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Applications

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Applications

Industrial and generic solutions such as Spotfire Copilot™

31 files

  1. DSML Toolkit for Python - Documentation and Spotfire® Examples

    This is the documentation, examples and data functions using spotfire-dsml Python package (downloadable from PyPI).
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  2. Missing Data Navigator for Spotfire®

    The Missing Data Navigator for Spotfire® is an interactive tool for analyzing and handling missing data. It is constructed to lead you through the typical steps of missing data analysis with the aim of providing guidance along this path.
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  3. Network Chart Template for Spotfire®

    This template provides you with a means to create a network chart to display the multi-directional relationships between the nodes in your data.
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  4. CPV Stage 2 - Process Parameter Qualification with Spotfire Statistica®

    This application demonstrates aspects of implementing continuous process verification using Spotfire Statistica®.
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  5. Alerting Framework for Spotfire®

    The Alerting Framework for Spotfire® is an extension for Spotfire that allows you to leverage your existing Spotfire analysis files to receive Alerts. It provides seamless integration with Automation Services via an Alerting Task.
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  6. Templates for Statistica® Data Entry in Spotfire®

    This component is showing the integration between Spotfire® and Statistica® Data Entry data. You can find here Spotfire® applications for exploring Statistica® Data Entry data.
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  7. Loss Distribution Approach to Operational Risk: Analysis Template for Spotfire

    This analysis implements simple frequency-severity models for Operational Risk event types. This forms the basis of the Loss Distribution Approach alternative in the Basel regulations.
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  8. Spotfire Copilot™

    A natural language extension to Spotfire®.
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  9. Natural Language Generation Example for Spotfire®

    An extensible natural language generation Spotfire dashboard implemented through a TERR data function.
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  10. Model Quality Diagnostic Plots Template for Spotfire® using Spotfire® Data Science - Workbench

    This is a template for calculating various model quality diagnostic plots from probability input data. At the same time, it can serve as an example of the data function for Spotfire® built in Spotfire® Data Science - Workbench (also known as Statistica)
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  11. Anomaly Detection Template for Spotfire®

    This template detects anomalous data points using an autoencoder algorithm. It features automated machine learning to facilitate use by business analysts. The Time Series release includes time series analysis and clustering of anomalies.
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  12. AutoML in Spotfire® with Amazon SageMaker Autopilot

    This component enables Amazon SageMaker AutoPilot integration with Spotfire.
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  13. Customer Analytics Template for Spotfire®

    This Spotfire template enables the user to build product recommendations for personalized marketing. This template is intended for any industry providing customers with products and services such as retail, travel, or banking.
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  14. Statistical Process Control Template for Spotfire® using Spotfire® Data Science - Workbench

    This Spotfire template enables the user to build a wide range of Shewhart quality control charts. The chart specifications can be defined interactively or retrieved from a table. This comprehensive template is built with Statistica data function.
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  15. Monitoring and Alerting with Spotfire Statistica® using Spotfire®

    Leverage a copy of all MAS Statistica dashboards in Spotfire®.
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  16. Data Reader for Spotfire Statistica® Enterprise using Spotfire®

    This set of data functions enable you to open quickly and easily any dataset or workspace result from Statistica Enterprise metarepository in Spotfire.
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  17. Weibull Reliability and Optimal Maintenance with Spotfire®

    This component provides computation of Weibull distribution via TERR data function (for Spotfire).
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  18. NLP Python Toolkit for Spotfire®

    This Python toolkit contains natural language processing (NLP) functions that provide exploratory analysis of text data.
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  19. Model Explainability Python Toolkit for Spotfire®

    Explains model's predictions using LIME and SHAP algorithms.
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  20. Wafer Pattern Detection Dashboard for Spotfire®

    This dashboard helps you identify wafer map patterns that are interesting, helps to find more examples of those patterns, and gives you the ability to train accurate machine learning models to detect those patterns in future data.
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  21. Simple Probability Plot Analysis Template for Spotfire®

    Generate normal and lognormal probability plots
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  22. An AI Streaming Machine Learning Tutorial with Spotfire®

    Tutorial demonstrating how to put machine learning into production, but also aimed at business users who want to learn how to set up a machine learning strategy in their businesses. Spotfire Platform & Spotfire Cloud template versions are available.
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  23. Template for using XGBoost in Spotfire®

    Extreme Gradient Boosting or XGBoost is a supervised Machine-learning algorithm used to predict a target variable Y given a set of features - Xi
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  24. Model Evaluation Python Toolkit for Spotfire®

    Evaluate a model's performance using Scikit learn metrics.
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  25. Environmental Variable Display of TIBCO® Enterprise Runtime for R Data in TIBCO

    This template and data function return TERR environmental variables.
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