Transportation Intelligence
Logistics Machine Learning Data Visualization
The Logistics Machine Learning Data Visualization project uses the Logistics Machine Learning Intelligence subject area and allows selection of data via a filter and comparison of scenarios under this project via different analyses.
These projects are located in the catalog in Shared Folders/LML Data Visualisation
LML - Input Data
This project is located in the catalog in Shared Folders/LML Data Visualisation/LML- Input Data
If you are adding this project to an Enhanced Workbench, use the project path of "/@Catalog/shared/Custom/LML Data Visualisation/LML- Input Data"
Filters
The following filters appear at the top of the LML Input Data project and allow you to filter the data shown in the graphs and tables.
|
Filter |
Metadata Mapping |
|---|---|
|
Shipment Count |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Shipment Dimensions"."Shipment GID") |
|
Shipment GID |
"Logistics Machine Learning"."- Machine Learning Shipment Dimensions"."Shipment GID" |
|
Source Location GID |
"Logistics Machine Learning"."- Shipment Source Geography Dimensions"."Location GID" |
|
Location GID |
"Logistics Machine Learning"."- Shipment Destination Geography Dimensions"."Location GID" |
|
Actual Service Provider GID |
"Logistics Machine Learning"."- Shipment Carrier Dimensions"."Actual Service Provider GID" |
|
Project GID |
"Logistics Machine Learning"."- Machine Learning Shipment Dimensions"."Project GID" |
Planned Accuracy, Shipment Count by Lane Graph
This graph shows the planned accuracy and shipment count by lane.
|
Values |
Definition |
Metadata Mapping |
|---|---|---|
|
Y-Axis |
Planned Accuracy |
"Logistics Machine Learning"."- Machine Learning Shipment Facts"."Mean Absolute Percentage Error - Planned" |
|
Y-Axis |
Shipment Count |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Shipment Dimensions"."Shipment GID") |
|
X-Axis |
Lane |
CONCAT(CONCAT("Logistics Machine Learning"."- Shipment Source Geography Dimensions"."City",'-'),"Logistics Machine Learning"."- Shipment Destination Geography Dimensions"."City" ) |
Shipment Count, Planned Accuracy by Actual Service Provider Table
This table shows the following data:
|
Column |
Metadata Mapping |
|---|---|
|
Actual Service Provider Name |
"Logistics Machine Learning"."- Service Provider Dimensions"."Actual Service Provider Name" |
|
Actual Service Provider Type |
"Logistics Machine Learning"."- Service Provider Dimensions"."Actual Service Provider Type" |
|
Shipment Count |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Shipment Dimensions"."Shipment GID") |
|
Planned Accuracy |
1-"Logistics Machine Learning"."- Machine Learning Shipment Facts"."Mean Absolute Percentage Error - Planned" |
Shipment Count, Total Net Weight, and Volume by Source and Destination Graph
This graph showcases the shipment count, total net weight, and volume by source and destination cities.
|
Column |
Metadata Mapping |
|---|---|
|
City (Source) |
"Logistics Machine Learning"."- Shipment Source Geography Dimensions"."City" |
|
City (Destination) |
"Logistics Machine Learning"."- Shipment Destination Geography Dimensions"."City" |
|
Total Net Weight |
"Logistics Machine Learning"."- Machine Learning Shipment Facts"."Total Net Weight" |
|
Shipment Count |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Shipment Dimensions"."Shipment GID") |
|
Total Net Volume |
"Logistics Machine Learning"."- Machine Learning Shipment Facts"."Total Net Volume" |
LML Scenario Training Results
This project are located in the catalog in Shared Folders/LML Data Visualisation/LML Scenario Training Results
If you are adding this project to an Enhanced Workbench, use the project path of "/@Catalog/shared/Custom/LML Data Visualisation/LML Scenario Training Results"
Filters
The following filters appear at the top of the LML Scenario Training Results project and allow you to filter the data shown in the graphs and tables.
