Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Statistical profile estimation in database systems
ACM Computing Surveys (CSUR)
Optimal histograms for limiting worst-case error propagation in the size of join results
ACM Transactions on Database Systems (TODS)
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimal histograms for hierarchical range queries (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Optimal and approximate computation of summary statistics for range aggregates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
What can Hierarchies do for Data Warehouses?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Universality of Serial Histograms
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Fast range query estimation by N-level tree histograms
Data & Knowledge Engineering
Optimizing candidate check costs for bitmap indices
Proceedings of the 14th ACM international conference on Information and knowledge management
Approximation and streaming algorithms for histogram construction problems
ACM Transactions on Database Systems (TODS)
Compact histograms for hierarchical identifiers
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
REHIST: relative error histogram construction algorithms
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Extreme visualization: squeezing a billion records into a million pixels
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Ad-hoc aggregations of ranked lists in the presence of hierarchies
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Hierarchical synopses with optimal error guarantees
ACM Transactions on Database Systems (TODS)
Wavelet synopsis for hierarchical range queries with workloads
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Multiplicative synopses for relative-error metrics
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Consistent histograms in the presence of distinct value counts
Proceedings of the VLDB Endowment
Tree-structured image difference for fast histogram and distance between histograms computation
Pattern Recognition Letters
Optimizing i/o costs of multi-dimensional queries using bitmap indices
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Approximate dynamic programming using halfspace queries and multiscale Monge decomposition
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
Histograms as statistical estimators for aggregate queries
Information Systems
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Data Warehousing and OLAP applications typically view data an having multiple logical dimensions (e.g., product, location) with natural hierarchies defined 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 defined on them, using histograms. Our algorithms are based on dynamic programming and a novel notion of sparse intervals that we introduce, and are the first practical algorithms for this problem. They effectively trade space for construction time without compromising histogram accuracy. We complement our analytical contributions with an experimental evaluation using real data sets, demonstrating the superiority of our approach.