Identifying similarities, periodicities and bursts for online search queries

By Michail Vlachos, Christopher Meek, Zografoula Vagena, Dimitrios Gunopulos. 2004

294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Parameter Free Bursty Events Detection in Text Streams (2005, methodology): “[22] proposes methods for mining knowledge from the query logs of the MSN search engine by building a time-series for each query term, where the elements of the time-series are the number of times that a query is issued on a day.”
Dynamical classes of collective attention in twitter (2011, background): “Public attention can suddenly concentrate on a Web page or application [1, 2, 3, 4, 5, 6], a Youtube video [7, 8, 9], a trending topic in Twitter [10, 11, 12], or on a story in the news media [13], sometimes even in absence of an apparent reason.”
Conversational tagging in twitter (2010, methodology): “[11] focused on burst and periodic queries, representing them concisely using coefficients in a Fourier transform.”
A review on time series data mining (2011, methodology): “Vlachos et al. (2004) apply power spectral density estimation using DFT and tree index to discover important periods.”
Towards recency ranking in web search (2010, methodology): “Existing methods for detecting such topics typically involve tracking the frequency of the topic over discrete time slots, and detecting irregularities such as a large divergence from the mean number of occurrences or a sudden burst of activity [13, 20].”
A word at a time: computing word relatedness using temporal semantic analysis (2011, methodology): “More similar to our work, but in the context of analyzing temporal search engine query logs (which often exhibit strong temporal regularities [4]), some work [8, 25, 39] proposed a method for detecting semantically similar queries through temporal correlation analysis.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, unknown): “This would result in a sort of signal to which standard temporal series techniques may be applied [59, 85, 222, 223, 243].”
Modeling and predicting behavioral dynamics on the web (2012, methodology): “[20] were among the first to examine and model periodicities and bursts in Web queries using methods from Fourier analysis.”
Evolutionary clustering (2006, background): “propose a Fourier approach to this problem [15].”