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Knowledge

Spatial Risk Systems

Graph

Billions of
Expanding

Connections

Reality

#Woobler #Knowledge Graph 49 59 View Detailed Data

Open Datasets - Unified Taxonomy

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

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.

SRS frees data scientists and analysts from tedious and time consuming tasks and lets them focus on higher value analytical work.

Location Intelligence

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. 

WHY Spatial Risk Systems

SRS Accelerates Time to Value

WHY Spatial Risk Systems

Contextualized search provides focused results...

BUILDING The GRAPH

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.

1

Data Connectivity

All the individual data points need to be directly connected or indirectly connected to one another.

2

Data Precision

Data must be high quality. ‘Dirty’ data will invalidate ML/AI results every time.

3

Standardized Taxonomies

A consistent approach to classifying and relating data points. ML/AI does not like inconsistent or multiple data schemas.

4

Hierarchcal Relationships

Hierarchies play a critical factor in mapping data relationships, data networks, and building decision trees. ML/AI ‘shines’ when hierarchical relationships are fully supported.

5

Data Aggregation

Knowledge Graphs support simple methods for data aggregation and data drill down.

6

Point in Time (PIT)

All nodes or entities in a Knowledge Graph can support attribute values over time.

7

Data Visualization

Data connectivity greatly expands and enhances data visualization and data reporting.

8

Data Integration

The ability to provide multiple ways to easily connect to other third party and open data sources.

9

Single Data Catalog

Simple way to drive data discovery and data access.

View the data

Inside the PPP

View a detailed SRS Report on the Paycheck Protection Program.

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    sales.support@spatialrisksystems.com
    data.support@spatialrisksystems.com