NDLI: Hierarchical clustering for OLAP: the CUBE File approach

Content Provider	Springer Nature Link
Author	Karayannidis, Nikos Sellis, Timos
Copyright Year	2006
Abstract	This paper deals with the problem of physical clustering of multidimensional data that are organized in hierarchies on disk in a hierarchy-preserving manner. This is called hierarchical clustering. A typical case, where hierarchical clustering is necessary for reducing I/Os during query evaluation, is the most detailed data of an OLAP cube. The presence of hierarchies in the multidimensional space results in an enormous search space for this problem. We propose a representation of the data space that results in a chunk-tree representation of the cube. The model is adaptive to the cube’s extensive sparseness and provides efficient access to subsets of data based on hierarchy value combinations. Based on this representation of the search space we formulate the problem as a chunk-to-bucket allocation problem, which is a packing problem as opposed to the linear ordering approach followed in the literature.We propose a metric to evaluate the quality of hierarchical clustering achieved (i.e., evaluate the solutions to the problem) and formulate the problem as an optimization problem. We prove its NP-Hardness and provide an effective solution based on a linear time greedy algorithm. The solution of this problem leads to the construction of the CUBE File data structure. We analyze in depth all steps of the construction and provide solutions for interesting sub-problems arising, such as the formation of bucket-regions, the storage of large data chunks and the caching of the upper nodes (root directory) in main memory.Finally, we provide an extensive experimental evaluation of the CUBE File’s adaptability to the data space sparseness as well as to an increasing number of data points. The main result is that the CUBE File is highly adaptive to even the most sparse data spaces and for realistic cases of data point cardinalities provides hierarchical clustering of high quality and significant space savings.
Starting Page	621
Ending Page	655
Page Count	35
File Format	PDF
ISSN	10668888
Journal	The VLDB Journal
Volume Number	17
Issue Number	4
e-ISSN	0949877X
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-09-07
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hierarchical clustering OLAP CUBE File Data cube Physical data clustering Database Management
Content Type	Text
Resource Type	Article
Subject	Information Systems Hardware and Architecture

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