Overview on Rulex Tasks

Tasks are the most elementary units in Rulex. Each task, apart from import tasks, receives data from its parent task and creates outputs (processed data, models,...), which in turn are provided to its child tasks. This is the concept of dataflow: each task propagates all the received tables, modifying those related to its elaboration.

Rulex tasks can complete entire functions, not just single operations. For example, the Data Manager task provides all data exploration operations, such as sorting, filtering, grouping, computing statistics, applying formulas, creating plots, etc. Everything you need to truly understand the data you have in hand. As all these operations are in one task you can perform many operations quickly, through rapid trial and error, and avoid lengthy computations of multiple tasks.

Double clicking on a task in the Rulex stage allows you to customize it through a set of configurable options (in the graphical interface and via parametric options). The options depend on the selected task.
Right-click any task to manage it with the following operations:

Command	Description

Command	Description
Compute	Computes the tasks in different ways according to the option chosen. See Saving and Computing Tasks for details.
Reset	Resets the task in one of two ways: soft: resets the task history and status. Any previously computed data are preserved until a new computation is issued. hard: resets the task history and status and erases any data associated with the task.
Edit Priority	Allows you to modify priority settings for the specific task. See Prioritizing Tasks for details.
Take a look	Allows you to view the data related to a task, and perform all the tasks provided by the Data Manager task, in read-only mode, so you do not need to add a Data Manager task to the process to view its contents.
Link to	Connects the task another selected task in the process. After selecting Link to, simply select the second task to which you want to connect.
Create module	Compacts all the selected tasks into a single module task, For more details see Compacting Tasks into Modules
Open	Opens the selected task in the process. The same operation can be performed by double-clicking the task.
Rename	Opens an edit box where you can rename the task. Providing user-friendly names for tasks, which identify their contents or function, makes the process easier to understand.
Delete	Deletes the selected task and any connectors.
Copy & Paste	Copies and pastes the task to another part of the process. This operation copies the configuration options made in the task, but not the data it contains.

Task Categories

Each task, uniquely identified by an alphanumeric string, belongs to a specific category that identifies the action to be performed on the data:

Category	Contains	Tasks

Category	Contains	Tasks
Import	Tools to import datasets, structures and processes.	Conditional Import Empty Source Import from Database Import from Excel File Import from Json File Import from Task Import from Text File Import from Xml File Rulex Process File Source
Export	Tools to export datasets.	Export to Database Export to Excel File Export to Json File Export to Text File Export to XML
Visualization/Editing	Tools to visualize data and perform basic statistical analysis.	Association Manager Data Manager Itemsets/Sequences Manager Rule Manager
Pre-Processing	Techniques for elaborating the dataset, merging tables and changing the format of data.	Concatenate Discretize Fill/Clean Join Moving Window Reshape To Long Reshape To Wide Transpose Principal Component Analysis
Classification	Methods to induce the classification of targeted samples.	Decision Tree K-Nearest Neighbor Logic Learning Machine Logistic Neural Networks SVM
One-Class	Algorithms for unsupervised anomaly detection and rules discovery.	One-Class Logic Learning Machine
Regression	Algorithms for obtaining a multivariate functional relationship among ordered variables.	Auto Regressive K-Nearest Neighbor Linear Logic Learning Machine Neural Networks Regression SVM Regression Tree
Optimization	Tasks for solving problems related to optimizing a situation, by maximizing or minimizing specific values or optimizing a distribution network.	Mixed Integer Linear Programming Network Optimizer /wiki/spaces/DP/pages/1529254925
Clustering	Tools for determining the partition in groups of untargeted samples.	Label Clustering (K-means) Projection Clustering (K-means) Standard Clustering (K-means)
Association Rules	Algorithms for extracting frequent groups of itemsets and defining the corresponding association rules.	Anomaly Detection Assortment Optimizer Frequent Itemsets Mining Hierarchical Basket Analysis Sequence Analysis Similar Items Detector
Evaluation	Methods for evaluating the results of previous analysis.	Confusion Matrix Feature Ranking Rule Viewer
General	General-purpose tasks.	Apply Model Convert Dataset to Model Convert Dataset to Ruleset Converting Datasets to Structures Convert Model to Dataset Convert Ruleset to Dataset Converting Structures to Datasets Execute Rulex Process File Export Current Process Merge Rules Optimize Ruleset R Bridge Runtime Variables Select Flows Split Data
Control	Tests and optimizes rules.	Rule Based Control
Bridge	Tasks to enable communication with external scripts.	Import Data from Python Scripts Apply Python Scripts in Rulex Processes Import Data from R Scripts Apply R Scripts in Rulex Processes