Hierarchical clustering for OLAP: the CUBE File approach

  • Authors:

  • Nikos Karayannidis;Timos Sellis

  • Affiliations:

  • Institute of Communication and Computer Systems and School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece 15773;Institute of Communication and Computer Systems and School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece 15773

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2008

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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.