Multiattribute hashing using Gray codes
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Fractals for secondary key retrieval
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
On the analysis of indexing schemes
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
A retrieval technique for high-dimensional data and partially specified queries
Data & Knowledge Engineering
A Robust Multi-Attribute Search Structure
Proceedings of the Fifth International Conference on Data Engineering
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
Implementation of Multidimensional Index Structures for Knowledge Discovery in Relational Databases
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Indexing temporal data with virtual structure
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Indexing RFID data using the VG-curve
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
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Efficiently accessing multidimensional data is a challenge for building modern database applications that involve many folds of data such as temporal, spatial, data warehousing, bio-informatics, etc. This problem stems from the fact that multidimensional data have no given order that preserves proximity. The majority of the existing solutions to this problem cannot be easily integrated into the current relational database systems since they require modifications to the kernel. A prominent class of methods that can use existing access structures are 'space filling curves'. In this study, we describe a method that is also based on the space filling curve approach, but in contrast to earlier methods, it connects regions of various sizes rather than points in multidimensional space. Our approach allows an efficient transformation of interval queries into regions of data that results in significant improvements when accessing the data. A detailed empirical study demonstrates that the proposed method outperforms the best available off-theshelf methods for accessing multidimensional data.