Inside The SRS Spatial Finance Knowledge Graph
A global map of complex data relationships connecting dozens of data sets
into a highly precise and unified data structure – the knowledge graph.
Extracting value out of the vast amounts of open data available today is difficult and expensive. One of the main reasons is that open data sets do not connect easily with each other nor with the data sets owned by the user. To connect these datasets one needs to understand the various data identification schemas used by the government agencies and industry bodies that publish open data and figure out how to interconnect entities and locations of interest to the information available in open datasets. In addition, given the ever-changing nature of the world, maintaining the interconnects between data sources is a continuous effort of change monitoring and adjustments. These problems make the construction and maintaining of AI-ready databases expensive and time-consuming.
The SRS Knowledge Graph enables location Intelligence and offers a service that will accelerate and dramatically decrease the cost of AI-ready database construction by providing ONE SOURCE for accurate and consistently maintained inter-connects between physical locations and the information stored in the open data sets.
The SRS Knowledge Graph’s consistent classification schema, support for hierarchical relationships and bi-temporal (Point In Time) data increases transparency and confidence in analysis.
SRS Accelerates Time to Value
Contextualized search provides focused results...
Auto Manufacturing Worldwide
SRS Knowledge Graph
Core Product Principles
For data to be considered in an AI ready state. The following ‘data’ conditions need to be in place.
All the individual data points need to be directly connected or indirectly connected to one another.
Data must be high quality. ‘Dirty’ data will invalidate ML/AI results every time.
A consistent approach to classifying and relating data points. ML/AI does not like inconsistent or multiple data schemas.
Hierarchies play a critical factor in mapping data relationships, data networks, and building decision trees. ML/AI ‘shines’ when hierarchical relationships are fully supported.
Knowledge Graphs support simple methods for data aggregation and data drill down.
Point in Time (PIT)
All nodes or entities in a Knowledge Graph can support attribute values over time.
Data connectivity greatly expands and enhances data visualization and data reporting.
The ability to provide multiple ways to easily connect to other third party and open data sources.
Single Data Catalog
Simple way to drive data discovery and data access.
Inside the PPP
View a detailed SRS Report on the Paycheck Protection Program.