Scalable and near real-time burst detection from ecommerce queries
N Parikh, N Sundaresan - Proceedings of the 14th ACM SIGKDD …, 2008 - dl.acm.org
N Parikh, N Sundaresan
Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008•dl.acm.orgIn 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 …
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 …
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.
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