Incremental quantile estimation for massive tracking
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Emerging scientific applications in data mining
Communications of the ACM - Evolving data mining into solutions for insights
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
Efficient mining method for retrieving sequential patterns over online data streams
Journal of Information Science
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Online mining of frequent sets in data streams with error guarantee
Knowledge and Information Systems
Frequency-based load shedding over a data stream of tuples
Information Sciences: an International Journal
Interactive refinement of filtering queries on streaming intelligence data
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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Transaction data can arrive at a ferocious rate in the order that transactions are completed. The data contain an enormous amount of information about customers, not just transactions, but extracting up-to-date customer information from an ever changing stream of data and mining it in real-time is a challenge. This paper describes a statistically principled approach to designing short, accurate summaries or signatures of high dimensional customer behavior that can be kept current with a stream of transactions. A signature database can then be used for data mining and to provide approximate answers to many kinds of queries about current customers quickly and accurately, as an empirical study of the calling patterns of 96,000 wireless customers who made about 18 million wireless calls over a three month period shows.