Efficient processing of one and two dimensional proximity queries in associative memory
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Finding Meaningful Regions Containing Given Keywords from Large Text Collections
DS '99 Proceedings of the Second International Conference on Discovery Science
MeshSQL: the query language for simulation mesh data
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
A Novel Document Ranking Method Using the Discrete Cosine Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for modeling and evaluating automatic semantic reconciliation
The VLDB Journal — The International Journal on Very Large Data Bases
Query processing of multi-way stream window joins
The VLDB Journal — The International Journal on Very Large Data Bases
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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.