Data Mining and Decision Support for Business and Science

Abstract

Information by itself is no longer perceived as an asset. Billions of business transactions are recorded in enterprise-scale data warehouses every day. Acquisition, storage, and management of business information are commonplace and often automated. Recent advances in remote or other sensor technologies have led to the development of scientific data repositories. Database technologies, ranging from relational systems to extensions like spatial, temporal, time series, text, or media, as well as specialized tools like geographical information systems (GIS) or online analytical processing (OLAP), have transformed the design of enterprise-scale business or large scientific applications. The question increasingly faced by the scientific or business decision-maker is not how one can get more information or design better information systems but what to make of the information and systems already in place. The challenge is to be able to utilize the available information, to gain a better understanding of the past, and to predict or influence the future through better decision making. Researchers in data mining technologies (DMT) and decision support systems (DSS) are responding to this challenge. Broadly defined, data mining (DM) relies on scalable statistics, artificial intelligence, machine learning, or knowledge discovery in databases (KDD). DSS utilize available information and DMT to provide a decision-making tool usually relying on human-computer interaction. Together, DMT and DSS represent the spectrum of analytical information technologies (AIT) and provide a unifying platform for an optimal combination of data dictated and human-driven analytics.