SUCCESS STORIES– INDUSTRIAL ANALYTICS & BIG DATA

THE PROBLEM

Our client is an Argentinean company dedicated to providing geographic information services and AVL (Automatic Vehicle Location) systems. They operate a fleet of over 20,000 vehicles, including fleets from the production sector and public service agencies.

One of their clients, the ‘Police of the Province of Buenos Aires,’ had a monitoring system that updated the coverage and patrol status of vehicles every 24 hours, making it unable to make early decisions in case of deviations or incidents. The architectural approach of that solution was a classic data warehouse-based on SQL technologies.

Therefore, there was a need to provide a new system capable of real-time monitoring of patrol coverage, with the aim of optimizing decision-making regarding the geographical deployment of patrol vehicles.

THE SOLUTION

A platform was developed focused on processing data or message streams (stream processing) from embedded devices on patrol vehicles. In real-time, the positioning of the vehicles on maps residing in a graph-oriented database is determined using high-performance recursive algorithms. This allows the computation of patrol coverage indexes by zones and districts and the calculation of a series of indicators that provide the necessary statistics for further analysis.

The architecture of this system is centered around the concepts of Big Data and NoSQL persistence management in various forms. A server cluster was used to meet the processing requirements of the solution. Additionally, this architecture includes analytics components, running machine learning algorithms to detect abnormal conditions in patrol patterns.

Finally, the information is displayed on control dashboards, consisting of maps and indicators, allowing for real-time visualization of coverage status by district and for the entire province.

THE RESULTS

The system was successfully deployed into production without major issues, becoming a valuable tool that provides real-time visibility into the efficiency and effectiveness of mobile deployment strategies. This enables corrective actions to optimize crime prevention and law enforcement outcomes.