Hierarchical clustering for OLAP: The CUBE File approach

Karayannidis, N; Sellis, T

dc.contributor.author	Karayannidis, N	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T01:28:35Z
dc.date.available	2014-03-01T01:28:35Z
dc.date.issued	2008	en
dc.identifier.issn	1066-8888	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18878
dc.subject	CUBE File	en
dc.subject	Data cube	en
dc.subject	Hierarchical clustering	en
dc.subject	OLAP	en
dc.subject	Physical data clustering	en
dc.subject.classification	Computer Science, Hardware & Architecture	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.other	GRID FILE	en
dc.subject.other	IMPLEMENTATION	en
dc.title	Hierarchical clustering for OLAP: The CUBE File approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00778-006-0022-1	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00778-006-0022-1	en
heal.language	English	en
heal.publicationDate	2008	en
heal.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. © 2006 Springer-Verlag.	en
heal.publisher	SPRINGER	en
heal.journalName	VLDB Journal	en
dc.identifier.doi	10.1007/s00778-006-0022-1	en
dc.identifier.isi	ISI:000256765700002	en
dc.identifier.volume	17	en
dc.identifier.issue	4	en
dc.identifier.spage	621	en
dc.identifier.epage	655	en