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  2. Apache Spark - Wikipedia

    en.wikipedia.org/wiki/Apache_Spark

    Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...

  3. SPARK (programming language) - Wikipedia

    en.wikipedia.org/wiki/SPARK_(programming_language)

    A fourth version of the SPARK language, SPARK 2014, based on Ada 2012, was released on April 30, 2014. SPARK 2014 is a complete re-design of the language and supporting verification tools. The SPARK language consists of a well-defined subset of the Ada language that uses contracts to describe the specification of components in a form that is ...

  4. Spark NLP - Wikipedia

    en.wikipedia.org/wiki/Spark_NLP

    Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.

  5. MapReduce - Wikipedia

    en.wikipedia.org/wiki/MapReduce

    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 ...

  6. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    Self-organizing maps, like most artificial neural networks, operate in two modes: training and mapping. First, training uses an input data set (the "input space") to generate a lower-dimensional representation of the input data (the "map space"). Second, mapping classifies additional input data using the generated map. In most cases, the goal ...

  7. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    Kernel matrix defines the proximity of the input information. For example, in Gaussian radial basis function, it determines the dot product of the inputs in a higher-dimensional space, called feature space. It is believed that the data become more linearly separable in the feature space, and hence, linear algorithms can be applied on the data ...

  8. Software map - Wikipedia

    en.wikipedia.org/wiki/Software_map

    Software maps are applied in the context of software engineering: Complex, long-term software development projects are commonly faced by manifold difficulties such as the friction between completing system features and, at the same time, obtaining a high degree of code quality and software quality to ensure software maintenance of the system in the future.

  9. Map (higher-order function) - Wikipedia

    en.wikipedia.org/wiki/Map_(higher-order_function)

    Map functions can be and often are defined in terms of a fold such as foldr, which means one can do a map-fold fusion: foldr f z . map g is equivalent to foldr (f . g) z . The implementation of map above on singly linked lists is not tail-recursive , so it may build up a lot of frames on the stack when called with a large list.