LCS-Hist: Taming Massive High-Dimensional Data Cube Compression

The problem of efficiently compressing massive high-dimensional data cubes still waits for efficient solutions capable of overcoming well-recognized scalability limitations of state-of-the-art histogram-based techniques, which perform well on small-in-size low-dimensional data cubes, whereas their performance in both representing the input data domain and efficiently supporting approximate query answering against the generated compressed data structure decreases dramatically when data cubes grow in dimension number and size. To overcome this relevant research challenge, in this paper we propose LCS-Hist, an innovative multidimensional histogram devising a complex methodology that combines intelligent data modeling and processing techniques in order to tame the annoying problem of compressing massive high-dimensional data cubes. With respect to similar histogram-based proposals, our technique introduces (i) a surprising consumption of the storage space available to house the compressed representation of the input data cube, and (ii) a superior scalability on high-dimensional data cubes. Finally, several experimental results performed against various classes of data cubes confirm the advantages of LCS-Hist, even in comparison with those given by state-of-the-art similar techniques. 1