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MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the graphical user interface (GUI), the command-line, and the Java API.
Standard input is a stream from which a program reads its input data. The program requests data transfers by use of the read operation. Not all programs require stream input. For example, the dir and ls programs (which display file names contained in a directory) may take command-line arguments, but perform their operations without any stream ...
Most (90%) of a stream processor's work is done on-chip, requiring only 1% of the global data to be stored to memory. This is where knowing the kernel temporaries and dependencies pays. Internally, a stream processor features some clever communication and management circuits but what's interesting is the Stream Register File (SRF). This is ...
Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.
Apache Storm is a distributed stream processing computation framework written predominantly in the Clojure programming language. Originally created by Nathan Marz [2] and team at BackType, [3] the project was open sourced after being acquired by Twitter. [4]
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The platform was known as Java 2 Platform, Standard Edition or J2SE from version 1.2, until the name was changed to Java Platform, Standard Edition or Java SE in version 1.5. The "SE" is used to distinguish the base platform from the Enterprise Edition and Micro Edition platforms. The "2" was originally intended to emphasize the major changes ...
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