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  2. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    The term big data has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term. [22] [23] Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

  3. Big Ideas Learning - Wikipedia

    en.wikipedia.org/wiki/Big_Ideas_Learning

    The origins of Big Ideas Learning go back to 1980, when mathematics textbook author Ron Larson started a small company called Larson Texts. The company became incorporated in Pennsylvania in 1992 and became Larson Texts, Inc. [2]

  4. Lambda architecture - Wikipedia

    en.wikipedia.org/wiki/Lambda_architecture

    The two view outputs may be joined before presentation. The rise of lambda architecture is correlated with the growth of big data, real-time analytics, and the drive to mitigate the latencies of map-reduce. [1] Lambda architecture depends on a data model with an append-only, immutable data source that serves as a system of record.

  5. Programming with Big Data in R - Wikipedia

    en.wikipedia.org/wiki/Programming_with_Big_Data_in_R

    Programming with Big Data in R (pbdR) [1] is a series of R packages and an environment for statistical computing with big data by using high-performance statistical computation. [ 2 ] [ 3 ] The pbdR uses the same programming language as R with S3/S4 classes and methods which is used among statisticians and data miners for developing statistical ...

  6. Topological data analysis - Wikipedia

    en.wikipedia.org/wiki/Topological_data_analysis

    The algorithm for persistent homology over was given by Edelsbrunner et al. [8] Zomorodian and Carlsson gave the practical algorithm to compute persistent homology over all fields. [10] Edelsbrunner and Harer's book gives general guidance on computational topology. [19] One issue that arises in computation is the choice of complex.

  7. Big data maturity model - Wikipedia

    en.wikipedia.org/wiki/Big_Data_Maturity_Model

    The TDWI big data maturity model is a model in the current big data maturity area and therefore consists of a significant body of knowledge. [6] Maturity stages. The different stages of maturity in the TDWI BDMM can be summarized as follows: Stage 1: Nascent. The nascent stage as a pre–big data environment. During this stage:

  8. Data Science and Predictive Analytics - Wikipedia

    en.wikipedia.org/wiki/Data_Science_and...

    The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the Journal of the American Statistical Association, [5] International Statistical Institute’s ISI Review Journal, [3] and the Journal of the American Library Association. [4] Many scholarly publications reference the DSPA textbook. [6] [7]

  9. Data science - Wikipedia

    en.wikipedia.org/wiki/Data_science

    A cloud-based architecture for enabling big data analytics. Data flows from various sources, such as personal computers, laptops, and smart phones, through cloud services for processing and analysis, finally leading to various big data applications. Cloud computing can offer access to large amounts of computational power and storage. [40]