Science User Scenarios for a Virtual Observatory Design Reference Mission: Science Requirements for Data Mining

The knowledge discovery potential of the new large astronomical databases is vast. When these are used in conjunction with the rich legacy data archives, the opportunities for scientific discovery multiply rapidly. A Virtual Observatory (VO) framework will enable transparent and efficient access, search, retrieval, and visualization of data across multiple data repositories, which are generally heterogeneous and distributed. Aspects of data mining that apply to a variety of science user scenarios with a VO are reviewed. The development of a VO should address the data mining needs of various astronomical research constituencies. By way of example, two user scenarios are presented which invoke applications and linkages of data across the catalog and image domains in order to address specific astrophysics research problems. These illustrate a subset of the desired capabilities and power of the VO, and as such they represent potential components of a VO Design Reference Mission. One of the major functions of a Virtual Observatory (VO) is to facilitate data mining and knowledge discovery within the very large astronomical databases that are now coming on-line (or soon will be). A similarly important function of the VO is to facilitate linkages and cross-archive investigations utilizing these new data in conjunction with the rich legacy data archives that preceded them. The scientific teams that generate large (multi-Terabyte) databases cannot begin to tap their full scientific potential. Thus a significant portion of the astronomical research community and a comprehensive suite of research tools should be brought to bear on extracting the maximum scientific return for the huge investment in these large astronomical facilities, large surveys, and large scientific data systems. One approach to this problem can be identified as " data mining ". What is data mining and why is applicable to scientific research? Data mining is defined as an information extraction activity whose goal is to discover hidden facts contained in databases. Data mining has taken the business community by storm and the phrase has become a bit overworked to describe some fairly routine functions of marketing. Even so, there are consequently now a vast array of resources and research techniques available for exploitation by the scientific communities. It is useful therefore to examine a further categoriza-tion of data mining thrusts and their sub-components, since these are likewise applicable to the scientific exploration of large astronomical databases.