Results 221 to 230 of about 36,814 (268)
Warping the time on data streams [PDF]
Data & Knowledge Engineering, 2007 Continuously monitoring through time the correlation/distance of multiple data streams is of interest in a variety of applications, including financial analysis, video surveillance, and mining of biological data. However, distance measures commonly adopted for comparing time series, such as Euclidean and Dynamic Time Warping (DTW), either are known to ...
CAPITANI, PAOLO, CIACCIA, PAOLO
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Communications of the ACM, 2010
In today's real-time Web, data streaming applications no longer have the luxury of making multiple passes over a recorded data set.
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In today's real-time Web, data streaming applications no longer have the luxury of making multiple passes over a recorded data set.
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Proceedings of the thirty-third annual ACM symposium on Theory of computing, 2001
Histograms have been used widely to capture data distribution, to represent the data by a small number of step functions. Dynamic programming algorithms which provide optimal construction of these histograms exist, albeit running in quadratic time and linear space.
Sudipto Guha, Nick Koudas, Kyuseok Shim
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Histograms have been used widely to capture data distribution, to represent the data by a small number of step functions. Dynamic programming algorithms which provide optimal construction of these histograms exist, albeit running in quadratic time and linear space.
Sudipto Guha, Nick Koudas, Kyuseok Shim
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2006
Recent research efforts in the fields of data stream processing and data stream management systems (DSMSs) show the increasing importance of processing data streams, e. g., in the e-science domain. Together with the advent of peer-to-peer (P2P) networks and grid computing, this leads to the necessity of developing new techniques for distributing and ...
Richard Kuntschke, Alfons Kemper
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Recent research efforts in the fields of data stream processing and data stream management systems (DSMSs) show the increasing importance of processing data streams, e. g., in the e-science domain. Together with the advent of peer-to-peer (P2P) networks and grid computing, this leads to the necessity of developing new techniques for distributing and ...
Richard Kuntschke, Alfons Kemper
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Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279), 2002
This work presents a new I/O system design and implementation targeted at applications that perform data streaming. The approach yields true zero-copy transfers between I/O devices in many instances. We give a general characterization of I/O elements and provide a framework that allows analysis of the potential for zero-copy transfers.
Frank W. Miller +2 more
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This work presents a new I/O system design and implementation targeted at applications that perform data streaming. The approach yields true zero-copy transfers between I/O devices in many instances. We give a general characterization of I/O elements and provide a framework that allows analysis of the potential for zero-copy transfers.
Frank W. Miller +2 more
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Streaming and Data Enrichment.
2007Data is being created at a continuously increasing rate. Scientists are "drowning in their data". So much so they cannot visualize the data to see the big picture. This work-in-progress paper describes a data capture and visualization problem, and the steps being undertaken to solve the problem. An overview is given of the various approaches to dealing
Mary J. McDerby +2 more
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Data Streaming in Telepresence Environments
IEEE Transactions on Visualization and Computer Graphics, 2005In this paper, we discuss data transmission in telepresence environments for collaborative virtual reality applications. We analyze data streams in the context of networked virtual environments and classify them according to their traffic characteristics. Special emphasis is put on geometry-enhanced (3D) video.
Edouard Lamboray +2 more
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9th International Database Engineering & Application Symposium (IDEAS'05), 2006
We present external-memory algorithms for differencing large hierarchical datasets. Our methods are especially suited to streaming data with bounded differences. For input sizes m and n and maximum output (difference) size e, the I/O, RAM, and CPU costs of our algorithm rdiff are, respectively, m + n, 4e + 8, and O(MN).
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We present external-memory algorithms for differencing large hierarchical datasets. Our methods are especially suited to streaming data with bounded differences. For input sizes m and n and maximum output (difference) size e, the I/O, RAM, and CPU costs of our algorithm rdiff are, respectively, m + n, 4e + 8, and O(MN).
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Data Is a Stream: Security of Stream-Based Channels
2015The common approach to defining secure channels in the literature is to consider transportation of discrete messages provided via atomic encryption and decryption interfaces. This, however, ignores that many practical protocols (including TLS, SSH, and QUIC) offer streaming interfaces instead, moreover with the complexity that the network (possibly ...
Marc Fischlin +3 more
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