Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Global optimization of histograms
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Wavelet synopses with error guarantees
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Range Selectivity Estimation for Continuous Attributes
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
The optimization of queries in relational databases
The optimization of queries in relational databases
Improving range-sum query evaluation on data cubes via polynomial approximation
Data & Knowledge Engineering
Data mining middleware for wide-area high-performance networks
Future Generation Computer Systems - IGrid 2005: The global lambda integrated facility
Approximate range---sum query answering on data cubes with probabilistic guarantees
Journal of Intelligent Information Systems
Hierarchical synopses with optimal error guarantees
ACM Transactions on Database Systems (TODS)
Optimality and scalability in lattice histogram construction
Proceedings of the VLDB Endowment
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The need to compress data into synopses of summarized information often arises in many application scenarios, where the aim is to retrieve aggregate data efficiently, possibly trading off the computational efficiency with the accuracy of the estimation. A widely used approach for summarizing multi-dimensional data is the histogram-based representation scheme, which consists in partitioning the data domain into a number of blocks (called buckets), and then storing summary information for each block. In this paper, a new histogram-based summarization technique which is very effective for multi-dimensional data is proposed. This technique exploits a multi-resolution organization of summary data, on which an efficient physical representation model is defined. The adoption of this representation model (based on a hierarchical organization of the buckets) enables some storage space to be saved w.r.t. traditional histograms, which can be invested to obtain finer grain blocks, thus approximating data with more detail. Experimental results show that our technique yields higher accuracy in retrieving aggregate information from the histogram w.r.t. traditional approaches (classical multi-dimensional histograms as well as other types of summarization technique).