Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Bayesian modeling and classification of neural signals
Neural Computation
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Bursty and hierarchical structure in streams
Proceedings of the eighth 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
Proceedings of the 15th international conference on World Wide Web
Mining correlated bursty topic patterns from coordinated text streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting changes in large data sets of payment card data: a case study
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting changes in unlabeled data streams using martingale
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A software system for buzz-based recommendations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th international conference on World wide web
PET: a statistical model for popular events tracking in social communities
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Identification, Modelling and Prediction of Non-periodic Bursts in Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Mining named entities with temporally correlated bursts from multilingual web news streams
Proceedings of the fourth ACM international conference on Web search and data mining
Recommender systems at the long tail
Proceedings of the fifth ACM conference on Recommender systems
Early detection of buzzwords based on large-scale time-series analysis of blog entries
Proceedings of the 23rd ACM conference on Hypertext and social media
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
On the bursty evolution of online social networks
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
Integrating scale out and fault tolerance in stream processing using operator state management
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Chelsea won, and you bought a t-shirt: characterizing the interplay between Twitter and e-commerce
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Predicting event-relatedness of popular queries
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In large scale online systems like Search, eCommerce, or social network applications, user queries represent an important dimension of activities that can be used to study the impact on the system, and even the business. In this paper, we describe how to detect, characterize and classify bursts in user queries in a large scale eCommerce system. We build upon the approaches discussed in KDD 2002 "Bursty and Hierarchical Structure in Streams" [3] and apply them to a high volume industrial context. We describe how to identify bursts on a near real-time basis, classify them, and apply them to build interesting merchandizing applications.