This document will continue to evolve as existing sections change and new information is added. All updates appear in the following table:
| Date | Product | Feature | Notes |
|---|---|---|---|
| 01 SEP 2020 | Created initial document. |
This guide outlines the information you need to know about new or improved functionality in this update.
DISCLAIMER
The information contained in this document may include statements about Oracle’s product development plans. Many factors can materially affect Oracle’s product development plans and the nature and timing of future product releases. Accordingly, this Information is provided to you solely for information only, is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described remains at the sole discretion of Oracle.
This information may not be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. Oracle specifically disclaims any liability with respect to this information. Refer to the Legal Notices and Terms of Use for further information.
Fine-Tune Data Storage and Management
The Data Storage page has been enhanced, so that you can better manage data storage and retention settings for your application.
The new System Metric Data section lets you fine-tune the settings for retaining system metric data. System metrics are inbuilt metrics (KPIs) that are different from the custom metrics that you create in the application.
You can also choose the lifespan for device data, such as application messages, connector messages, and log messages. Sensor data is now included under Training Data.
This document will continue to evolve as existing sections change and new information is added. All updates appear in the following table:
| Date | Product | Feature | Notes |
|---|---|---|---|
| 09 JUL 2020 | Created initial document. |
This guide outlines the information you need to know about new or improved functionality in this update.
DISCLAIMER
The information contained in this document may include statements about Oracle’s product development plans. Many factors can materially affect Oracle’s product development plans and the nature and timing of future product releases. Accordingly, this Information is provided to you solely for information only, is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described remains at the sole discretion of Oracle.
This information may not be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. Oracle specifically disclaims any liability with respect to this information. Refer to the Legal Notices and Terms of Use for further information.
PMML Data Engineering Support for Predictions
If you wish to use a pre-trained prediction model in place of the automatic prediction training in Oracle IoT Asset Monitoring Cloud Service, you can upload the trained model in Oracle IoT Asset Monitoring Cloud Service to create a prediction. Oracle IoT Asset Monitoring Cloud Service performs the prediction scoring using your pre-trained model.
You can use training models supported by pmml4s (PMML Scoring Library for Scala), such as the neural network. When creating a new prediction, upload your PMML file to replace the built-in models used by Oracle IoT Asset Monitoring Cloud Service.
Use advanced analytics in Oracle IoT Asset Monitoring Cloud Service to forecast product demand for new products in Oracle Demand Management Cloud. Oracle IoT Asset Monitoring Cloud Service employs feature-based machine learning on historical product sales data to come up with insights and forecast recommendations for new products.
Oracle Demand Management Cloud provides the required input data through Oracle Object Storage using BICC (Oracle Business Intelligence Cloud Connector). Oracle IoT Asset Monitoring Cloud Service creates training models on the ingested data and performs scoring to create on-demand forecasts for Oracle Demand Management Cloud.