Scalable and near real-time burst detection from eCommerce queries

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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval ? Information filtering; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval ? Clustering. General Terms Algorithms, Measurement, Design, Experimentation. Keywords Temporal burst mining, wavelet-based feature ext...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Nish Parikh, Neel Sundaresan
• Keywords: Data Mining, KDD 2008, Keywords Temporal Burst, Scale ECommerce System, Wavelet-Based Feature Extraction