Indexing Blocks to Reduce Space and Time Requirements for Searching Large Data Files

Scientific discoveries are increasingly relying on analysis of massive amounts of data generated from scientific experiments, observations, and simulations. The ability to directly access the most relevant data records, without shifting through all of them becomes essential. While many indexing techniques have been developed to quickly locate the selected data records, the time and space required for building and storing these indexes are often too expensive to meet the demands of in situ or real-time data analysis. Existing indexing methods generally capture information about each individual data record, however, when reading a data record, the I/O system typically has to access a block or a page of data. In this work, we postulate that indexing blocks instead of individual data records could significantly reduce index size and index building time without increasing the I/O time for accessing the selected data records. Our experiments using multiple real datasets on a supercomputer show that block index can reduce query time by a factor of 2 to 50 over other existing methods, including SciDB and FastQuery. But the size of block index is almost negligible comparing to the data size, and the time of building index can reach the peak I/O speed.