|
Filter |
Metadata Mapping |
|---|---|
|
Scenario GID |
"Logistics Machine Learning"."- Machine Learning Scenario Dimensions"."Scenario GID" |
|
Accuracy |
"Logistics Machine Learning"."- Machine Learning Scenario Level Training Facts"."Accuracy" |
| Model ID |
"Logistics Machine Learning"."- Machine Learning Scenario Dimensions"."Model ID" |
| Learning Request ID |
"Logistics Machine Learning"."- Machine Learning Scenario Dimensions"."Learning Request ID" |
| Project GID |
"Logistics Machine Learning"."- Machine Learning Scenario Dimensions"."Project GID" |
Accuracy by Scenario GID Graph
This graph shows the accuracy by scenario GID.
|
Column |
Definition |
Metadata Mapping |
|---|---|---|
|
X-Axis |
Accuracy |
"aggregate("Logistics Machine Learning"."- Machine Learning Scenario Level Training Facts"."Accuracy" by "Logistics Machine Learning"."- Machine Learning Scenario Dimensions"."Scenario GID")" |
|
Y-Axis |
Scenario GID |
"Logistics Machine Learning"."- Machine Learning Scenario Dimensions"."Scenario GID" |
|
Confidence High Interval |
NA |
"Logistics Machine Learning"."- Machine Learning Scenario Level Training Facts"."Confidence Interval High" |
|
Confidence Low Interval |
NA |
"Logistics Machine Learning"."- Machine Learning Scenario Level Training Facts"."Confidence Interval Low" |
Percentage Contribution by Feature Name Pie Chart
This pie chart shows the percentage contribution by feature name.
|
Column |
Metadata Mapping |
|---|---|
|
Percentage Contribution |
"Logistics Machine Learning"."- Machine Learning Feature Contribution Facts"."Percentage Contribution" |
|
Feature Name |
"Logistics Machine Learning"."- Machine Learning Feature Contribution Dimensions"."Feature Name" |
LML - Shipment Training Results
This project is located in the catalog in Shared Folders/LML Data Visualisation/LML - Shipment Training Results
If you are adding this project to an Enhanced Workbench, use the project path of "/@Catalog/shared/Custom/LML Data Visualisation/LML - Shipment Training Results"
Filters
The following filters appear at the top of the LML Shipment Training Results project and allow you to filter the data shown in the graphs and tables.
|
Filter |
Type |
Metadata Mapping |
|---|---|---|
|
Shipment Count |
Top Bottom N |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Shipment GID") |
|
Mean Absolute Percentage Error - Predicated |
Range |
"Logistics Machine Learning"."- Machine Learning Shipment Level Training Facts"."Mean Absolute Percentage Error - Predicted" |
|
Actual Service Provider Name |
List |
"Logistics Machine Learning"."- Training Shipment Carrier Dimensions"."Actual Service Provider Name" |
|
Is Train |
List |
"Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Is Train" |
|
Project GID |
List |
"Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Project GID" |
|
Scenario GID |
List |
"Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Scenario GID" |
|
Shipment GID |
List |
"Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Shipment GID" |
Prediction Accuracy, Planned Accuracy by Lane Graph
This graph shows the prediction accuracy and planned accuracy by lane.
|
Column |
Definition |
Metadata Mapping |
|---|---|---|
|
Y-Axis |
Prediction Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Shipment Level Training Facts"."Mean Absolute Percentage Error - Predicted" |
|
Y-Axis |
Planned Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Shipment Level Training Facts"."Mean Absolute Percentage Error - Planned" |
|
X-Axis |
Lane |
CONCAT(CONCAT("Logistics Machine Learning"."- Training Shipment Source Geography Dimensions"."City",'-'),"Logistics Machine Learning"."- Training Shipment Destination Geography Dimensions"."City") |
Prediction Accuracy, Planned Accuracy, Shipment Count by Actual Service Provider Name Table
This table shows the following data:
|
Column |
Metadata Mapping |
|---|---|
|
Actual Service Provider Name |
"Logistics Machine Learning"."- Training Shipment Carrier Dimensions"."Actual Service Provider Name" |
|
Prediction Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Shipment Level Training Facts"."Mean Absolute Percentage Error - Predicted" |
|
Planned Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Shipment Level Training Facts"."Mean Absolute Percentage Error - Planned" |
|
Shipment Count |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Shipment GID") |
Prediction Accuracy, Planned Accuracy, Shipment Count by Is Train, Scenario GID Table
This table shows the following data:
|
Column |
Metadata Mapping |
|---|---|
|
Scenario GID |
"Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Scenario GID" |
|
Is Train |
"Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Is Train" |
|
Prediction Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Shipment Level Training Facts"."Mean Absolute Percentage Error - Predicted" |
|
Planned Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Shipment Level Training Facts"."Mean Absolute Percentage Error - Planned" |
|
Shipment Count |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Training Results Dimensions"."Shipment GID") |
LML - Shipment Prediction Results
This project is located in the catalog in Shared Folders/LML Data Visualisation/LML - Shipment Prediction Results
If you are adding this project to an Enhanced Workbench, use the project path of "/@Catalog/shared/Custom/LML Data Visualisation/LML - Shipment Prediction Results"
Filters
The following filters appear at the top of the LML Shipment Training Results project and allow you to filter the data shown in the graphs and tables.
