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In information technology and computer science, the pattern of applying one-way mutations on an immutable data state is called unidirectional data flow. [1] Separation of state changes from presentation has many benefits and was popularized with Redux for unidirectional data flow combined with React for presenting, or rendering, data state.
Data flow has been proposed [by whom?] as an abstraction for specifying the global behavior of distributed system components: in the live distributed objects programming model, distributed data flows are used to store and communicate state, and as such, they play the role analogous to variables, fields, and parameters in Java-like programming ...
The examples above are problems in which the data-flow value is a set, e.g. the set of reaching definitions (Using a bit for a definition position in the program), or the set of live variables. These sets can be represented efficiently as bit vectors , in which each bit represents set membership of one particular element.
A unidirectional network (also referred to as a unidirectional gateway or data diode) is a network appliance or device that allows data to travel in only one direction. Data diodes can be found most commonly in high security environments, such as defense, where they serve as connections between two or more networks of differing security ...
There have been multiple data-flow/stream processing languages of various forms (see Stream processing). Data-flow hardware (see Dataflow architecture) is an alternative to the classic von Neumann architecture. The most obvious example of data-flow programming is the subset known as reactive programming with spreadsheets. As a user enters new ...
Data flow diagram with data storage, data flows, function and interface. A data-flow diagram is a way of representing a flow of data through a process or a system (usually an information system). The DFD also provides information about the outputs and inputs of each entity and the process itself.
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. With this form of generative deep learning , the output layer can get information from past (backwards) and future (forward) states simultaneously.
Packet flow can be represented as a path in a network to model network performance. For example, a water flow network can be used to conceptualize packet flow. Communication channels can be thought of as pipes, with the pipe capacity corresponding to bandwidth and flows corresponding to data throughput. This visualization can help to understand ...