On the analysis of indexing schemes
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Indexing the edges—a simple and yet efficient approach to high-dimensional indexing
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
An effective way to represent quadtrees
Communications of the ACM
Multidimensional binary search trees used for associative searching
Communications of the ACM
Strategies for processing ad hoc queries on large data warehouses
Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
A retrieval technique for high-dimensional data and partially specified queries
Data & Knowledge Engineering
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
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
Making the Pyramid Technique Robust to Query Types and Workloads
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
"One Size Fits All": An Idea Whose Time Has Come and Gone
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Supporting RFID-based item tracking applications in Oracle DBMS using a bitmap datatype
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Indexing temporal data with virtual structure
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Variable granularity space filling curve for indexing multidimensional data
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Data management in RFID applications
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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Existing methods for the management of multidimensional data typically do not scale well with an increased number of dimensions or require the unsupported augmentation of the kernel. However, the use of multidimensional data continues to grow in modern database applications, specifically in spatio-temporal databases. These systems produce vast volumes of multidimensional data, and as such, data is stored in commercial RDBMS. Therefore, the efficient management of such multidimensional data is crucial. Despite it being applicable to any multidimensional vector data, we consider Radio Frequency Identifications (RFID) systems in this work. Due to RFID's acceptance and rapid growth into new and complex applications, together with the fact that, as with commercial applications, its data is stored within commercial RDBMS, we have chosen RFID as a pertinent test-bed. We show that its data can be represented as vectors in multidimensional space and that the VG-curve combined with Multidimensional Dynamic Clustering Primary Index, which can be integrated into commercial RDBMS, can be used to efficiently access such data. In an empirical study conducted on three, five and nine dimensional RFID data we show that the presented concept outperforms available off-the-shelf options with a fraction of the required space.