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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.
When referring to streaming technologies, real-time processing means that a transaction is processed fast enough for the result to come back and be acted on right away. [2] Such real-time databases are useful for assisting social media platforms in the removal of fake news, in-store surveillance cameras identifying potential shoplifters by ...
Stream processing — in parallel processing, especially in graphic processing, the term stream is applied to hardware as well as software. There it defines the quasi-continuous flow of data that is processed in a dataflow programming language as soon as the program state meets the starting condition of the stream.
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 ...
The term "near real-time" or "nearly real-time" (NRT), in telecommunications and computing, refers to the time delay introduced, by automated data processing or network transmission, between the occurrence of an event and the use of the processed data, such as for display or feedback and control purposes. For example, a near-real-time display ...
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]
Streaming data is data that is continuously generated by different sources. Such data should be processed incrementally using stream processing techniques without having access to all of the data. In addition, it should be considered that concept drift may happen in the data which means that the properties of the stream may change over time.