Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Distributed streams algorithms for sliding windows
Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
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)
Continuously Maintaining Quantile Summaries of the Most Recent N Elements over a Data Stream
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
Maintaining significant stream statistics over sliding windows
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
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
Sketch-based querying of distributed sliding-window data streams
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
We study the problem of identifying items with heavy weights in the sliding window of a weighted data stream. We give a deterministic algorithm that solves the problem within error bound Ɛ, uses O(R/Ɛ) space and supports O(R/Ɛ) query and update times. Here, R is the maximum item weight. We also show that the space can be reduced substantially in practice by showing for any c 0, we can construct an O(c log R/Ɛ2)-space algorithm, which returns correct answers provided that the ratio between the total weights of any two adjacent sliding windows is not greater than c. We also give a randomized algorithm that solves the problem with success probability 1 - δ using O(1/Ɛ2 log R log D log log D/δƐ) space where D is the number of distinct items in the data stream.