BALWOIS 2010 - Ohrid, Republic of Macedonia - 25, 29 May 2010 1 Data Analysis of Spatio-Temporal Sensor Data as a Contribution to the Model Analysis for Water Resources Sanja Veleva 1 , Kosta Mitreski 2 University SS Cyril and Methodius, Faculty of Electrical Engineering and Information Technologies Skopje, MACEDONIA Abstract The quality of the information is measured by its accuracy and its relevance over time. Therefore, the process of data analysis of the sensor eco-data is of a great importance to the detection and prediction of the eco-hydrology phenomena. The existing models for data mining do not relate to the continuously changing characteristics of the sensor eco-data. Furthermore, most of the monitoring systems are based on event alert services, which do not answer to the continuous variations of the measured parameters. Our approach embeds the nature of system characteristics into one dynamic model for data mining of continuously changing spatio-temporal characteristics of one eco-hydrology system. The continuously gathered sensor eco-data from the region of Lake Prespa consisted of 320 water samples, among them 224 from the lake gauging stations and 96 from the river gauging stations. Considering the recommendations from the Water Framework Directive (WFD), the sensor eco-data were grouped into three types: physical, chemical and biological, corresponding to their aspect of water quality. All of these types convey the same class definition in the form of value, spatial and temporal information. To define our sensor data mining model we contribute to three segments: outlier analysis, pattern analysis, and prediction analysis. The suggested sensor data analysis model should be of a useful asset in obtaining knowledge for certain aquatic phenomena. Keywords: sensor eco-data, model analysis, eco-hydrology, Water Framework Directive, Lake Prespa Introduction The significance of the sensor eco-data is more and more perceptible considering the continuously varying conditions in the today’s climate. Nowadays, the presence of the sensor eco-data is evident and even increasingly noticeable in different areas of application. Therefore, it is of crucial importance to obtain fast and effective extraction of the information of a certain eco-phenomena, but also with acceptable reliability. The continuously gathered sensor eco-data from the region of Lake Prespa consisted of 320 water samples, among them 224 from the lake gauging stations and 96 from the river gauging stations. The eco-data was organized in the integrated database system, in order to provide a convenient, easy-to- use, and an intuitive way of storing the captured data. The integrated database system represents a centralized storage facility which enables the users to better organize, control, manage and use the data, create reports, perform statistical analysis, establish patterns in the model of the data, etc. Considering the recommendations from the Water Framework Directive (WFD), the sensor eco-data were grouped into three types: physical, chemical and biological, corresponding to their aspect of water quality. For the needs of this paper, we introduce the process of data mining as a part of the data analysis of eco-hydrology phenomena. The defined types of eco-data are characterized by the same class definition in the form of value, spatial and temporal information. To define our sensor data mining model we contribute to the three segments of data analysis: outlier analysis, pattern analysis, and prediction analysis. Our approach embeds the nature of system characteristics into one dynamic model for data mining of continuously changing spatio-temporal characteristics of one eco-hydrology system. All of these eco-data analysis potentially lead toward an integrated and sustainable model of ecological and environmental structure of a certain water body. Sensor eco-data The researchers form Albania, Greece and Macedonia in the framework of FP6 Project TRABOREMA collected water samples of the Lake Prespa from several specific designated points and areas of the