Optimal histograms for limiting worst-case error propagation in the size of join results
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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
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Approximate medians and other quantiles in one pass and with limited memory
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Random sampling for histogram construction: how much is enough?
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Wavelet-based histograms for selectivity estimation
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Data cube approximation and histograms via wavelets
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Join synopses for approximate query answering
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The Aqua approximate query answering system
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Progressive vector transmission
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Optimal histograms for hierarchical range queries (extended abstract)
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Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
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Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Histogram-Based Approximation of Set-Valued Query-Answers
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Universality of Serial Histograms
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Selectivity Estimation Without the Attribute Value Independence Assumption
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Fast Incremental Maintenance of Approximate Histograms
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Fast Approximate Answers to Aggregate Queries on a Data Cube
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SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
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Dynamic multidimensional histograms
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Distributed streams algorithms for sliding windows
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Fast incremental maintenance of approximate histograms
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Temporal and spatio-temporal aggregations over data streams using multiple time granularities
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Pass efficient algorithms for approximating large matrices
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
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Temporal Aggregation over Data Streams Using Multiple Granularities
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Semantic Approximation of Data Stream Joins
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
Content-based routing: different plans for different data
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Approximation algorithms for wavelet transform coding of data streams
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Matrix approximation and projective clustering via volume sampling
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The space complexity of pass-efficient algorithms for clustering
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Research issues in data stream association rule mining
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On graph problems in a semi-streaming model
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Approximation and streaming algorithms for histogram construction problems
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Discretization from data streams: applications to histograms and data mining
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Counting triangles in data streams
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A simpler and more efficient deterministic scheme for finding frequent items over sliding windows
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CFI-Stream: mining closed frequent itemsets in data streams
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An integrated efficient solution for computing frequent and top-k elements in data streams
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Data streams: algorithms and applications
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Error minimization in approximate range aggregates
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Incremental discretization, application to data with concept drift
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ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Comparing data streams using Hamming norms (how to zero in)
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Approximate frequency counts over data streams
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Tuple routing strategies for distributed eddies
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Finding hierarchical heavy hitters in data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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REHIST: relative error histogram construction algorithms
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Finding hierarchical heavy hitters in streaming data
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Multiple-Objective Compression of Data Cubes in Cooperative OLAP Environments
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Conceptual modeling rules extracting for data streams
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Histograms have been used widely to capture data distribution, to represent the data by a small number of step functions. Dynamic programming algorithms which provide optimal construction of these histograms exist, albeit running in quadratic time and linear space. In this paper we provide linear time construction of 1 + &egr; approximation of optimal histograms, running in polylogarithmic space.Our results extend to the context of data-streams, and in fact generalize to give 1 + &egr; approximation of several problems in data-streams which require partitioning the index set into intervals. The only assumptions required are that the cost of an interval is monotonic under inclusion (larger interval has larger cost) and that the cost can be computed or approximated in small space. This exhibits a nice class of problems for which we can have near optimal data-stream algorithms.