Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Next-Cut: a second generation framework for concurrent engineering
Proceedings of the MIT-JSME workshop on Computer-aided cooperative product development
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Consistency Algorithms for Multi-Source Warehouse View Maintenance
Distributed and Parallel Databases - Special issue on parallel and distributed information systems
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Designing data marts for data warehouses
ACM Transactions on Software Engineering and Methodology (TOSEM)
Efficient intensional redefinition of aggregation hierarchies in multidimensional databases
Proceedings of the 4th ACM international workshop on Data warehousing and OLAP
A foundation for capturing and querying complex multidimensional data
Information Systems - Data warehousing
Building the Data Warehouse,3rd Edition
Building the Data Warehouse,3rd Edition
A multidimensional and multiversion structure for OLAP applications
Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
Schema evolution in data warehouses
Knowledge and Information Systems
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Conceptual Design of Data Warehouses from E/R Schema
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Automated data warehousing for rule-based CRM systems
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Maintaining Data Cubes under Dimension Updates
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Parallel multisource view maintenance
The VLDB Journal — The International Journal on Very Large Data Bases
Creation and management of versions in multiversion data warehouse
Proceedings of the 2004 ACM symposium on Applied computing
On querying versions of multiversion data warehouse
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
A personalization framework for OLAP queries
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
A Knowledge-driven Data Warehouse Model for Analysis Evolution
Proceedings of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering
RoK: Roll-Up with the K-Means Clustering Method for Recommending OLAP Queries
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
| Hi-index | 0.00 |
Data warehouses store aggregated data issued from different sources to meet users' analysis needs for decision support. The nature of the work of users implies that their requirements are often changing and do not reach a final state. Therefore, a data warehouse cannot be designed in one step, usually it evolves over the time. In this paper, we propose a user-driven approach that enables a data warehouse schema update. It consists in integrating the users' knowledge in the data warehouse modeling to allow new analysis possibilities. More precisely, we consider the specific users' knowledge, which defines new aggregated data, under the form of "if-then" rules that we call aggregation rules. These rules are used to dynamically create new granularity levels in dimension hierarchies, following an automatic and concurrent way. Our approach is composed of four phases: (1) users' knowledge acquisition, (2) knowledge integration, (3) data warehouse schema update, and (4) on-line analysis. To support our approach, we define a Rule-based Data Warehouse (R-DW) model composed of two parts: one "fixed" part and one "evolving" part. The fixed part corresponds to the initial data warehouse schema, whose purpose is to provide an answer to global analysis needs. The evolving part is defined by means of aggregation rules, which allow personalized analyses. To validate our approach, we developed a prototype called WEDriK (data Warehouse Evolution Driven by Knowledge), in which the R-DW model is implemented within the Oracle 10g DBMS. We also present how to achieve our approach by proposing a model dedicated to the management of the data warehouse schema evolution and the updates' algorithms. Furthermore, we applied our approach on banking data of the French bank LCL-Le Crédit Lyonnais and we illustrate our purpose with the LCL case study.