Fast Algorithms For Hierarchical Range Histogram Construction

Data Warehousing and OLAPapplications typically view data as having multiple logical dimensions e.g., product, location with natural hierarchies de ned on each dimension. OLAP queries usually involve hierarchical selections on some of the dimensions, and often aggregate measure attributes e.g., sales, volume. Accurately estimating the distribution of measure attributes, under hierarchical selections, is important in a variety of scenarios, including approximate query evaluation and cost-based optimization of queries. In this paper, we propose fast near linear time algorithms for the problem of approximating the distribution of measure attributes with hierarchies de ned on them, using histograms. Our algorithms are based on dynamic programming and a novel notion of sparse intervals that we introduce, and are the rst practical algorithms for this problem. They e ectively trade space for construction time without compromising histogram accuracy. We complement our analytical contributions...

• Type: Conference
• Year: 2002
• Where: PODS
• Authors: Sudipto Guha, Nick Koudas, Divesh Srivastava
• Keywords: Database, Linear Time Algorithms, Measure Attributes E.g, PODS 2002, Rst Practical Algorithms