System dynamics modeling and data mining analyses: a possible integration.

The term “Business Intelligence” was used for the first time in a 1958 article by an IBM researcher Hans Peter Lunh. In his paper he defined business as a “collection of activities carried on for whatever purpose, be it science, technology, commerce industry, law, government, defense, et cetera.

The communication facility serving the conduct of a business (in the broad sense) may be referred to as an intelligence system”. An as definition of intelligence he used this notion: “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal”.

But Business intelligence is more than the mere combination of definitions; in fact we can see it as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. Business intelligence applications include the activities of decision support, query and reporting, online analytical process (OLAP), statistical analysis, forecasting, and data mining.

In this thesis we are interested in the extraction of hidden predictive information from large databases, so we are interested in Data Mining. It is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing business to make proactive, knowledge-driven decision.

The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.