Access methods for multiversion data
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
ACM SIGMOD Record
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Comparison of access methods for time-evolving data
ACM Computing Surveys (CSUR)
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Proceedings of the Tenth International Conference on Data Engineering
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ICDT '97 Proceedings of the 6th International Conference on Database Theory
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VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
The Time Index: An Access Structure for Temporal Data
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
The LHAM log-structured history data access method
The VLDB Journal — The International Journal on Very Large Data Bases
An asymptotically optimal multiversion B-tree
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental computation and maintenance of temporal aggregates
The VLDB Journal — The International Journal on Very Large Data Bases
Join operations in temporal databases
The VLDB Journal — The International Journal on Very Large Data Bases
Transaction Time Support Inside a Database Engine
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
On computing temporal aggregates with range predicates
ACM Transactions on Database Systems (TODS)
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TPCTC'11 Proceedings of the Third TPC Technology conference on Topics in Performance Evaluation, Measurement and Characterization
ACM SIGMOD Record
Comprehensive and interactive temporal query processing with SAP HANA
Proceedings of the VLDB Endowment
Storing and processing temporal data in a main memory column store
Proceedings of the VLDB Endowment
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Managing temporal data is becoming increasingly important for many applications. Several database systems already support the time dimension, but provide only few temporal operators, which also often exhibit poor performance characteristics. On the academic side, a large number of algorithms and data structures have been proposed, but they often address a subset of these temporal operators only. In this paper, we develop the Timeline Index as a novel, unified data structure that efficiently supports temporal operators such as temporal aggregation, time travel, and temporal joins. As the Timeline Index is independent of the physical order of the data, it provides flexibility in physical design; e.g., it supports any kind of compression scheme, which is crucial for main memory column stores. Our experiments show that the Timeline Index has predictable performance and beats state-of-the-art approaches significantly, sometimes by orders of magnitude.