How US Homeland Security plans to use knowledge graphs in its border patrol efforts
During this summer’s Data Centric Architecture Forum, Ryan Riccucci, Division Chief for U.S. Border Patrol – Tucson (AZ) Sector, and his colleague Eugene Yockey gave a glimpse of what the data environment is like within the US Department of Homeland Security (DHS), as well as how transforming that data environment has been evolving.
The DHS celebrated its 20-year anniversary recently. The Federal department’s data challenges are substantial, considering the need to collect, store, retrieve and manage information associated with 500,000 daily border crossings, 160,000 vehicles, and $8 billion in imported goods processed daily by 65,000 personnel.
Riccucci is leading an ontology development effort within the Customs and Border Patrol (CBP) agency and the Department of Homeland Security more generally to support scalable, enterprise-wide data integration and knowledge sharing. It’s significant to note that a Division Chief has tackled the organization’s data integration challenge. Riccucci doesn’t let leading-edge, transformational technology and fundamental data architecture change intimidate him.
Riccucci described a typical use case for the transformed, integrated data sharing environment that DHS and its predecessor organizations have envisioned for decades.
The CBP has various sensor nets that monitor air traffic close to or crossing the borders between Mexico and the US, and Canada and the US. One such challenge on the Mexican border is Fentanyl smuggling into the US via drones. Fentanyl can be 50 times as powerful as morphine. Fentanyl overdoses caused 110,000 deaths in the US in 2022.
On the border with Canada, a major concern is gun smuggling via drone from the US. to Canada. Though legal in the US, Glock pistols, for instance, are illegal and in high demand in Canada.
The challenge in either case is to intercept the smugglers retrieving the drug or weapon drops while they are in the act. Drones may only be active for seven to 15 minutes at a time, so the opportunity window to detect and respond effectively is a narrow one.
Field agents ideally need to see enough visual real-time, mapped airspace information on the sensor activated, allowing them to move quickly and directly to the location. Specifics are important; verbally relayed information by contrast can often be less specific, causing confusion or misunderstanding.
The CBP’s successful proof of concept involved a basic Resource Description Framework (RDF) triple, semantic capabilities with just this kind of information:
Sensor → Act of sensing → drone (SUAS, SUAV, vehicle, etc.)
In a recent test scenario, CBP collected 17,000 records that met specified time/space requirements for a qualified drone interdiction over a 30-day period.
The overall impression that Riccucci and Yockey conveyed was that DHS has both the budget and the commitment to tackle this and many other use cases using a transformed data-centric architecture. By capturing information within an interoperability format, the DHS has been apprehending the bad guys with greater frequency and precision.
Contributed by Alan Morrison
