Efficient Indexing Technology for Data Mining of Scientific Data Arie Shoshani, Senior Staff Scientist NSERC, and Lawrence Berkeley National Laboratory USA Return to Keynotes Abstract Data mining in scientific applications usually involves searches over a large number of objects in the multidimensional space of their properties, or searches for known patterns. This is in contrast to mining for associations between objects, or discovering new patterns. Examples are searching over billions of objects to find rare objects by expressing numerical range conditions on their properties, or finding flame fronts in large volume, spatio-temporal combustion simulation data by expressing multiple conditions over the data values associated with the cells in the 3D space. A critical issue in supporting such directed searches over large data volumes is the efficiency of the indexing method. This is required in order to facilitate real time exploration of the data. In this talk, we will describe a specialized bitmap indexing method, called FastBit, which has proved especially appropriate for numeric multidimensional data common in scientific applications. We will illustrate the use of this technology with several examples. ©2006 Association for Computing Machinery