Building the data warehouse
Knowledge discovery in databases: an overview
AI Magazine
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Building Web applications with UML
Building Web applications with UML
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Integrating Data Mining with SQL Databases: OLE DB for Data Mining
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A Comparison between Query Languages for the Extraction of Association Rules
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Analysis of UML Stereotypes within the UML Metamodel
UML '02 Proceedings of the 5th International Conference on The Unified Modeling Language
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Ad-Hoc Association-Rule Mining within the Data Warehouse
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 8 - Volume 8
Mining Association Rules from Stars
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The Object Constraint Language: Getting Your Models Ready for MDA
The Object Constraint Language: Getting Your Models Ready for MDA
A UML profile for multidimensional modeling in data warehouses
Data & Knowledge Engineering - Special issue: ER 2003
Extending the UML for designing association rule mining models for data warehouses
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Toward data mining engineering: A software engineering approach
Information Systems
Interactive situation modelling in knowledge-intensive domains
International Journal of Business Information Systems
A UML profile for the conceptual modelling of data-mining with time-series in data warehouses
Information and Software Technology
An easy-to-implement fuzzy expert package with applications using existing Java classes
Expert Systems with Applications: An International Journal
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Information and Software Technology
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By using data mining techniques, the data stored in a Data Warehouse (DW) can be analyzed for the purpose of uncovering and predicting hidden patterns within the data. So far, different approaches have been proposed to accomplish the conceptual design of DWs by following the multidimensional (MD) modeling paradigm. In previous work, we have proposed a UML profile for DWs enabling the specification of main MD properties at conceptual level. This paper presents a novel approach to integrating data mining models into multidimensional models in order to accomplish the conceptual design of DWs with Association Rules (AR). To this goal, we extend our previous work by providing another UML profile that allows us to specify Association Rules mining models for DW at conceptual level in a clear and expressive way. The main advantage of our proposal is that the Association Rules rely on the goals and user requirements of the Data Warehouse, instead of the traditional method of specifying Association Rules by considering only the final database implementation structures such as tables, rows or columns. In this way, ARs are specified in the early stages of a DW project, thus reducing the development time and cost. Finally, in order to show the benefits of our approach, we have implemented the specified Association Rules on a commercial database management server.