Comparing the Perfomances of Multidimensional Databases

Do you see your future in working with data? Is analysis of Big Data and using the results to aid organizations in better decision making sounds exciting to you? Do you want to step on the path of becoming an expert in data management? Then you should take the opportunity and consider this exciting topic. Companies in the domains of logistics, retail and finance are using multidimensional databases to store huge amount of business information. There, business facts such as revenue and costs are stored across dimensions such as countries, products, customer types, etc. There are different ways of organizing such data in multidimensional databases. You probably have heard about star and snowflake schemas that are typically employed in the practice. Beside these popular architectures for multidimensional databases there are alternatives that claim to have advantages over established architectures on the potential expense of perfomance. Your task is to use a large set of multidimensional data, to organize it according to different architectures, and to compare their perfomance. Having this clear goal, you can use the conceptual foundations of the underlying architectures to write a usefull and interesting thesis.