One of the biggest challenges in law enforcement is the (in)accessibility of law enforcement data. In the performance of their duties, police officers use a number of disparate information systems. Commonly, the objective is to reveal the identity of an individual, whether victim, witness, or suspect. Because the systems are often siloed, disparate, legacy holdovers, and contain various types of data, officers must send multiple types of queries and await the responses—which based on their access rights and privileges, they may or may not get. This delays or even prevents them from achieving the main objective of revealing identities to solve crimes.
The Challenges of Law Enforcement Systems
Throughout their investigations, officers encounter six main issues in using law enforcement systems:
- they are forced to spend a great deal of valuable time querying each system separately, because for operational as well as legal and administrative reasons, law enforcement information systems are usually implemented in isolated silos.
- they miss crucial connections between identities. Because systems are siloed, each may establish a different identity for one and the same person. For example, if a single individual is a foreign resident, a traveler, a car owner, and the perpetrator of a crime, that person will have four possible encounters in the systems of Residency, Passenger Information, Vehicle Registration, and Criminal AFIS respectively, without any link between the identities: it is as if they were four different persons.
- they face restrictions when they search, because many databases process personal & sensitive data, subjecting them to access rules and user privileges, as well as regulations regarding data privacy, such as GDPR.
- they cannot seamlessly exchange information between the systems they use, as these contain different databases and interfaces developed by different vendors. Information exchange is only possible if it is performed according to specific standards or if the data is transformed.
- they cannot compare identities on an ‘apples to apples’ basis, because law enforcement information systems process and contain different, disparate types of data, such as biometric data, biographic/demographic data, and metadata.
- they lack the means to process and search big data. Many cities have thousands of surveillance cameras that create huge amounts of hard-to-search, hard-to-link-together video and face metadata.
Data Models and Formats
Making different and disparate systems interoperable is a complex undertaking. One data model in particular could form the backbone of interoperability solutions for law enforcement systems: POLE, short for People, Objects, Locations, and Events.
POLE was specifically designed to optimize these systems and solve their security and safety challenges. All the different types of data processed in law enforcement systems can be organized under the four categories of this model. POLE defines persons, objects, locations, and events in different pillars, which can be connected or separate. A similar standard is the UMF3 or Universal Message Format. Using such a standard and data model, an interoperability solution can easily present a set of clear search results after searching multiple disparate and distinct databases and systems and assessing and consolidating the results.
HORUS: WCC's Solution for Interoperability and SSI
HORUS is WCC’s solution for achieving interoperability. It offers a single search interface to resolve the complexities of siloed law enforcement information systems, supporting officers with maximum search efficiency across the different databases and systems while respecting access rules and authorization. HORUS allows officers to submit simultaneous queries to different systems, and to consolidate the results or create links between different encounters of the same identity contained in different systems or databases.
The design of HORUS is based on the POLE data model. This means it can search and link across various types of identity data, such as biographic, biometric, and metadata. HORUS is vendor-independent: it can search and link different types and structures of information regardless of the biometric or database vendor. It can perform either in synchronous or asynchronous mode.
HORUS checks the type of data, decides which systems should be searched and whether iterative searches are required, distributes queries simultaneously to the relevant systems, consolidates or matches results against each other, and applies complex business rules.
In short, HORUS allows officers to search and link identities more successfully and efficiently in order to identify people and solve crimes.
Below is just one example, in which an officer enters a license plate number and HORUS gets results not just from the Vehicle Registration Database, but also sends these initial results to other systems to uncover more data pertaining to the same identity, displaying the consolidated result set to the officer.