Big data preprocessing: methods and prospects. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: García, Salvador; Ramírez-Gallego, Sergio; Luengo, Julián; Benítez, José; Herrera, Francisco
• Is Part Of: Big data analytics. Volume 1:Issue 1(2016)
• Page Start: 1
• Page End: 22
• Publication Date: 2016-12
• Subjects: Big data -- Data mining -- Data preprocessing -- Hadoop -- Spark -- Imperfect data -- Data transformation -- Feature selection -- Instance reduction; Big data -- Periodicals; Biology -- Data processing -- Periodicals; 570.28557
• DOI: 10.1186/s41044-016-0014-0
• Languages: English
• ISSNs: 2058-6345

Abstract

Abstract The massive growth in the scale of data has been observed in recent years being a key factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety of data that require a new high-performance processing. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The presence of data preprocessing methods for data mining in big data is reviewed in this paper. The definition, characteristics, and categorization of data preprocessing approaches in big data are introduced. The connection between big data and data preprocessing throughout all families of methods and big data technologies are also examined, including a review of the state-of-the-art. In addition, research challenges are discussed, with focus on developments on different big data framework, such as Hadoop, Spark and Flink and the encouragement in devoting substantial research efforts in some families of data preprocessing methods and applications on new big data learning paradigms.