Distributed and parallel construction method for equi-width histogram in cloud database

Exploring and cleaning big data with random sample data blocks

“…It is a common practice to iteratively generate small random samples of a big data set to explore the statistical properties of the entire data and define cleaning rules [10][11][12][13][14][15][16][17][18][19]. This iterative process becomes impractical or impossible on small computing clusters due to the communication, I/O and memory costs of cluster computing frameworks that implement a shared-nothing architecture [20][21][22]. While these distributed frameworks have not adapted well to the requirements of data exploration tasks, existing sequential techniques don't scale easily to big data [23].…”