BI and data warehouse solutions for energy production industry: application of the CRISP-DM methodology.

This paper reports two projects for supporting decisions of the Company of Electricity in Azores Islands, Electricidade dos Açores. There were several decisions to support, such as whether communications between islands should moved from the present telephone lines to VoIP, and if better models to support forecast power consumption should be adopted. The solution established integrates OLAP cubes in a data mining project, based on CRISP-DM process model. Both for strategic and more operational decisions the objective was always to get accurate data, build a data warehouse and to get tools to analyze it in order to properly inform the decision makers. These DSS's translates big CSV flat files or acquire data in real time from operational Data Bases to update a data warehouse, including importing, evaluating data quality and populating relational tables. Multidimensional data cubes with numerous dimensions and measures were used for operational decisions and as exploration tools in the strategic ones. Data mining models for forecasting, clustering, decision trees and association rules identified several inefficient procedures and even fraud situations. Not only was possible to support the necessary decisions, but several models were also displayed so that control decision makers and strategists could support new problems

Data Management and Mining in Astrophysical Databases

We analyse the issues involved in the management and mining of astrophysical data. The traditional approach to data management in the astrophysical field is not able to keep up with the increasing size of the data gathered by modern detectors. An essential role in the astrophysical research will be assumed by automatic tools for information extraction from large datasets, i.e. data mining techniques, such as clustering and classification algorithms. This asks for an approach to data management based on data warehousing, emphasizing the efficiency and simplicity of data access; efficiency is obtained using multidimensional access methods and simplicity is achieved by properly handling metadata. Clustering and classification techniques, on large datasets, pose additional requirements: computational and memory scalability with respect to the data size, interpretability and objectivity of clustering or classification results. In this study we address some possible solutions.Comment: 10 pages, Late