machine learning

Combining Human Expertise and Machine Learning for Intelligent Transportation Resource Management

Typically, logistics networks generate billions of new data items on a monthly basis that can be used to help predict potential customer behaviour. Lorries transport thousands of pallets on hundreds of trailers for millions of customers scattered across hundreds of thousands of postcodes. Customers are continuously placing orders with information being generated about what they want to transport, where it will be coming from, where it will be going and what their service requirements are.

CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications

The Internet of Things (IoT) is a growing field which is expected to generate and collect data everywhere at any time. Highly scalable cloud analytics systems are frequently being used to handle this data explosion. However, the ubiquitous nature of the IoT data imposes new technical and non-technical requirements which are difficult to address with a cloud deployment. To solve these problems, we need a new set of development technologies such as Distributed Data Mining and Ubiquitous Data Mining targeted and optimized towards IoT applications.