Data page layouts for relational databases on deep memory hierarchies
The VLDB Journal — The International Journal on Very Large Data Bases
Database Architecture Optimized for the New Bottleneck: Memory Access
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Data morphing: an adaptive, cache-conscious storage technique
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Sybase IQ multiplex - designed for analytics
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Column-stores vs. row-stores: how different are they really?
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Row-wise parallel predicate evaluation
Proceedings of the VLDB Endowment
DSM vs. NSM: CPU performance tradeoffs in block-oriented query processing
Proceedings of the 4th international workshop on Data management on new hardware
Keyword oriented bitmap join index for in-memory analytical processing
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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The performance of online analytical processing (OLAP) is critical for meeting the increasing requirements of massive volume analytical applications. Typical techniques, such as in-memory processing, column-storage, and join indexes focus on high performance storage media, efficient storage models, and reduced query processing. While they effectively perform OLAP applications, there is a vital limitation: mainmemory database based OLAP (MMOLAP) cannot provide high performance for a large size data set. In this paper, we propose a novel memory dimension table model, in which the primary keys of the dimension table can be directly mapped to dimensional tuple addresses. To achieve higher performance of dimensional tuple access, we optimize our storage model for dimension tables based on OLAP query workload features. We present directly dimensional tuple accessing (DDTA) based join (DDTAJOIN), a technique to optimize query processing on the memory dimension table by direct dimensional tuple access. We also contribute by proposing an optimization of the predicate tree to shorten predicate operation length by pruning useless predicate processing. Our experimental results show that the DDTA-JOIN algorithm is superior to both simulated row-store main memory query processing and the open-source column-store main memory database MonetDB, thanks to the reduced join cost and simple yet efficient query processing.