The design and analysis of spatial data structures
The design and analysis of spatial data structures
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
S3: similarity search in CAD database systems
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Machine Learning
Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
OPTICS-OF: Identifying Local Outliers
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Efficiently Supporting Multiple Similarity Queries for Mining in Metric Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Towards Location-Based Real-Time Monitoring Systems in u-LBS
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
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Numerous data mining algorithms rely heavily on similarity queries. Although many or even all of the performed queries do not depend on each other, the algorithms process them in a sequential way. Recently, a novel technique for efficiently processing multiple similarity queries issued simultaneously has been introduced. It was shown that multiple similarity queries substantially speed-up query intensive data mining applications. For the important case of multiple k-nearest neighbor queries on top of a multidimensional index structure the problem of scheduling directory pages and data pages arises. This aspect has not been addressed so far. In this paper, we derive the theoretic foundation of this scheduling problem. Additionally, we propose several scheduling algorithms based on our theoretical results. In our experimental evaluation, we show that considering the maximum priority of pages clearly outperforms other scheduling approaches.