Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Finding Frequent Items in Sliding Windows with Multinomially-Distributed Item Frequencies
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
A simpler and more efficient deterministic scheme for finding frequent items over sliding windows
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
CR-PRECIS: a deterministic summary structure for update data streams
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
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In this paper, we consider the problem of finding @e-approximate frequent items over a sliding window of size N. A recent work by Lee and Ting (2006) [7] solves the problem by giving an algorithm that supports O(1@e) query and update time, and uses O(1@e) space. Their query time and memory usage are essentially optimal, but the update time is not. We give a new algorithm that supports O(1) update time with high probability while maintaining the query time and memory usage as O(1@e).