On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The analysis of a simple k-means clustering algorithm
Proceedings of the sixteenth annual symposium on Computational geometry
Proceedings of the 17th International Conference on Data Engineering
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Weather Data Mining Using Independent Component Analysis
The Journal of Machine Learning Research
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Multi-objective query processing for database systems
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Reaching the Top of the Skyline: An Efficient Indexed Algorithm for Top-k Skyline Queries
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Monitoring global forest cover using data mining
ACM Transactions on Intelligent Systems and Technology (TIST)
Efficiently evaluating skyline queries on RDF databases
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
ANAPSID: an adaptive query processing engine for SPARQL endpoints
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Efficiently Producing the K Nearest Neighbors in the Skyline on Vertically Partitioned Tables
International Journal of Information Retrieval Research
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Nowadays, climate changes are impacting life on Earth; ecological effects as warming of sea-surface temperatures, catastrophic events as storms or mudslides, and the increase of infectious diseases, are affecting life and development. Unfortunately, experts predict that global temperatures will increase even more during the next years; thus, to decide how to assist possibly affected people, experts require tools that help them to discover potential risky regions based on their weather conditions. We address this problem and propose a tool able to support experts in the discovery of these risky areas.We present CAREY, a federated tool built on top of a weather database, that implements a semi-supervised data mining approach to discover regions with similar weather observations which may characterize micro-climate zones. Additionally, Top-k Skyline techniques have been developed to rank micro-climate areas according to how close they are to a given weather condition of risk. We conducted an initial experimental study as a proof-of-concepts, and the preliminary results suggest that CAREY may provide an effective support for the visualization of potential risky areas.