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  2. 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 ...

  3. 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 ...

  4. Comparison of programming languages (associative array)

    en.wikipedia.org/wiki/Comparison_of_programming...

    The arrays are heterogeneous: a single array can have keys of different types. PHP's associative arrays can be used to represent trees, lists, stacks, queues, and other common data structures not built into PHP. An associative array can be declared using the following syntax:

  5. Dataframe - Wikipedia

    en.wikipedia.org/wiki/Dataframe

    Download as PDF; Printable version; In other projects ... common to many data processing libraries: pandas (software) § DataFrames; The Dataframe API in Apache Spark ...

  6. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a mapping of the set of words to a vector space , typically of several hundred dimensions , with each unique word in the corpus being assigned a vector in the space.

  7. 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 ...

  8. Simultaneous localization and mapping - Wikipedia

    en.wikipedia.org/wiki/Simultaneous_localization...

    MAP estimators compute the most likely explanation of the robot poses and the map given the sensor data, rather than trying to estimate the entire posterior probability. New SLAM algorithms remain an active research area, [6] and are often driven by differing requirements and assumptions about the types of maps, sensors and models as detailed ...

  9. AoS and SoA - Wikipedia

    en.wikipedia.org/wiki/AOS_and_SOA

    Structure of arrays (SoA) is a layout separating elements of a record (or 'struct' in the C programming language) into one parallel array per field. [1] The motivation is easier manipulation with packed SIMD instructions in most instruction set architectures, since a single SIMD register can load homogeneous data, possibly transferred by a wide internal datapath (e.g. 128-bit).