Data stream

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  • Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.

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  • Open issue trackers are a type of social media that has received relatively little attention from the text-mining community. We investigate the problems inherent in learning to triage bug reports from time-varying data. We demonstrate that concept drift is an important consideration. We show the effectiveness of online learning algorithms by evaluating them on several bug report datasets collected from open issue trackers associated with large open-source projects. We make this collection of data publicly available. ...

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  • 4.2.3 MTMF MTMF combines the best parts of the Linear Spectral Mixing model and the statistical Matched Filter model while avoiding the drawbacks of each parent method (Boardman, 1998). It is a useful Matched Filter method without knowing all the possible endmembers in a landscape especially in case of subtle, sub-pixel occurrences. Firstly, pixel spectra and endmember spectra require a minimum noise fraction (MNF) (Green et al., 1988, Boardman, 1993) transformation. MNF reduces and separates an image into its most dimensional and non-noisy components.

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  • Data Streams Models and Algorithms- P1: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

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  • Data Streams Models and Algorithms- P2: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

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  • Data Streams Models and Algorithms- P6: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

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  • Data Streams Models and Algorithms- P3: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

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  • Data Streams Models and Algorithms- P4: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

    pdf30p alone246 04-09-2010 33 5   Download

  • Data Streams Models and Algorithms- P5: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

    pdf30p alone246 04-09-2010 38 4   Download

  • Data Streams Models and Algorithms- P9: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

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  • Data Streams Models and Algorithms- P11: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

    pdf30p alone246 04-09-2010 37 4   Download

  • Data Streams Models and Algorithms- P12: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

    pdf30p alone246 04-09-2010 33 4   Download

  • Data Streams Models and Algorithms- P7: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

    pdf30p alone246 04-09-2010 36 3   Download

  • Data Streams Models and Algorithms- P8: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

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  • Data Streams Models and Algorithms- P10: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

    pdf30p alone246 04-09-2010 35 3   Download

  • Data Streams Models and Algorithms- P13: In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. In addition, the development of sensor technology has resulted in the possibility of monitoring many events in real time.

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  • Briefly review fundamental stream operations and introduce the stream classes that are provided by System.IO. Point out that this module discusses synchronous operations only; asynchronous operations are beyond the scope of this course.

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  • LABORATORY PROCEDURE I Sampling A. The time division Multiplexer (TDM MUX) module is used to recombine two analog signals into one data stream. The input A is connected to the OUT connector for a period T (T = 1/fs). During the next period, input B is connected to the OUT connector. The process then repeats. It should be obvious that each input, input A(or B) is “Sampled” at a rate equal to ½ fs as set by the Master clock. NOTE: The actual waveform sampling rate = ½ fs as set by the “DATA/Sampling RATE”, output, mentioned above (e.g. to...

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  • Oracle Streams Concepts and Administration describes the features and functionality of Streams. This document contains conceptual information about Streams, along with information about managing a Streams environment. In addition, this document contains detailed examples that configure a Streams capture and apply environment and a rule-based application.

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  • [ Team LiB ] Recipe 5.4 Serializing Data Problem You need to serialize the contents of a DataSet so that you can store the data on a disk or transfer it across a network. Solution You can serialize a DataSet into XML, binary, or SOAP formats and save the serialized DataSet to a stream (such as a file or network stream).

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