Conference Paper

Relative prefix sums: an efficient approach for querying dynamic OLAP data cubes

Dept. of Comput. Sci., California Univ., Santa Barbara, CA
DOI: 10.1109/ICDE.1999.754948 Conference: Data Engineering, 1999. Proceedings., 15th International Conference on
Source: DBLP

ABSTRACT Range sum queries on data cubes are a powerful tool for analysis.
A range sum query applies an aggregation operation (e.g., SUM) over all
selected cells in a data cube, where the selection is specified by
providing ranges of values for numeric dimensions. Many application
domains require that information provided by analysis tools be current
or “near-current.” Existing techniques for range sum queries
on data cubes, however, can incur update costs on the order of the size
of the data cube. Since the size of a data cube is exponential in the
number of its dimensions, rebuilding the entire data cube can be very
costly. We present an approach that achieves constant time range sum
queries while constraining update costs. Our method reduces the overall
complexity of the range sum problem

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    ABSTRACT: Data cubes support a powerful data analysis method called the range-sum query. The range-sum query is widely used in finding trends and in discovering relationships among attributes in diverse database applications. A range-sum query computes aggregate information over an online analytical processing (OLAP) data cube in specified query ranges. Existing techniques for range-sum queries on data cubes use an additional cube called the prefix sum cube (PC), to store the cumulative sums of data, causing a high space overhead. This space overhead not only leads to extra costs for storage devices, but also causes additional propagations of updates and longer access time on physical devices.In this paper, we present a new cube representation called 'the PC Pool', which drastically reduces the space of the PC in a large data warehouse. The PC Pool decreases the update propagation caused by the dependency between values in cells of the PC. We develop an effective algorithm, which finds dense sub-cubes from a large data cube. We perform an extensive experiment with diverse data sets, and examine the space reduction and performance of our proposed method with respect to various dimensions of the data cube and query sizes. Experimental results show that our method reduces the space of the PC while having a reasonable query performance.
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    ABSTRACT: A range-max query finds the maximum value over all selected cells of an on-line analytical processing (OLAP) data cube where the selection is specified by ranges of contiguous values for each dimension. One of the approaches to process such queries is to precompute a prefix cube (PC), which is a cube of the same dimensionality and size as the original data cube, but with some pre-computed results stored in each cell.
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    ABSTRACT: I/O parallelism is considered to be a promising approach to achieving high performance in parallel data warehousing systems where huge amounts of data and complex analytical queries have to be processed. This paper proposes a parallel secondary data cube storage structure (PHC for short) to efficiently support the processing of range sum queries and dynamic updates on data cube using parallel computing systems. Based on PHC, two parallel algorithms for processing range sum queries and updates are proposed also. Both the algorithms have the same time complexity, O(log d n/P). The analytical and experimental results show that PHC and the parallel algorithms have high performance and achieve optimum speedup.
    Journal of Computer Science and Technology 01/2005; 20(3):345-356. · 0.48 Impact Factor


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