VParC: A Compression Scheme for Numeric Data in Column-Oriented Databases

Compression is one of the most important techniques in data management, which is usually used to improve the query efficiency in database. However, there are some restrictions on existing compression algorithms that have been applied to numeric data in column-oriented databases. First, a compression algorithm is suitable only for columns with certain data distributions not for all kinds of data columns; second, a data column with irregular distribution is hard to be compressed; third, the data column compressed by using heavyweight methods cannot be operated before decompression which leads to inefficient query. Based on the fact that it is more possible for a column to have sub-regularity than have global-regularity, we developed a compression scheme called Vertically Partitioning Compression (VParC). This method is suitable for columns with different data distributions, even for irregular columns in some cases. The more important thing is that data compressed by VParC can be operated directly without decompression in advance. Details of the compression and query evaluation approaches are presented in this paper and the results of our experiments demonstrate the promising features of VParC.