Searched for: author:"Albers, T."
(1 - 4 of 4)
document
Albers, T. (author), Martin Soroa, I. (author), Vonder, M.R. (author)
article 2021
document
Albers, T. (author), Lazovik, E. (author), Yousefi, M.H.N. (author), Lazovik, A. (author)
Distributed data processing systems have become the standard means for big data analytics. These systems are based on processing pipelines where operations on data are performed in a chain of consecutive steps. Normally, the operations performed by these pipelines are set at design time, and any changes to their functionality require the...
article 2021
document
Snijders, R. (author), de Jong, A.P.J. (author), Albers, T. (author), van Waaij, B.D. (author), Vonder, M.R. (author)
The vast amount of livestock (sensor) data collected everyday, offers a huge potential to improve model prediction and therefore decision support for farmers. However, the prediction performance of these models depends highly on the availability and quality of the data. Careful dataset preparation is therefore important. Nowadays, selecting and...
conference paper 2019
document
Lazovik, E. (author), Medema, M. (author), Albers, T. (author), Langius, E.A.F. (author), Lazovik, A. (author)
Distributed data processing systems are the standard means for large-scale data analysis in the Big Data field. These systems are based on processing pipelines where the processing is done via a composition of multiple elements or steps. In current distributed data processing systems, the code and parameters that create the pipeline are set at...
conference paper 2017
Searched for: author:"Albers, T."
(1 - 4 of 4)