OLAP-Based Data Mining for Business Intelligence Applications in Telecommunications and E-commerce
DNIS '00 Proceedings of the International Workshop on Databases in Networked Information Systems
Visual Data Mining for Business Intelligence Applications
WAIM '00 Proceedings of the First International Conference on Web-Age Information Management
OLAP-based Scalable Profiling of Customer Behavior
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
An OLAP-based Scalable Web Access Analysis Engine
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Dynamic self organizing maps for discovery and sharing of knowledge in multi agent systems
Web Intelligence and Agent Systems
SQL TVF Controlling Forms - Express Structured Parallel Data Intensive Computing
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
User Defined Partitioning - Group Data Based on Computation Model
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Online querying of d-dimensional hierarchies
Journal of Parallel and Distributed Computing
Brown Dwarf: A fully-distributed, fault-tolerant data warehousing system
Journal of Parallel and Distributed Computing
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Warehousing and mining sales transaction data to generate summary information, customer profiles, and business rules has become increasingly important in e-commerce. Such summary information and rules have to be extracted from very large collections of transaction data gathered at many distributed sites. This is challenging data mining, both in terms of the magnitude of data involved, and the need to incrementally adapt the mined patterns and rules as new data is collected. This paper describes a distributed and cooperative data warehousing, OLAP, and data mining infrastructure that addresses these challenges. Our contributions are as follows. First, we define various new classes of multi- dimensional and multi-level association rules (scoped multidimensional, with conjoint items, and functional) that can be extracted from customer profiles and are useful for e-commerce applications. Then, we show how customer profiles and different classes of association rules can be computed in a distributed, cooperative manner using OLAP tools. Finally, we show how the summaries, profiles, and rules can be incrementally updated as new transaction data is collected. This infrastructure has been prototyped at HP Labs.