Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Scalable and near real-time burst detection from eCommerce queries
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Intervention Events Detection and Prediction in Data Streams
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Predicting service request rates for adaptive resource allocation in SOA
Proceedings of the International Workshop on Enterprises & Organizational Modeling and Simulation
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Data mining to predict and prevent errors in health insurance claims processing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient approach for mining segment-wise intervention rules in time-series streams
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Interactive learning for efficiently detecting errors in insurance claims
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Design principles of massive, robust prediction systems
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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An important problem in data mining is detecting changes in large datasets. Although there are a variety of change detection algorithms that have been developed, in practice it can be a problem to scale these algorithms to large data sets due to the heterogeneity of the data. In this paper, we describe a case study involving payment card data in which we built and monitored a separate change detection model for each cell in a multi-dimensional data cube. We describe a system that has been in operation for the past two years that builds and monitors over 15,000 separate baseline models and the process that isused for generating and investigating alerts using these baselines.