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A typical example of RDD-centric functional programming is the following Scala program that computes the frequencies of all words occurring in a set of text files and prints the most common ones. Each map , flatMap (a variant of map ) and reduceByKey takes an anonymous function that performs a simple operation on a single data item (or a pair ...
This list of JVM Languages comprises notable computer programming languages that are used to produce computer software that runs on the Java virtual machine (JVM). Some of these languages are interpreted by a Java program, and some are compiled to Java bytecode and just-in-time (JIT) compiled during execution as regular Java programs to improve performance.
SPARK is a formally defined computer programming language based on the Ada programming language, intended for the development of high integrity software used in systems where predictable and highly reliable operation is essential.
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
The Flajolet–Martin algorithm is an algorithm for approximating the number of distinct elements in a stream with a single pass and space-consumption logarithmic in the maximal number of possible distinct elements in the stream (the count-distinct problem).
As an imaginary example of the concept, when encoding an image built up from colored dots, the sequence "green green green green green green green green green" is shortened to "green x 9". This is most efficient on data that contains many such runs, for example, simple graphic images such as icons, line drawings, games, and animations.
Apriori [1] is an algorithm for frequent item set mining and association rule learning over relational databases.It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes.The method was invented by John Platt in the context of support vector machines, [1] replacing an earlier method by Vapnik, but can be applied to other classification models. [2]