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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.
This approach to architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data. The two view outputs may be joined before presentation.
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. One example of such use is to do a search and replace on all the files in a directory, from the command line.
Oracle Event Processing - for building applications to filter, correlate, and process events in real time. SAP ESP - A low-latency, rapid development and deployment platform that allows processing multiple streams of data in real time [19] SQLstream SQLstream's stream processing platform, s-Server, provides a relational stream computing ...
In event stream processing (ESP), both ordinary and notable events happen. Ordinary events (orders, RFID transmissions) are screened for notability and streamed to information subscribers. Event stream processing is commonly used to drive the real-time flow of information in and around the enterprise, which enables in-time decision making. [10]
These algorithms are designed to operate with limited memory, generally logarithmic in the size of the stream and/or in the maximum value in the stream, and may also have limited processing time per item. As a result of these constraints, streaming algorithms often produce approximate answers based on a summary or "sketch" of the data stream.
Real-estate: Websites can track a subset of data from consumers’ mobile devices and makes real-time property recommendations of properties to visit based on their geo-location . Gaming: An online gaming company can collect streaming data about player-game interactions, and feeds the data into its gaming platform ( Amazon ).
The stream processing nature of GPUs remains valid regardless of the APIs used. (See e.g., [38]) GPUs can only process independent vertices and fragments, but can process many of them in parallel. This is especially effective when the programmer wants to process many vertices or fragments in the same way.