The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
PROBE Spatial Data Modeling and Query Processing in an Image Database Application
IEEE Transactions on Software Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd 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
A Storage and Access Architecture for Efficient Query Processing in Spatial Database Systems
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Hierarchical Graph Embedding for Efficient Query Processing in Very Large Traffic Networks
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Optimal incremental multi-step nearest-neighbor search
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Techniques for Efficiently Searching in Spatial, Temporal, Spatio-temporal, and Multimedia Databases
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Effectiveness of optimal incremental multi-step nearest neighbor search
Expert Systems with Applications: An International Journal
Proximity queries in time-dependent traffic networks using graph embeddings
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science
Spatial query processing for fuzzy objects
The VLDB Journal — The International Journal on Very Large Data Bases
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Similarity search algorithms that directly rely on index structures and require a lot of distance computations are usually not applicable to databases containing complex objects and defining costly distance functions on spatial, temporal and multimedia data. Rather, the use of an adequate multi-step query processing strategy is crucial for the performance of a similarity search routine that deals with complex distance functions. Reducing the number of candidates returned from the filter step which then have to be exactly evaluated in the refinement step is fundamental for the efficiency of the query process. The state-of-the-art multi-step k-nearest neighbor (kNN) search algorithms are designed to use only a lower bounding distance estimation for candidate pruning. However, in many applications, also an upper bounding distance approximation is available that can additionally be used for reducing the number of candidates. In this paper, we generalize the traditional concept of R-optimality and introduce the notion of RI -optimality depending on the distance information I available in the filter step. We propose a new multi-step kNN search algorithm that utilizes lower- and upper bounding distance information (Ilu) in the filter step. Furthermore, we show that, in contrast to existing approaches, our proposed solution is RIlu - optimal. In an experimental evaluation, we demonstrate the significant performance gain over existing methods.