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The main goal of Reactive Streams is to govern the exchange of stream data across an asynchronous boundary – like passing elements on to another thread or thread-pool – while ensuring that the receiving side is not forced to buffer arbitrary amounts of data.
Run-length encoding (RLE) is a form of lossless data compression in which runs of data (consecutive occurrences of the same data value) are stored as a single occurrence of that data value and a count of its consecutive occurrences, rather than as the original run.
The term "stream" is used in a number of similar ways: "Stream editing", as with sed, awk, and perl.Stream editing processes a file or files, in-place, without having to load the file(s) into a user interface.
Before explaining the benefits of streaming data, it is important to understand the difference between digitization and digitalization.Digitiztion is the creation (encoding) of digital information (e.g., a file) using analog information.
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In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation.
Though streaming algorithms had already been studied by Munro and Paterson [1] as early as 1978, as well as Philippe Flajolet and G. Nigel Martin in 1982/83, [2] the field of streaming algorithms was first formalized and popularized in a 1996 paper by Noga Alon, Yossi Matias, and Mario Szegedy. [3]
Data Stream contains different sets of data, that depend on the chosen data format. Attributes – each attribute [3] of the data stream represents a certain type of data, e.g. segment / data point ID, timestamp, geodata.