Database partitioning in a cluster of processors
ACM Transactions on Database Systems (TODS)
Data allocation in distributed database systems
ACM Transactions on Database Systems (TODS)
Parallel database systems: the future of high performance database systems
Communications of the ACM
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Automating physical database design in a parallel database
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Horizontal data partitioning in database design
SIGMOD '82 Proceedings of the 1982 ACM SIGMOD international conference on Management of data
OLAP Query Routing and Physical Design in a Database Cluster
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Cache-Aware Query Routing in a Cluster of Databases
Proceedings of the 17th International Conference on Data Engineering
Multi-Dimensional Database Allocation for Parallel Data Warehouses
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
WARLOCK: A Data Allocation Tool for Parallel Warehouses
Proceedings of the 27th International Conference on Very Large Data Bases
Analysis of Dynamic Load Balancing Strategies for Parallel Shared Nothing Database Systems
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Dynamic Load Balancing in Hierarchical Parallel Database Systems
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Experimental Evaluation of a New Distributed Partitioning Technique for Data Warehouses
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
Query-Driven Data Allocation Algorithms for Distributed Database Systems
DEXA '97 Proceedings of the 8th International Conference on Database and Expert Systems Applications
Data placement in shared-nothing parallel database systems
The VLDB Journal — The International Journal on Very Large Data Bases
Experimental evidence on partitioning in parallel data warehouses
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Allocating Fragments in Distributed Databases
IEEE Transactions on Parallel and Distributed Systems
Supporting table partitioning by reference in oracle
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Data Partitioning in Data Warehouses: Hardness Study, Heuristics and ORACLE Validation
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Parallel OLAP query processing in database clusters with data replication
Distributed and Parallel Databases
A Joint Design Approach of Partitioning and Allocation in Parallel Data Warehouses
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Dimension table driven approach to referential partition relational data warehouses
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
How to exploit the device diversity and database interaction to propose a generic cost model?
Proceedings of the 17th International Database Engineering & Applications Symposium
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Data fragmentation and allocation in distributed and parallel Database Management Systems (DBMS) have been extensively studied in the past. Previous work tackled these two problems separately even though they are dependent on each other. We recently developed a combined algorithm that handles the dependency issue between fragmentation and allocation. A novel genetic solution was developed for this problem. The main issue of this solution and previous solutions is the lack of real life verifications of these models. This paper addresses this gap by verifying the effectiveness of our previous genetic solution on the Teradata DBMS. Teradata is a shared nothing DBMS with proven scalability and robustness in real life user environments as big as 10's of petabytes of relational data. Experiments are conducted for the genetic solution and previous work using the SSB benchmark (TPC-H like) on a Teradata appliance running TD 13.10. Results show that the genetic solution is faster than previous work by a 38%.