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Approximate counts and quantiles over sliding windows
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Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems table of contents
Paris, France
SESSION: Data streams table of contents
Pages: 286 - 296  
Year of Publication: 2004
ISBN:158113858X
Authors
Arvind Arasu  Stanford University
Gurmeet Singh Manku  Stanford University
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGMOD: ACM Special Interest Group on Management of Data
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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ABSTRACT

We consider the problem of maintaining ε-approximate counts and quantiles over a stream sliding window using limited space. We consider two types of sliding windows depending on whether the number of elements N in the window is fixed (fixed-size sliding window) or variable (variable-size sliding window). In a fixed-size sliding window, both the ends of the window slide synchronously over the stream. In a variable-size sliding window, an adversary slides the window ends independently, and therefore has the ability to vary the number of elements N in the window.We present various deterministic and randomized algorithms for approximate counts and quantiles. All of our algorithms require O(1/ε polylog(1/ε, N)) space. For quantiles, this space requirement is an improvement over the previous best bound of O(1/ε2 polylog(1/ε, N)). We believe that no previous work on space-efficient approximate counts over sliding windows exists.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY  23
 
 
 
 
 
Collaborative Colleagues:
Arvind Arasu: colleagues
Gurmeet Singh Manku: colleagues