Efraimidou, MelinaKanaki, MariaAthanasiadis, IoannisMitkas, PericlesKaratzas, KostasTochtermann, KlausScharl, Arno2019-09-162019-09-162006https://dl.gi.de/handle/20.500.12116/27535Urban air quality management and information systems are required to include advanced capabilities of quick, effective and easy to operate environmental data analysis applications, for information extraction and analysis and for the support of decision making. These systems are based on the need of city authorities and national governments to establish a framework which enables them to take actions, in order to ensure that air quality is improved and relevant standards are maintained in urban areas. In this context, quantitative data-driven decision support models are challenged by the difficulties in handling dynamic and uncertain features of real-world environmental systems. In addition, conditions for environmental management keep changing with time, demanding periodically updated decision support. These properties can be realized by learning from environmental data, using knowledge discovery techniques. In the present paper, data mining techniques are applied for data analysis and for the construction of forecasting modules towards decision making, on the basis of selected air quality information for Athens, Greece. Conclusions are drawn concerning the performance of algorithms, and for the research to be conducted in the future.Text/Conference Paper