| 1 |
Streaming-Data Algorithms for High-Quality Clustering
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| 2 |
Random Sampling with a Resercoir
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| 3 |
BIRCH: An Efficient Data Clustering Method for Very Large Databases
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| 4 |
StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time
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| 5 |
The Space Complexity of Approximating the Frequency Monent
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| 6 |
Continuous Queries over Data Streams
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| 7 |
DEMON: Mining and Monitoring Evolving Data
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| 8 |
Clustering Data Streams
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| 9 |
CURE: An Efficient Clustering Algorithm for Large Databases
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| 10 |
Stable Distributions, Pseudorandom Generators, Embeddings and Data Stream Computation
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| 11 |
Models and Issues in Data Stream Systems
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| 12 |
Fast, small-space algorithms for approximate histogram maintenance
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| 13 |
Selectivity and Cost Estimation for Joins Based on Random Sampling
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| 14 |
Better streaming algorithms for clustering problems
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| 15 |
Mining high-speed data streams
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| 16 |
An Approximate L1-Difference Algorithm for Massive
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| 17 |
Probabilistic Counting Algorithms for Data Base Applications
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| 18 |
Approximate Medians and other Quantiles in One Pass and with Limited Memory
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| 19 |
Synopsis Data Structures for Massive Data Sets
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| 20 |
Selection andSorting with Limited Storage
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| 21 |
Space-Efficient Online Computation of Quantile Summaries
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| 22 |
Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation
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| 23 |
Data-streams and histograms
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| 24 |
Approximate Frequency Count over Data Streams
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