Indexing temporal data using existing B+-trees
Data & Knowledge Engineering
On the analysis of indexing schemes
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Comparison of access methods for time-evolving data
ACM Computing Surveys (CSUR)
Developing time-oriented database applications in SQL
Developing time-oriented database applications in SQL
Efficient Management of Time-Evolving Databases
IEEE Transactions on Knowledge and Data Engineering
Designing Access Methods for Bitemporal Databases
IEEE Transactions on Knowledge and Data Engineering
Indexing Valid Time Databases via B+-Trees
IEEE Transactions on Knowledge and Data Engineering
Efficient Indexing for Constraint and Temporal Databases
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Managing Intervals Efficiently in Object-Relational Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Object-Relational Indexing for General Interval Relationships
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Light-Weight Indexing of General Bitemporal Data
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Temporal Data and the Relational Model
Temporal Data and the Relational Model
An efficient method for indexing now-relative bitemporal data
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Efficiently processing queries on interval-and-value tuples in relational databases
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
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In this study, we investigate and present a new index structure, Triangular Decomposition Tree (TD-tree), which can efficiently store and query temporal data in modern database applications. TD-tree is based on spatial representation of interval data and a recursive triangular decomposition of this space. A bounded number of intervals are stored in each leaf of the tree, which hence may be unbalanced. We describe the algorithms used with this structure. A single query algorithm can be applied uniformly to different query types without the need of dedicated query transformation. In addition to the advantages related to the usage of a single query algorithm for different query types and better space complexity, the empirical performance of the TD-tree is demonstrated to be superior to its best known competitors. Also, presented concept can be extended to more dimensions and therefore applied to efficiently manage spatio-temporal data.