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    DataEngine

    Category  Intelligent Software>Data Mining Systems/Tools

    Abstract   DataEngine is an intelligent data analysis and data mining
    software system. By using neural networks, fuzzy logic and statistical
    methods, DataEngine provides you with the most advanced
    techniques for data analysis. DataEngine Components/Features
    include:

    Data Access - Product offers various options for data access through
    its flexible import and export interfaces - 1) import and export of
    Microsoft Excel files; 2) flexible import and export of American Standard
    Code for Information Interchange (ASCII) files; 3) database interface,
    including import and export of data via Object Linking and Embedding,
    Database (OLE DB)/Open Database Connectivity (ODBC), and its
    'mapping' feature allows you to transform the values of categorical
    variables into numerical values; 4) generic 'data acquisition board'
    support including (analog input and output, trigger and pre-trigger
    support).

    Basic Functions - DataEngine supplies basic mathematical, statistical
    and signal processing functions.

    Data Analysis & Data Mining - Fuzzy logic and neural networks are the
    basis of DataEngine's data analysis and data mining power. Fuzzy and
    neural models developed with DataEngine can be exported using the
    DataEngine ADL (Application Development Library).

    Fuzzy Rule Base features are - 1) linguistic If-Then Rules; 2) examples
    of potential applications are: knowledge-based diagnosis,
    classification, control and modeling; 3) multi-step inference technique;
    4) supports the representation of symbolic and/or linguistic
    information; 5) fuzzy operators are: minimum, maximum, algebraic
    product, algebraic sum, gamma operator; 6) defuzzyfying strategies
    are: mean of maxima, center of gravity and fuzzy output; 7) debugging.

    Multilayer Perceptron features are - 1) supervised learning neural
    network; 2) examples of potential applications are: classification,
    modeling and control; 3) learning rules are: backpropagation,
    quickpropagation, Super Self Adaptive Backpropagation (SAB), resilent
    propagation, each with momentum and decay; 4) learning rate decay
    to avoid overfitting; 5) pruning algorithm for adapting the structure of the
    neural network architecture; 6) shortcut connections are possible:
    direct connections of input with output parameters and skipping
    hidden layers; 7) the 'best' neural network that appears during the
    training process can be saved automatically and the creation criteria
    for the 'best' network can also be configured.

    Kohonen Feature Map features are - 1) unsupervised learning neural
    network; 2) examples of potential applications are: clustering and
    classification; 3) labeling by examples; 4) graphical representation of
    the feature map.

    Fuzzy C-Means features are - 1) fuzzy clustering algorithm; 2) examples
    of potential applications are: clustering and classification; 3) initialize
    algorithm with pre-defined data partitions; 4) labeling by examples; 5)
    the 'best' number of classes can be determined automatically and the
    criteria for the 'best' number of classes can also be configured.

    Fuzzy Kohonen Network features are - 1) unsupervised learning neural
    network; 2) examples of potential applications are: clustering,
    classification; 3) very fast fuzzy training algorithm; 4) labeling by
    examples; 5) graphical representation of the feature map.

    Model Analysis - An analysis component that supports the validation
    and the improvement of your models and consists of:

    1) Adjustment of operating values - The feature values of objects can
    be varied and the model's changing outputs can then be displayed. A
    "slider" software device is provided that can be used to vary the feature
    values.

    2) Transfer function - On the basis of the chosen operating point a
    transfer function can be computed by varying (automatically) one or two
    of the features within their value range(s). The resulting function can be
    visualized as a 2- or 3-dimensional diagram.

    3) Sensitivity analysis - On the basis of the chosen operating point a
    sensitivity analysis can be carried out for the current model. To help
    assess the sensitivity of the model, three (3) measures are provided.
    Each one of the measures is computed by varying one of the feature
    values. These measures are: 1) the minimum and maximum values of
    an output variable can be (shown as a hi-lo-close-plot); 2) the graph of
    an output variable can be (shown as a set of curves); 3) the cumulative
    derivation of an output variable can be (shown as a bar diagram).

    Test error - In addition to the test evaluations that currently exist in the
    model editors several additional error measures can also be
    calculated.

    Matrix of confusion - For classification tasks a 'matrix of confusion' is
    computed to determine what types of misclassifications exist in the
    test data.

    Visualization - The 2D and 3D charting module facilitates the
    interpretation of data analysis results and delivers presentation-style
    graphics for your reports. This module is available throughout
    DataEngine.

    DataEngine PlugIns - DataEngine capabilities can be extended by the
    user via user-defined function blocks. A Dynamic Link Library (DLL)
    based PlugIn interface enables you to integrate your own analysis
    tools into DataEngine in a simple and effective way.

    Cards - DataEngine's Cards provide a graphical macro language that
    can be used for the automation of data analysis procedures.

    System Requirements  

    A) 32-bit multi-threaded application
    B) complete integration with the  Microsoft Windows operating system
    C) An OLE interface allows you to configure the models and to perform
    the training/labeling/testing from within other programs (e.g. for
    Windows-based data analysis applications).

    Additional Software Requirements for Server License

    Microsoft Windows 2000 Terminal Server

    Manufacturer   Home office; see web site for international distributors.

    Management Intelligenter Technologien GmbH
    Pascalstraße 69
    52076 Aachen, Germany
    software@mitgmbh.de
    by fax +49 2408 94582
    by phone +49 2408 94580

    Manufacturer's Web Site  www.dataengine.de/english/sp/index.htm

    Price  DataEngine Server License Server version for MS Windows 2000
    Terminal Server incl. 3 Clients*  Euro 35.000,-

    DataEngine Workstation License  Euro 8.000.-

    DataEngine ADL for Win95/98 or NT  Euro 1.500.-

    DataEngine V.i  Euro 2.500.-

    G6G Product Number  20042

    G6G Manufacturer Number  101714
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