Integration of Data Mining and Data Warehousing: a practical methodology

The ever growing repository of data in all fields poses new challenges to the modern analytical systems. Real-world datasets, with mixed numeric and nominal variables, are difficult to analyze and require effective visual exploration that conveys semantic relationships of data. Traditional data mining techniques such as clustering clusters only the numeric data. Little research has been carried out in tackling the problem of clustering high cardinality nominal variables to get better insight of underlying dataset. Several works in the literature proved the likelihood of integrating data mining with warehousing to discover knowledge from data. For the seamless integration, the mined data has to be modeled in form of a data warehouse schema. Schema generation process is complex manual task and requires domain and warehousing familiarity. Automated techniques are required to generate warehouse schema to overcome the existing dependencies. To fulfill the growing analytical needs and to overcome the existing limitations, we propose a novel methodology in this paper that permits efficient analysis of mixed numeric and nominal data, effective visual data exploration, automatic warehouse schema generation and integration of data mining and warehousing. The proposed methodology is evaluated by performing case study on real-world data set. Results show that multidimensional analysis can be performed in an easier and flexible way to discover meaningful knowledge from large datasets

Seismological data warehousing and mining system for seismic network in Sri Lanka

Earthquake data composes an ever increasing collection of earth science information for post-processing analysis. Earth scientists, Geologist, administration officers are working with these data collections for scientific or planning purposes. Therefore, colleting ofseismic data is very much useful. We propose a seismic data management system and analyzing system for seismological data collected in the local seismic network system in Sri Lanka. Although several hundreds of earthquakes and earth tremors reported in and around Sri Lanka during the last decade, detailed investigation of their activity has not been carried out. The unavailability of suitable seismic network system for Sri Lanka and the lack of expertise for analyzing such data are two main reasons for this. Therefore we try to investigate possible location ofseismic station and study the method of data management system. By using available seismic data and the location ofseismically active regions a suitable locations for seismic stations are proposed. One ofthe most important parts ofthe present study is to create a Seismological Information Data warehouse to store the data coming from the seismic network system and global network data, and managing existing database. Since this type of local seismic station system has not been installed yet, the maintaining procedure ofthe proposing database is tested using the available global data from the IRIS data management center. A seismic data management system and analyzing system for seismological data collected in the local seismic network are discussed. The QlikView Business Intelligent Tool is used to develop the algorithm for visualization of data stored in the proposed seismic network system. Various types of profiles can be visualized using QlikView visualization method. Then we try to discuss the architecture of a seismic data management and mining system for quick and easy data collection, processing. The database is creating using SQL server and applying the data to the software for creating data warehouse, Querying, and Mining ofseismic data stored in the database. Giving earthquake information alert to general public and government administrative officers are also an important task. We therefore made an attempt to give earthquake information to relevant authorities using our database. If an earthquake takes place, the region where the earthquake occurred can be identified using our database. Then location of the event can be informed with respect to the famous cities in Sri Lanka