Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Spatial join selectivity using power laws
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using the fractal dimension to cluster datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
F4: large-scale automated forecasting using fractals
Proceedings of the eleventh international conference on Information and knowledge management
Journal of the American Society for Information Science and Technology
A fast and effective method to find correlations among attributes in databases
Data Mining and Knowledge Discovery
MAMCost: Global and Local Estimates leading to Robust Cost Estimation of Similarity Queries
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Measuring evolving data streams' behavior through their intrinsic dimension
New Generation Computing
Halite: Fast and Scalable Multiresolution Local-Correlation Clustering
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
Research on global warming and climate changes has attracted a huge attention of the scientific community and of the media in general, mainly due to the social and economic impacts they pose over the entire planet. Climate change simulation models have been developed and improved to provide reliable data, which are employed to forecast effects of increasing emissions of greenhouse gases on a future global climate. The data generated by each model simulation amount to Terabytes of data, and demand fast and scalable methods to process them. In this context, we propose a new process of analysis aimed at discriminating between the temporal behavior of the data generated by climate models and the real climate observations gathered from ground-based meteorological station networks. Our approach combines fractal data analysis and the monitoring of real and model-generated data streams to detect deviations on the intrinsic correlation among the time series defined by different climate variables. Our measurements were made using series from a regional climate model and the corresponding real data from a network of sensors from meteorological stations existing in the analyzed region. The results show that our approach can correctly discriminate the data either as real or as simulated, even when statistical tests fail. Those results suggest that there is still room for improvement of the state-of-the-art climate change models, and that the fractal-based concepts may contribute for their improvement, besides being a fast, parallelizable, and scalable approach.