analysis for science in a real-world example in astroparticle physics. We outline a high-level design approach which is capable of defining the complete data flow from sensor hardware to final analysis. |
| Month | 12 |
| Year | 2017 |
| Projekt | SFB876-A1,SFB876-C3 |
| Url | https://doi.org/10.1007/s13218-017-0522-8 |
|---|