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Redundancy or redundant may refer to: Language. Redundancy (linguistics), information that is expressed more than once; Engineering and computer science.
In linguistics, a redundancy is information that is expressed more than once. [1] [2]Examples of redundancies include multiple agreement features in morphology, [1] multiple features distinguishing phonemes in phonology, [2] or the use of multiple words to express a single idea in rhetoric. [1]
RAS syndrome, where RAS stands for redundant acronym syndrome (making the phrase "RAS syndrome" autological), is the redundant use of one or more of the words that make up an acronym in conjunction with the abbreviated form. This means, in effect, repeating one or more words from the acronym.
In computer main memory, auxiliary storage and computer buses, data redundancy is the existence of data that is additional to the actual data and permits correction of errors in stored or transmitted data.
Geographic redundancy corrects the vulnerabilities of redundant devices deployed by geographically separating backup devices. Geographic redundancy reduces the likelihood of events such as power outages, floods, HVAC failures, lightning strikes, tornadoes, building fires, wildfires, and mass shootings disabling most of the system if not the entirety of it.
redundant does not mean "useless" or "unable to perform its function". It means that there is an excess of something, that something is "surplus to requirements" and no longer needed. It can also refer to a duplicate of something retained as a backup, failsafe, or reinforcement.
The "up" in "climb up" is not always redundant, as in the example "He climbed up and then fell down the mountain." Many other examples of pleonasm are redundant only if the speaker's knowledge is taken into account. For example, most English speakers would agree that "tuna fish" is redundant because tuna is a kind of fish.
In describing the redundancy of raw data, the rate of a source of information is the average entropy per symbol. For memoryless sources, this is merely the entropy of each symbol, while, in the most general case of a stochastic process, it is