Foundations of statistical natural language processing
Foundations of statistical natural language processing
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Learning to detect events with Markov-modulated poisson processes
ACM Transactions on Knowledge Discovery from Data (TKDD)
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Mining the blogosphere for top news stories identification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Linear time series models for term weighting in information retrieval
Journal of the American Society for Information Science and Technology
Learning recurrent event queries for web search
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
Detecting seasonal queries by time-series analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Strategy in action: analyzing online search behavior bymining search strategies
Proceedings of the 7th ACM international conference on Web search and data mining
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
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The massive volume of queries submitted to major Web search engines reflects human interest at a global scale. While the popularity of many search queries is stable over time or fluctuates with periodic regularity, some queries experience a sudden and ephemeral rise in popularity that is unexplained by their past volumes. Typically the popularity surge is precipitated by some real-life event in the news cycle. Such queries form what are known as search trends. All major search engines, using query log analysis and other signals, invest in detecting such trends. The goal is to surface trends accurately, with low latency relative to the actual event that sparked the trend. This work formally defines precision, recall and latency metrics related to top-k search trend detection. Then, observing that many trend detection algorithms rely on query counts, we develop a linear auto-regression model to predict future query counts. Subsequently, we tap the predicted counts to expedite search trend detection by plugging them into an existing trend detection scheme. Experimenting with query logs from a major Web search engine, we report both the stand-alone accuracy of our query count predictions, as well as the task-oriented effects of the prediction on the emitted trends. We show an average reduction in trend detection latency of roughly twenty minutes, with a negligible impact on the precision and recall metrics.