Exploring and cleaning big data with random sample data blocks

Abstract

Data scientists need scalable methods to explore and clean big data before applying advanced data analysis and mining algorithms. In this paper, we propose the RSP-Explore method to enable data scientists to iteratively explore big data on small computing clusters. We address three main tasks: statistical estimation, error detection, and data cleaning. The Random Sample Partition (RSP) distributed data model is used to represent the data as a set of ready-to-use random sample data blocks (called RSP blocks) of the entire data. Block-level samples of RSP blocks are selected to understand the data, identify potential types of value errors, and get samples of clean data. We provide a theoretical analysis on using RSP blocks for statistical estimation and demonstrate empirically the advantages of the RSP-Explore method. The experimental results of three real data sets show that the approximate results from RSP-Explore can rapidly converge toward the true values. Furthermore, cleaning a sample of RSP blocks is sufficient to estimate the statistical properties of the unknown clean data.