Among the steps to follow in the process of extracting unknown knowledge out of large databases, data mining is a central element. Techniques to automate the extraction of repeating patterns were initially developed for centralised data. As industry and science increasing rely on geographically dispersed computing resources, methods for distributed data mining started to emerge. In fact, numerous solutions are available using techniques such as distributed clustering, classification and regression. However, only a few of them rely on intelligent agents that can control a growing number of data mining tasks. The EU-funded project 'Agent-oriented distributed data mining using computational statistics' (ADMIT) explored the added value of concepts borrowed from agent technology. In multi-agent systems, the individual and collective behaviours of agents depend on the observed data. ADMIT researchers considered decentralised data processing techniques, including regression forecasting and change-point analysis to determine if and when a change in a data set has occurred. These data coordination models were applied for decision making and achieved similar performance as with a central authority. Τhe synergy between such communities of agents and cloud computing offered additional perspectives for new technologies. ADMIT researchers examined decentralised data clustering that is an important data pre-processing step in cloud data repositories. By grouping similar data together, it was possible to construct more accurate data representatives for application such as optimal route selection and speed adaptation in traffic. ADMIT's many results were presented at 11 international conferences. Sixteen papers were also published in conference proceedings and peer-reviewed scientific journals. More importantly, the proposed methods have been integrated into intelligent transportation systems for traffic management and environment monitoring, and validated using real-world traffic data from the German city of Hannover.
Data mining, software agents, cloud computing, data clustering, intelligent transport