RT Journal Article
T1 Improving pipelining tools for pre-processing data
A1 Novo Lourés, María
A1 Lage, Yeray
A1 Pavón Rial, Maria Reyes
A1 Laza Fidalgo, Rosalía
A1 Ruano Ordás, David Alfonso
A1 Méndez Reboredo, José Ramón
K1 1203.17 Informática
AB The last several years have seen the emergence of data mining and its transformation into a powerful tool that adds value to business and research. Data mining makes it possible to explore and find unseen connections between variables and facts observed in different domains, helping us to better understand reality. The programming methods and frameworks used to analyse data have evolved over time. Currently, the use ofpipelining schemes is the most reliable way of analysing data and due to this, several important companies are currently offering this kind of services. Moreover, several frameworks compatible with different programminglanguages are available for the development of computational pipelines and many research studies have addressed the optimization of data processing speed. However, as this study shows, the presence of early error detection techniques and developer support mechanisms is very limited in these frameworks. In this context, this study introduces different improvements, such as the design of different types of constraints for the early detection of errors, the creation of functions to facilitate debugging of concrete tasks included in a pipeline, the invalidation of erroneous instances and/or the introduction of the burst-processing scheme. Adding these functionalities, we developed Big Data Pipelining for Java (BDP4J, https://github.com/sing-group/bdp4j), a fully functional new pipelining framework that shows the potential of these features.
PB International Journal of Interactive Multimedia and Artificial Intelligence
SN 19891660
YR 2022
FD 2022-06
LK http://hdl.handle.net/11093/5488
UL http://hdl.handle.net/11093/5488
LA eng
NO International Journal of Interactive Multimedia and Artificial Intelligence, 7(4): 214 (2022)
NO Agencia Estatal de Investigación | Ref. TIN2017-84658-C2-1-R
DS Investigo
RD 14-sep-2024