TY - GEN
T1 - Sketching asynchronous streams over a sliding window
AU - Tirthapura, Srikanta
AU - Xu, Bojian
AU - Busch, Costas
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - We study the problem of maintaining sketches of recent elements of a data stream. Motivated by applications involving network data, we consider streams that are asynchronous, in which the observed order of data is not the same as the time order in which the data was generated. The notion of recent elements of a stream is modeled by the sliding timestamp window, which is the set of elements with timestamps that are close to the current time. We design algorithms for maintaining sketches of all elements within the sliding timestamp window that can give provably accurate estimates of two basic aggregates, the sum and the median, of a stream of numbers. The space taken by the sketches, the time needed for querying the sketch, and the time for inserting new elements into the sketch are all polylog with respect to the maximum window size and the values of the data items in the window. Our sketches can be easily combined in a loss-less and compact way, making them useful for distributed computations over data streams. Previous works on sketching recent elements of a data stream have all considered the more restrictive scenario of synchronous streams, where the observed order of data is the same as the time order in which the data was generated. Our notion of recency of elements is more general than that studied in previous work, and thus our sketches are more robust to network delays and asynchrony.
AB - We study the problem of maintaining sketches of recent elements of a data stream. Motivated by applications involving network data, we consider streams that are asynchronous, in which the observed order of data is not the same as the time order in which the data was generated. The notion of recent elements of a stream is modeled by the sliding timestamp window, which is the set of elements with timestamps that are close to the current time. We design algorithms for maintaining sketches of all elements within the sliding timestamp window that can give provably accurate estimates of two basic aggregates, the sum and the median, of a stream of numbers. The space taken by the sketches, the time needed for querying the sketch, and the time for inserting new elements into the sketch are all polylog with respect to the maximum window size and the values of the data items in the window. Our sketches can be easily combined in a loss-less and compact way, making them useful for distributed computations over data streams. Previous works on sketching recent elements of a data stream have all considered the more restrictive scenario of synchronous streams, where the observed order of data is the same as the time order in which the data was generated. Our notion of recency of elements is more general than that studied in previous work, and thus our sketches are more robust to network delays and asynchrony.
KW - Aggregates
KW - Asynchronous streams
KW - Data stream processing
KW - Distributed streams
KW - Sketches of streams
KW - Sliding windows
KW - Union of streams
UR - http://www.scopus.com/inward/record.url?scp=33748712629&partnerID=8YFLogxK
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U2 - 10.1145/1146381.1146397
DO - 10.1145/1146381.1146397
M3 - Conference contribution
AN - SCOPUS:33748712629
SN - 1595933840
SN - 9781595933843
T3 - Proceedings of the Annual ACM Symposium on Principles of Distributed Computing
SP - 82
EP - 91
BT - Proceedings of the 25th Annual ACM Symposium on Principles of Distributed Computing 2006
PB - Association for Computing Machinery (ACM)
T2 - 25th Annual ACM Symposium on Principles of Distributed Computing 2006
Y2 - 23 July 2006 through 26 July 2006
ER -