SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Database System Implementation
Database System Implementation
Weaving Relations for Cache Performance
Proceedings of the 27th International Conference on Very Large Data Bases
Towards Sensor Database Systems
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
Building the Data Warehouse
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Integrating compression and execution in column-oriented database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Performance tradeoffs in read-optimized databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Sybase IQ multiplex - designed for analytics
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Self-organizing tuple reconstruction in column-stores
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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Materialization is a key issue for query execution in column-oriented Data Warehouse Management System (DWMS) due to the fact that it has direct influence on the query efficiency. Focusing on the defects of traditional strategies Early Materialization and Late Materialization, this paper propose a new materialization strategy called VPMS (Value path Materialization Strategy) to solve those problem. First, VPMS define a new descriptor structure---pass block for the intermediate results during physical execution. For a given physical query tree, VPMS generates value path. Depending on the value path, the values of the column are saved in the value area of the pass block when needed by the upper nodes, otherwise, only save the location information. Finally, during the query execution, the physical sub-operation is specified according to the materialization path and the query object itself, which effectively reduces the unnecessary duplication of judgments. Experiments on benchmark dataset SSB demonstrate the overall effectiveness of our approach.