In-memory data management for consumer transactions the timesten approach
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Implementation techniques for main memory database systems
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Main Memory Database Systems: An Overview
IEEE Transactions on Knowledge and Data Engineering
SIREN: A Memory-Conserving, Snapshot-Consistent Checkpoint Algorithm for in-Memory Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
The end of an architectural era: (it's time for a complete rewrite)
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Dynamic adaptive data structures for monitoring data streams
Data & Knowledge Engineering
Finding frequent items in data streams using ESBF
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
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Main memory database management systems have become essential for response-time-bounded applications, such as those in telecommunications systems or Internet, where users frequently access a table in order to get information or check whether an element exists, and require the response to be as fast as possible. Continuous data growth is making it unaffordable to keep entire relations in memory and some commercial applications provide two different engines to handle data in-memory and on-disk separately. However, these systems assign each table to one of these engines, forcing large relations to be kept on secondary storage. In this paper we present TwinS -- a hybrid database management system that allows managing hybrid tables, i.e. tables partially managed by both engines. We show that we can reduce response time when accessing a large table in the database. All our experiments have been run on a dual-engine DBMS: IBM®SolidDB®.