文章基本信息

标题：Improving Pipelining Tools for Pre-processing Data
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作者：María Novo-Lourés ; Yeray Lage ; Reyes Pavón 等
期刊名称：International Journal of Interactive Multimedia and Artificial Intelligence
印刷版ISSN：1989-1660
出版年度：2022
卷号：7
期号：4
页码：214-224
DOI：10.9781/ijimai.2021.10.004
语种：English
出版社：ImaI-Software
摘要：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 of pipelining 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 programming languages 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.