On Multiple Query Optimization in Data Mining

2005 ◽  
pp. 696-701 ◽  
Author(s):  
Marek Wojciechowski ◽  
Maciej Zakrzewicz
Keyword(s):  
Data Mining ◽  
Query Optimization ◽  
Multiple Query Optimization ◽  
Multiple Query

Multiple Decisional Query Optimization in Big Data Warehouse

2018 ◽  
Vol 14 (3) ◽  
pp. 22-43
Author(s):  
Ratsimbazafy Rado ◽  
Omar Boussaid
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Query Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Distributed Environment ◽  
Shared Data ◽  
Multiple Query Optimization ◽  
Execution Engine ◽  
Multiple Query

Data warehousing (DW) area has always motivated a plethora of hard optimization problem that cannot be solved in polynomial time. Those optimization problems are more complex and interesting when it comes to multiple OLAP queries. In this article, the authors explore the potential of distributed environment for an established data warehouse, database-related optimization problem, the problem of Multiple Query Optimization (MQO). In traditional DW materializing views is an optimization technic to solve such problem by storing pre-computed join or frequently asked queries. In this era of big data this kind of view materialization is not suitable due to the data size. In this article, the authors tackle the problem of MQO on distributed DW by using a multiple, small, shared and easy to maintain shared data. The evaluation shows that, compared to available default execution engine, the authors' approach consumes on average 20% less memory in the Map-scan task and it is 12% faster regarding the execution time of interactive and reporting queries from TPC-DS.

Multiple query optimization approach based on hive+1

2018 ◽  
Vol 35 (1) ◽  
pp. 47-53
Author(s):  
Ying Han ◽  
Xiao Qiang Yu ◽  
Xu Li ◽  
Chun Long Yao ◽  
Xin Zhao
Keyword(s):  
Query Optimization ◽  
Optimization Approach ◽  
Multiple Query Optimization ◽  
Multiple Query
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