Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Principles of distributed database systems
Principles of distributed database systems
A graph based cluster approach for vertical partitioning in database design
Data & Knowledge Engineering
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Analysis and performance of inverted data base structures
Communications of the ACM
A Transaction-Based Approach to Vertical Partitioning for Relational Database Systems
IEEE Transactions on Software Engineering
Materialized Views Selection in a Multidimensional Database
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Foundation for Multi-dimensional Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Algorithms for Materialized View Design in Data Warehousing Environment
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Case for Parallelism in Data Warehousing and OLAP
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
The Data Warehouse Lifecycle Toolkit
The Data Warehouse Lifecycle Toolkit
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Focusing on Data Distribution in the WebD2W System
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Adding a Performance-Oriented Perspective to Data Warehouse Design
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Vertical fragmentation of XML data warehouses using frequent path sets
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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In the context of multidimensional databases implemented on relational DBMSs through star schemes, the most effective technique to enhance performances consists of materializing redundant aggregates called views. In this paper we investigate the problem of vertical fragmentation of views aimed at minimizing the workload response time. Each view includes several measures which not necessarily are always requested together; thus, the system performance may be increased by partitioning the views into smaller tables. On the other hand, drill-across queries involve measures taken from two or more views; in this case the access costs may be decreased by unifying these views into larger tables. After formalizing the fragmentation problem as a 0-1 integer linear programming problem, we define a cost function and outline a branch-and-bound algorithm to minimize it. Finally, we demonstrate the usefulness of our approach by presenting a set of experimental results based on the TPC-D benchmark.