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  2. Activity relationship chart - Wikipedia

    en.wikipedia.org/wiki/Activity_relationship_chart

    An activity relationship chart (ARC) is a tabular means of displaying the closeness rating among all pairs of activities or departments. [1] In an ARC there are six closeness ratings which may be assigned to each pair of departments, as well as nine reasons for those ratings (each is assigned by a reason code).

  3. Data model - Wikipedia

    en.wikipedia.org/wiki/Data_model

    Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.

  4. Critical path method - Wikipedia

    en.wikipedia.org/wiki/Critical_path_method

    Logical end points such as milestones or deliverable items; Using these values, CPM calculates the longest path of planned activities to logical end points or to the end of the project, and the earliest and latest that each activity can start and finish without making the project longer. This process determines which activities are "critical ...

  5. Office Open XML - Wikipedia

    en.wikipedia.org/wiki/Office_Open_XML

    In 2000, Microsoft released an initial version of an XML-based format for Microsoft Excel, which was incorporated in Office XP. In 2002, a new file format for Microsoft Word followed. [9] The Excel and Word formats—known as the Microsoft Office XML formats—were later incorporated into the 2003 release of Microsoft Office.

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

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