Cost models for overlapping and multiversion structures
ACM Transactions on Database Systems (TODS)
Joining interval data in relational databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Benchmarking access methods for time-evolving regional data
Data & Knowledge Engineering
Join operations in temporal databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient join processing over uncertain data
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Query processing of multi-way stream window joins
The VLDB Journal — The International Journal on Very Large Data Bases
Read-Optimized, Cache-Conscious, Page Layouts for Temporal Relational Data
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Avoiding version redundancy for high performance reads in temporal databases
Proceedings of the 4th international workshop on Data management on new hardware
Multiversion join index for multiversion data warehouse
Information and Software Technology
Design and evaluation of trajectory join algorithms
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
BiB+-tree: an efficient multiversion access method for bitemporal databases
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Supporting temporal slicing in XML databases
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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We examine the problem of processing temporal joins in the presence of indexing schemes. Previous work on temporal joins has concentrated on non-indexed relations which were fully scanned. Given the large data volumes created by the ever increasing time dimension, sequential scanning is prohibitive. This is especially true when the temporal join involves only parts of the joining relations (e.g., a given time interval instead of the whole timeline). Utilizing an index becomes then beneficial as it directs the join to the data of interest. We consider temporal join algorithms for three representative indexing schemes, namely a B+-tree, an R*-tree and a temporal index, the Multiversion B+-tree (MVBT). Both the B+-tree and R*-tree result in simple but not efficient join algorithms because neither index achieves good temporal data clustering. Better clustering is maintained by the MVBT through record copying. Nevertheless, copies can greatly affect the correctness and effectiveness of the join algorithms. We identify these problems and propose efficient solutions and optimizations. An extensive comparison of all index based temporal joins, using a variety of datasets and query characteristics shows that the MVBT based join algorithms are consistently faster. In particular the link-based algorithm has the most robust behavior. In our experiments it showed a ten-fold improvement over the R*-tree joins while it was between six and thirty times faster than the B+-tree joins.