A number of execution strategies for parallel evaluation of multi-join queries have been proposed in the literature. In this paper we give a comparative performance evaluation of four execution strategies by implementing all of them on the same parallel database system, PRISMA/DB. Experiments have been done up to 80 processors. These strategies, coming from the literature, are named: Sequential Parallel, Synchronous Execution, Segmented Right-Deep, and Full Parallel. Based on the experiments clear guidelines are given when to use which strategy. This is an extended abstract; the full paper appeared in Proc. ACM SIGMOD'94, Minneapolis, Minnesota, May 24–27, 199
In the current work, we derive a complete approach to optimization and automatic parallelization of ...
In the current work, we derive a complete approach to optimization and automatic parallelization of ...
A consensus on parallel architecture for very large database management has emerged. This architectu...
A number of execution strategies for parallel evaluation of multi-join queries have been proposed in...
In the PRISMA-project, a large multi-processor system has been built, is be used to study the per-fo...
Paper presented to the 3rd Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held...
Abstract. In this paper, the performance and characteristics of the execution of various join-trees ...
In this paper, the performance and characteristics of the execution of various join-trees on a paral...
Big data analytics often requires processing complex queries us-ing massive parallelism, where the m...
The performance and characteristics of the execution of various join-trees on a parallel DBMS are st...
It is proposed that the execution of a set of join queries in a distributed environment should be co...
Abstract-In this paper, we study the subject of exploiting interoperator parallelism to optimize the...
Parallel and distributed processing are alternatives to optimize queries in Database Systems. In thi...
In this paper we present a new framework for studying parallel query optimization. We first note tha...
A consensus on parallel architecture for very large database management has emerged. This architectu...
In the current work, we derive a complete approach to optimization and automatic parallelization of ...
In the current work, we derive a complete approach to optimization and automatic parallelization of ...
A consensus on parallel architecture for very large database management has emerged. This architectu...
A number of execution strategies for parallel evaluation of multi-join queries have been proposed in...
In the PRISMA-project, a large multi-processor system has been built, is be used to study the per-fo...
Paper presented to the 3rd Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held...
Abstract. In this paper, the performance and characteristics of the execution of various join-trees ...
In this paper, the performance and characteristics of the execution of various join-trees on a paral...
Big data analytics often requires processing complex queries us-ing massive parallelism, where the m...
The performance and characteristics of the execution of various join-trees on a parallel DBMS are st...
It is proposed that the execution of a set of join queries in a distributed environment should be co...
Abstract-In this paper, we study the subject of exploiting interoperator parallelism to optimize the...
Parallel and distributed processing are alternatives to optimize queries in Database Systems. In thi...
In this paper we present a new framework for studying parallel query optimization. We first note tha...
A consensus on parallel architecture for very large database management has emerged. This architectu...
In the current work, we derive a complete approach to optimization and automatic parallelization of ...
In the current work, we derive a complete approach to optimization and automatic parallelization of ...
A consensus on parallel architecture for very large database management has emerged. This architectu...