|
Filter |
Type |
Metadata Mapping |
|---|---|---|
|
Mean Absolute Percentage Error - Predicated |
Range |
"Logistics Machine Learning"."- Machine Learning Prediction Results Facts"."Mean Absolute Percentage Error - Predicted" |
|
Shipment Count |
Top Bottom N |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Shipment GID") |
|
Actual Service Provider Name |
List |
"Logistics Machine Learning"."- Prediction Shipment Carrier Dimensions"."Actual Service Provider Name" |
|
Scenario GID |
List |
"Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Scenario GID" |
|
Shipment GID |
List |
"Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Shipment GID" |
|
Source Location GID |
List |
"Logistics Machine Learning"."- Prediction Shipment Source Geography Dimensions"."Location GID" |
|
Location GID |
List |
"Logistics Machine Learning"."- Prediction Shipment Destination Geography Dimensions"."Location GID" |
|
Project GID |
List |
"Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Project GID" |
Prediction Accuracy, Planned Accuracy by Lane Graph
This graph shows the prediction accuracy and planned accuracy by lane.
|
Column |
Definition |
Metadata Mapping |
|---|---|---|
|
Y-Axis |
Prediction Accuracy |
1- "Logistics Machine Learning"."- Machine Learning Prediction Results Facts"."Mean Absolute Percentage Error - Predicted" |
|
Y-Axis |
Planned Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Prediction Results Facts"."Mean Absolute Percentage Error - Planned" |
|
X-Axis |
Lane |
CONCAT(CONCAT("Logistics Machine Learning"."- Prediction Shipment Source Geography Dimensions"."City",'-'),"Logistics Machine Learning"."- Prediction Shipment Destination Geography Dimensions"."City") |
|
Shipment Count |
NA |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Shipment GID") |
Prediction Accuracy, Planned Accuracy, Shipment Count by Actual Service Provider Type and Name Table
This table shows the following data:
|
Column |
Metadata Mapping |
|---|---|
|
Actual Service Provider Name |
"Logistics Machine Learning"."- Prediction Shipment Carrier Dimensions"."Actual Service Provider Name" |
|
Actual Service Provider Type |
"Logistics Machine Learning"."- Prediction Shipment Carrier Dimensions"."Actual Service Provider Type" |
|
Prediction Accuracy |
1- "Logistics Machine Learning"."- Machine Learning Prediction Results Facts"."Mean Absolute Percentage Error - Predicted" |
|
Planned Accuracy |
1 - "Logistics Machine Learning"."- Machine Learning Prediction Results Facts"."Mean Absolute Percentage Error - Planned" |
|
Shipment Count |
COUNT(DISTINCT "Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Shipment GID") |
Accuracy by Project GID, Scenario GID Table
This table shows the following data:
|
Column |
Metadata Mapping |
|---|---|
|
Scenario GID |
"Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Scenario GID" |
|
Project GID |
"Logistics Machine Learning"."- Machine Learning Prediction Results Dimensions"."Project GID" |
|
Accuracy |
"Logistics Machine Learning"."- Machine Learning Prediction Results Facts"."Accuracy" |
Related Topics
About Logistics Machine Learning Intelligence
Transportation Intelligence Data Visualization
Logistics Network Modeling Data Visualization
Chapter 3. Oracle Analytics Server
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