An overview of data warehousing and OLAP technology
ACM SIGMOD Record
The Clio project: managing heterogeneity
ACM SIGMOD Record
Content integration for e-business
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Schema Mapping as Query Discovery
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Database Schema Matching Using Machine Learning with Feature Selection
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Generating EDI Message Translations from Visual Specifications
Proceedings of the 16th IEEE international conference on Automated software engineering
Metadata Management and Data Warehousing
Metadata Management and Data Warehousing
International Journal of Metadata, Semantics and Ontologies
MurO: a multi-representation ontology as a foundation of enterprise information systems
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
A matching algorithm for electronic data interchange
TES'05 Proceedings of the 6th international conference on Technologies for E-Services
International Journal of Intelligent Information and Database Systems
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Data warehousing is an essential element of decision support. It aims at enabling the knowledge user to make better and faster daily business decisions. In order to supply a decisional database, meta-data is needed to enable the communication between various function areas of the warehouse and an ETL tool (Extraction, Transformation, and Load) is needed to define the warehousing process. The developers use a mapping guideline to specify the ETL tool with the mapping expression of each attribute. In this paper, we will define a model covering different types of mapping expressions. We will use this model to create an active ETL tool. In our approach, we use queries to achieve the warehousing process. SQL queries will be used to represent the mapping between the source and the target data. Thus, we allow DBMS to play an expanded role as a data transformation engine as well as a data store. This approach enables a complete interaction between mapping meta-data and the warehousing tool. In addition, this paper investigates the efficiency for a Query-based data warehousing tool. It describes a query generator for reusable and more efficient data warehouse (DW) processing. Besides exposing the advantages of this approach, this paper shows a case study based on real scale commercial data to verify our tool features.