{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dpasf-a-flink-library-for-streaming-data","title":"DPASF: A Flink Library for Streaming Data preprocessing","arxiv_id":"1810.06021","date":"2018-10-14","proceeding":null,"authors":["Alejandro Alcalde-Barros","Diego García-Gil","Salvador García","Francisco Herrera"],"abstract":"Data preprocessing techniques are devoted to correct or alleviate errors in\ndata. Discretization and feature selection are two of the most extended data\npreprocessing techniques. Although we can find many proposals for static Big\nData preprocessing, there is little research devoted to the continuous Big Data\nproblem. Apache Flink is a recent and novel Big Data framework, following the\nMapReduce paradigm, focused on distributed stream and batch data processing. In\nthis paper we propose a data stream library for Big Data preprocessing, named\nDPASF, under Apache Flink. We have implemented six of the most popular data\npreprocessing algorithms, three for discretization and the rest for feature\nselection. The algorithms have been tested using two Big Data datasets.\nExperimental results show that preprocessing can not only reduce the size of\nthe data, but to maintain or even improve the original accuracy in a short\ntime. DPASF contains useful algorithms when dealing with Big Data data streams.\nThe preprocessing algorithms included in the library are able to tackle Big\nDatasets efficiently and to correct imperfections in the data.","url_abs":"http://arxiv.org/abs/1810.06021v1","url_pdf":"http://arxiv.org/pdf/1810.06021v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dpasf-a-flink-library-for-streaming-data","repo_url":"https://github.com/elbaulp/dpasf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}