Efficient processing of proximity queries for large databases

Emerging multimedia applications require database systems to provide support for new types of objects and to process queries that may have no parallel in traditional database applications. One such important class of queries are the proximity queries that aims to retrieve objects in the database that are related by a distance metric in a way that is specified by the query. The importance of proximity queries has earlier been realized in developing constructs for visual languages. In this paper, we present algorithms for answering a class of proximity queries-fixed-radius nearest-neighbor queries over point object. Processing proximity queries using existing query processing techniques results in high CPU and I/O costs. We develop new algorithms to answer proximity queries over objects that lie in the one-dimensional space (e.g., words in a document). The algorithms exploit query semantics to reduce the CPU and I/O costs, and hence improve performance. We also show how our algorithms can be generalized to handle d-dimensional objects