Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining Meets Performance Evaluation: Fast Algorithms for Modeling Bursty Traffic
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Detecting Lasting and Abrupt Bursts in Data Streams Using Two-Layered Wavelet Tree
AICT-ICIW '06 Proceedings of the Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services
Online Burst Detection Over High Speed Short Text Streams
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Fast burst correlation of financial data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Adaptively detecting aggregation bursts in data streams
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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Opt-in phone calls or emails refer to promotional phone calls or emails that have been requested by the people receiving them. In this paper we propose a model based on dynamic sliding windows to detect opt-in phone calls based on mobile phone call detail records. This work is useful for detecting unwanted calls (e.g., spam) and commercial purposes. For validation of our results, we used actual call logs of 100 users collected at MIT by the Reality Mining Project group for a period of 8 months. The experimental results show that our model achieves good performance with 91% accuracy.