The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
Multidimensional Database Technology
Computer
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Systems Analysis Modelling Simulation
Weather Data Mining Using Independent Component Analysis
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Effective OLAP Mining of Evolving Data Marts
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
Professional Microsoft SQL Server Analysis Services 2008 with MDX
Professional Microsoft SQL Server Analysis Services 2008 with MDX
Application of Multidimensional Databases of Rainfall and Low Pressure Systems on OLAP-Based Model
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 04
Effective data warehouse for information delivery: a literature survey and classification
International Journal of Networking and Virtual Organisations
Hi-index | 0.00 |
The multidimensional data model can be effectively utilised for analysing huge and detailed meteorological datasets forecasted by numerical weather prediction (NWP) model. The model cannot predict any weather event directly. The output products of model are interpreted by man-machine mix to infer the idiosyncratic behaviour of weather events. The mathematical tools for analysis and forecasting are able to provide forecast of weather variables only at grid-points. In this paper, the technology of dimension modelling has been adapted for analysing NWP model output datasets corresponding to sub-grid scale events viz. cloudburst, using OLAP technique. The huge datasets of weather variables available directly and derived indirectly, are mined so as to locate the patterns of cloudburst formation. K-means clustering technique has been used to generate clusters of convergence and divergence, for four real-life cases of cloudburst. It has been observed that clustering technique can help in identification of patterns conducive to formation of cloudburst.