The business impact of Big Data is starting to take form in many fields, however, innovation is held back by limitations of current Big Data processing methods and infrastructures. Track&Know researched and developed a framework that aims to increase the efficiency of Big Data. A variety of toolboxes, containing specific methods & algorithms for various types of data aggregation, manipulation & further analysis were developed within the project. The Big Data Processing toolbox implements data acquisition technology that captures data from heterogeneous data sources, extending the current solutions for efficient access, indexing, partitioning and load balancing for Big spatiotemporal data. The Complex Event Recognition toolbox detects complex event occurrences by analyzing patterns in simple events by using contextual information and results from the Big Data Analytics toolbox. The toolbox may infer a complex event (such as dangerous driving) by analyzing patterns based on vehicle speed, direction, driver events, fuel consumption and contextual information (e.g weather).
The BDA toolbox analyzes heterogeneous data, drawing conclusions about spatiotemporal distribution of mobility patterns by applying scalable data mining techniques (such as clustering, sequence mining, hotspot analysis) for voluminous trajectory data. The Visual Analytics toolbox develops interactive and scalable methodologies to efficiently visualize heterogeneous spatiotemporal data of varying levels of resolution and quality. The toolboxes are included in the Big Mobility Data Integrator platform and tested against three pilot cases, organized with two common links: service optimization and driver behavior. Applied to the Insurance sector: (a) to gain in-depth and accurate crash probability estimation; (b) Electric Cars adoption by studying the cost-benefit of switching to electric vehicle, matching charging times and points to drivers’ habits; (c) Car Pooling opportunities by analyzing for reduced parking on sharable routes with a cost-benefit of switching to a sharing mobility paradigm, and likelihood of finding a proper sharable route that matches time and geographical zone. Within the Health Service: (a) improve the response times (increasing new patients, follow-up patients; (b) reduce unnecessary travel (reduce patient travel distances, courier costs, CO2 emissions); (c) generate Cost efficiency gains; (d) New methods of OSA diagnoses based on driver behavior. For Fleet Management, Track&Know’s Business objectives include Predictive maintenance, anomaly detection, reduction of false alarms, correlation of data with weather services, fleet costs reduction, fleet downtime reduction, fleet response time improvement, improve driver behavior and reduce accidents.
