Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Identifying frequent items in sliding windows over on-line packet streams
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
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
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sketching asynchronous streams over a sliding window
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
The frequent items problem, under polynomial decay, in the streaming model
Theoretical Computer Science
Time-decaying Sketches for Robust Aggregation of Sensor Data
SIAM Journal on Computing
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Capturing characteristics of large data streams has received considerable attention. The constraints in space and time restrict the data stream processing to only one pass (or a small number of passes). Processing data streams over sliding windows make the problem more difficult and challenging. In this paper, we address the problem of maintaining ∈-approximate variance of data streams over sliding windows. To our knowledge, the best existing algorithm requires O(1/∈2 log N) space, though the lower bound for this problem is Ω(1/∈ log N). We propose the first ∈-approximation algorithm to this problem that is optimal in both space and worst case time. Our algorithm requires O(1/∈ log N) space. Furthermore, its running time is O(1) in worst case.