Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Infomaster: an information integration system
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
The Representation Race - Preprocessing for Handling Time Phenomena
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Towards Information Agent Interoperability
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
A New Framework to Assess Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
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The End-User Access to Multiple Sources, the EAMS system integrates document collections in the internet (intranet) and relational databases by an ontology. The ontology relates the document with the database world and generates the items in the user interface. In both worlds, machine learning is applied. In the document world, a learning search engine adapts to user behavior by analysing the click-through-data. In the database world, knowledge discovery in databases (KDD) bridges the gap between the fine granularity of relational databases and the coarse granularity of the ontology. KDD extracts knowledge from data and therefore allows the knowledge management system to make good use of already existing company data.The EAMS system has been applied to customer relationship management in the insurance domain. Questions to be answered by the system concern customer acquisition (e.g., direct marketing), customer up and cross selling (e.g., which products sell well together), and customer retention (here: which customers are likely to leave the insurance company or ask for a return of a capital life insurance). Documents about other insurance companies and demographic data published in the internet contribute to the answers as do the results of data analysis of the company's contracts.