• Release Notes Rulex 4
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  • Rulex Development Manual
    • Prerequisites
    • Getting to Know Rulex
      • Rulex Development Environment
        • Database Settings
        • Stage Settings
        • Command and Toolbar functions
        • Datasets and Attributes
      • Overview on Rulex Tasks
        • Saving and Computing Tasks
        • Selecting Attributes in Tasks
        • Analyzing Task Performance
        • Searching Tasks
      • How is the Manual Structured?
    • Creating Processes
      • Versioning Processes
      • Creating a Process from Scratch
      • Checking the Validity of Processes with the Workflow Review Tool
        • Creating an Excel Validation File
        • Mapping Tasks to Categories
    • Importing Data into a Process
      • Importing Data from Text Files
      • Importing Data from Json Files
      • Importing Data from Microsoft Excel
      • Importing Data from a Database
      • Importing Data from an XML File
      • Using Remote Connections
      • Conditionally Importing Data from Databases
      • Creating Datasets from Scratch
    • Exploring and Structuring Data
      • Reshaping, Transforming and Cleaning Datasets
        • Cleaning Datasets
        • Discretizing Data
        • Identifying and Managing Outliers
        • Reshaping Datasets to Wide Format
        • Reshaping Datasets to Long Format
        • Transposing Data
        • Performing Moving Windows Statistics on Data
      • Splitting Data into Training and Test Sets
        • Splitting Data with the Data Manager
        • Splitting Data with the Split Data Task
      • Exporting Data from a Process
        • Exporting Data to a Database
        • Exporting Data to an Excel File
        • Exporting Data to a Text File
        • Exporting Data to an XML File
        • Exporting Data from the Data Manager
        • Exporting Data to a Json File
      • Plotting Data in the Data Manager
        • Plotting Area Plots
        • Plotting Lorenz Curves
        • Plotting Q-Q Plots
        • Plotting P-P Plots
        • Merging Plots
        • Using Plots to Create What-If Scenarios
        • Plotting Stacked Bar Plots
        • Plotting Pie Charts
        • Plotting Curves
        • Plotting Scatters
        • Plotting ROC Curves
        • Viewing Multiple Plots
        • Plotting Heat Maps
        • Plotting Grouped Bar Plots
        • Plotting Box Plots
        • Customizing Plots
      • Data Manager Layout
      • Merging Datasets
        • Concatenating Datasets
        • Joining Datasets
      • Computing Statistics in the Data Manager
        • Computing Statistics - ROC curve
        • Computing Statistics - Values, Frequencies and Quantiles
        • Computing Statistics - Single Statistics
        • Computing Statistics - Cross Tabulation Statistics
        • Computing Statistics - Correlation and Covariance
        • Computing Statistics - Test for Independent Samples
        • Computing Statistics - Test for Paired Samples
      • Computing Formulas in the Data Manager
        • Common Operators in Data Manager Formulas
        • Examples of Formulas in Data Manager
        • Rulex Language Functions
        • Combining Formulas and Queries in Data Manager
      • Querying Data in the Data Manager
        • Filtering Data in the Query Manager
        • Grouping Data in the Query Manager
        • Applying Operations on Data in the Query Manager
        • Sorting Data in the Query Manager
      • Tracking Operations in the Data Manager
      • Managing Attributes in Data Manager
        • Managing Attribute Values
        • Managing Attribute Properties
      • Overview of Data Exploration in the Data Manager
      • Using External Scripts in Rulex
        • Applying R Scripts in Rulex Processes
        • Applying Python Scripts in Rulex Processes
        • Importing Data from R Scripts
        • Importing Data from Python Scripts
      • Identifying the Principal Components in Datasets with PCA
    • Solving your Problem
      • Solving Supervised Learning Problems
        • Using SVM to Solve Classification Problems
        • Using Decision Tree to Solve Classification Problems
        • Using Auto Regressive to Solve Regression Problems
        • Using K-Nearest Neighbor to Solve Regression Problems
        • Using K-Nearest Neighbor to Solve Classification Problems
        • Using Neural Networks to Solve Classification Problems
        • Using Neural Networks to Solve Regression Problems
        • Using Regression Tree to Solve Regression Problems
        • Using Regression SVM to Solve Regression Problems
        • Using Linear to Solve Regression Problems
        • Using LLM to Solve One-Class Problems
        • Using LLM to Solve Classification Problems
        • Using LLM to Solve Regression Problems
        • Using Logistic to Solve Classification Problems
      • Solving Unsupervised Learning Problems
        • Solving Association Problems in Rulex
          • Using Anomaly Detection to Solve Association Problems
          • Using Hierarchical Basket Analysis to Solve Association Problems
          • Using Frequent Itemsets Mining to Solve Association Problems
          • Using Sequence Analysis to Solve Association Problems
          • Using Assortment Optimizer to Solve Association Problems
          • Using Similar Items Detector to Solve Association Problems
        • Solving Clustering Problems
          • Using Label Clustering to Cluster Data
          • Using Projection Clustering to Cluster Data
          • Using Standard Clustering to Cluster Data
      • Solving Optimization Problems
        • Using Network Optimizer to Solve Optimization Problems
        • Using Mixed Integer Linear Programming to Solve Optimization Problems
    • Working with the Results
      • Converting Structures
        • Converting Rulesets to Datasets
        • Converting Datasets to Structures
        • Converting Models to Datasets
        • Converting Structures to Datasets
        • Converting Datasets to Models
        • Converting Datasets to Rulesets
      • Analyzing and Optimizing Results
        • Analyzing Rules in the Rule Manager
        • Adding Rules Manually in the Rule Manager
        • Analyzing Frequent Itemsets and Sequences
        • Optimizing Association and Replacement Rules
        • Ranking Rule Features and Values
        • Analyzing Model Performance in the Confusion Matrix
        • Analyzing Classification LLM Rules in the Rule Viewer
        • Merging Rules
        • Applying Models to Data
        • Finding and Replacing Values in Datasets
        • Optimizing Rulesets
    • Importing and Exporting Processes
      • Importing Structures with Import from Task
      • Exporting Rulex Process
        • Exporting Processes by Command
        • Exporting Processes by Task
      • Selecting Parts of a Process with Select Flow
      • Importing Rulex Processes
    • Compacting Tasks into Modules
      • Extracting Modules
      • Configuring Modules without Data Sources
      • Creating Modules without Data Sources
      • Creating Modules with Data Sources
      • Configuring Modules with Data Sources
    • Refining and Improving Processes
      • Using workflow variables in Rulex
        • Creating Process Variables
        • Configuring Runtime Variables
        • Connecting to Vault Variables
      • Managing Parametric Options
      • Setting Alerts for Tasks
      • Documenting Processes
      • Prioritizing Tasks
      • Creating and Using Macros
      • Improving the Appearance of Processes
    • Executing Rulex Processes
      • Scheduling Activities
      • Modifying Process Execution Parameters
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Creating Processes

Rulex processes contain all the tasks required to analyze data.

They are made up of a series of concatenated tasks, and can be exported and imported as PRCX files.

You can:

  • start working on a process from scratch

  • import a process and continue working on it

  • clone a process through the Rulex versioning tool.

If you're ready to start learning how to use Rulex sign up to Rulex Academy


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