Effective high-level data management is becoming an important issue with more and more scientific applications manipulating huge amounts of secondary-storage and tertiary-storage data using parallel processors. A major problem facing the current solutions to this data management problem is that these solutions either require a deep understanding of specific data storage architectures and file layouts to obtain the best performance (as in high-performance storage management systems and parallel file systems) or they sacrifice significant performance in exchange for ease-of-use and portability (as in traditional database management systems). While the success of these approaches varies depending on the specific system and applications, the trend in scientific computing towards processing large-scale datasets demands both high-performance and ease-of-use.