Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Integrating database and dialogue design
Knowledge and Information Systems
A Randomized Approach for the Incremental Design of an Evolving Data Warehouse
ER '01 Proceedings of the 20th International Conference on Conceptual Modeling: Conceptual Modeling
Using abstract state machines for distributed data warehouse design
APCCM '04 Proceedings of the first Asian-Pacific conference on Conceptual modelling - Volume 31
Quality-Assured Design of On-Line Analytical Processing Systems using Abstract State Machines
QSIC '04 Proceedings of the Quality Software, Fourth International Conference
Balancing redundancy and query costs in distributed data warehouses
APCCM '05 Proceedings of the 2nd Asia-Pacific conference on Conceptual modelling - Volume 43
QSIC '06 Proceedings of the Sixth International Conference on Quality Software
Refinements in typed abstract state machines
PSI'06 Proceedings of the 6th international Andrei Ershov memorial conference on Perspectives of systems informatics
A formal approach to the design of distributed data warehouses
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
View integration and cooperation in databases, data warehouses and web information systems
Journal on Data Semantics IV
| Hi-index | 0.00 |
On-line analytical processing (OLAP) systems deal with analytical tasks in businesses. As these tasks do not depend on the latest updates by transactions, it is assumed that the data used in OLAP systems are kept in a data warehouse, which separates the input from operational databases from the outputs to OLAP. Typical OLAP queries are data intensive, and thus time consuming. In order to speed up the query computation, it is a common practice to materialize some of the computations as views based on a set of queries given. In general we wish to optimize query time under a given maintenance constraints. However, OLAP queries are not static and may change over time. Thus designing data warehouse is an ongoing task. This process is also called dynamic or incremental design. In this paper, we approach this issue as a refinement step in our Abstract State Machine (ASM) based data warehouse design, and support it by a set of standard refinement rules.