Are you are using machine learning (ML) to support your business operations? If so, then you have the opportunity to increase the value you get from ML by incorporating geospatial information. But to get this additional value, the capability must avoid the need for excessive movement or duplication of geospatial data, which is often voluminous, as well as avoiding the need to code new algorithms.
To address this opportunity, we are excited to announce the upcoming enhancement of Oracle Machine Learning in Autonomous Database with spatial algorithms to incorporate location into your data science and machine learning-based solutions. This is one several AI and ML enhancements to Autonomous Database, as described here.
Why include spatial data and algorithms in ML?
The use of spatial data and algorithms in your ML workflows offers benefits for both detecting patterns and making predictions.
Patterns detected by Spatial algorithms include:
Pattern detection example: Incidents clustered according to thresholds for proximity and number of incidents. Incidents having same color are part of a cluster.
Predictions made by Spatial algorithms:
Prediction example: Maps show home value predictions residuals (predicted value - actual value). The lighter colors represent higher accuracy. The spatial model on the right shows generally better accuracy than the non-spatial model on the left. The bar chart shows a standard model quality score, with the spatial model showing better performance.
These are some of the aspects of how spatial data and algorithms enhance ML, and Oracle Machine Learning provides the ideal platform to enjoy these benefits.
New spatial enhancement in Oracle Machine Learning
Spatial data management and analysis features are built into every Oracle Database, including Autonomous Database. These spatial database features are referred to as Oracle Spatial. Machine Learning in Autonomous Database provides data scientists, data engineers, and developers with a complete environment to develop and operationalize models using familiar notebooks and languages, while behind the scenes work is performed by Autonomous Database. The spatial enhancement is coming to Oracle Machine Learning for Python (OML4Py) on Autonomous Database, so you can develop and operationalize geospatial-based predictive models at scale in Python via Oracle Machine Learning Notebooks while leveraging the native support of Oracle Spatial for spatial data management and analysis operations.
What will be included in the enhancement?
Spatial data is generally categorized as spatial vector data (point/line/polygon data such as address locations, utility lines, and trade areas) and spatial raster data (gridded data such as satellite imagery and elevation models). The first phase of this enhancement will include features to incorporate spatial point/line/polygon data into the ML lifecycle in Machine Learning for Python on Autonomous Database. The enhanced Python API is planned to include:
For both data science generalists and those with spatial expertise, this enhancement will provide a tremendous opportunity to make better predictions and discover more insights through the use of spatial data and algorithms backed by Autonomous Database.
Need more information?
You will be able to read more on this feature once it becomes available through Oracle Machine Learning Notebooks. In meanwhile:
David Lapp is a Senior Principal Product Manager at Oracle Corporation. His responsibilities include strategy and planning for Oracle's Spatial and Graph technologies and cloud services, and their use across the Oracle Cloud including machine learning and analytics. Prior to his current role in product management, David spent nearly 10 years in technical pre-sales covering analytics and spatial technology for the North American Public Sector. David is a graduate of the University of Washington.