The important scientific challenge of understanding global climate change is one that clearly requires the application of knowledge discovery and datamining techniques on a massive scale. Advances in parallel supercomputing technology, enabling high-resolution modeling, as well as in sensor technology, allowing data capture on an unprecedented scale, conspire to overwhelm present-day analysis approaches. We present here early experiences with a prototype exploratory data analysis environment, CONQUEST, designed to provide content-based access to such massive scientific datasets. CONQUEST (CONtent-based Querying in Space and Time) employs a combination of workstations and massively parallel processors (MPP's) to mine geophysical datasets possessing a prominent temporal component. It is designed to enable complex multi-modal interactive querying and knowledge discovery, while simultaneously coping with the extraordinary computational demands posed by the scope of the datasets involved. A...

A major challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of data produced by sensors or long simulations of climate models. We have developed a prototype system called QUEST to provide content-based access to massive datasets. QUEST employs workstations as well as teraFLOP computers to analyze geoscience data to produce spatial-temporal features that can be used as high-level indexes. Our first application area is global change climate modeling. In the initial prototype, the first features extracted are cyclones trajectories from the output of multi-year climate simulations produced by a General Circulation Model. We present an algorithm for cyclone extraction and illustrate the use of cyclone indexes to access subsets of GCM data for further analysis and visualization. 1 Introduction A critical challenge facing geophysical science today is the unavailability of high-level analysis tools with which to ...

A major challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of data produced by sensors or long simulations of climate models. As part of a NASA HPCC Grand Challenge effort [Mun92], we have developed a prototype environment called QUEST to provide content-based query access to massive datasets used in geophysical applications. QUEST employs work stations as well as massively parallel processors to produce spatio-temporal features that are used as high-level indexes into terabyte datasets. This paper discusses our continued development of the QUEST environment. 1 Introduction A critical challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of information captured by sensors onboard orbiting satellites or produced by climate models. To address this challenge, we must develop a new generation of systems for scientific data managem...