Chapter 6. Interactive Location-Based Intelligence
Just as most organizations now have a need to process at least some data in real time, they also have a growing desire to somehow integrate location into data analytics applications.
As more data becomes available from mobile sources like vehicles and smartphones, there are more opportunities to benefit from analyzing and visualizing the geospatial aspects of this data. But traditional geospatial mapping tools, which were designed primarily for creating static maps, are hardly up to the task.
Analyzing large datasets with any sort of interactivity requires overcoming two fundamental challenges: the lack of sufficient computational power in even today’s most powerful CPUs to handle large-scale geospatial analytics in anything near real time; and the inability of browsers to render the resulting points, lines and polygons in all but the simplest visualizations.
Given its roots in graphics processing, it should come as no surprise that the GPU is especially well-suited to processing geospatial algorithms on large datasets in real time, and rendering the results in map-based graphics that display almost instantly on ordinary browsers (see Figure 6-1). The GPU-accelerated database also makes it possible to ingest, analyze, and render results on a single platform, thereby eliminating the need to move data among different layers or technologies to get the desired results.
Figure 6-1. The GPU-accelerated database is ideally suited for the interactive location-based analytics that are becoming increasingly desirable
The massively parallel processing power of GPUs makes it possible to support both geospatial objects and operations in their native formats. The ability to perform geospatial operations, such as filtering by area, track, custom shapes, geometry, or other variables, directly on the database assures achieving the best possible performance. Support for geospatial objects, such as points, lines, polygons, tracks, vectors, and labels, in their standard formats also makes it easier to ingest raw data from and export results to other systems.
Standards are critical, as well, to ensuring a quality user experience when the results are rendered on browsers in various visualizations, including heatmaps, histograms, and scatter plots. Most graphical information system (GIS) databases support standards being advanced by the Open Geospatial Consortium, and a growing number of GPU databases now support these standards. OGC standards specify how GIS images are converted to common graphics formats, and also how the graphics are transported via standard web services software that can be incorporated directly into the GPU database.
This approach makes it easy to integrate data from major mapping providers, including Google, Bing, ESRI and MapBox, and facilitates the means for users to interact with the visualizations and change the way the results are displayed. With some solutions, users can now simply drag and drop analytical applets, data tables, and other “widgets” to create completely customized dashboards.
You can further extend geospatial analyses through user-defined functions (UDFs) that enable custom code to be executed directly on the GPU database. By bringing the analysis to the data, this approach eliminates the need to ever extract any data to a separate system.
These forms of customization open a world of possibilities, including using machine learning libraries such as TensorFlow for advanced geospatial predictions. Machine learning makes it possible, for example, to flag deliveries that are unlikely to arrive on time based on traffic, predict which drivers are most likely to be involved in an accident based on driving behavior, or calculate insurance risk for assets based on weather models.
The ability to interact with geospatial data in real time gives business analysts the power to make better decisions faster. With the breakthrough price and performance afforded by GPU databases, that ability is now within reach of almost every organization.