SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Self-spacial join selectivity estimation using fractal concepts
ACM Transactions on Information Systems (TOIS)
Multidimensional access methods
ACM Computing Surveys (CSUR)
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
Processing Complex Similarity Queries with Distance-Based Access Methods
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Evaluation Techniques for Complex Similarity Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Optimal Multidimensional Query Processing Using Tree Striping
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Deflating the Dimensionality Curse Using Multiple Fractal Dimensions
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The power-method: a comprehensive estimation technique for multi-dimensional queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Querying high-dimensional data in single-dimensional space
The VLDB Journal — The International Journal on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
Constrained subspace skyline computation
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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In this paper, we present a new approach to indexing multidimensional data that is particularly suitable for the efficient incremental processing of nearest neighbor queries. The basic idea is to use index-striping that vertically splits the data space into multiple low- and medium-dimensional data spaces. The data from each of these lower-dimensional subspaces is organized by using a standard multi-dimensional index structure. In order to perform incremental NN-queries on top of index-striping efficiently, we first develop an algorithm for merging the results received from the underlying indexes. Then, an accurate cost model relying on a power law is presented that determines an appropriate number of indexes. Moreover, we consider the problem of dimension assignment, where each dimension is assigned to a lower-dimensional subspace, such that the cost of nearest neighbor queries is minimized. Our experiments confirm the validity of our cost model and evaluate the performance of our approach.