Name
Affiliation
Papers
SRIVATSAN LAXMAN
Microsoft Research, India
26
Collaborators
Citations 
PageRank 
39
421
21.65
Referers 
Referees 
References 
878
555
344
Search Limit
100878
Title
Citations
PageRank
Year
Discovering and understanding word level user intent in Web search queries50.462015
Verito: A Practical System for Transparency and Accountability in Virtual Economies.00.342013
Improved multiple sequence alignments using coupled pattern mining10.352013
Ad impression forecasting for sponsored search40.402013
Error Correction in Learning using SVMs20.382013
A general streaming algorithm for pattern discovery.40.412013
Streaming Algorithms for Pattern Discovery over Dynamically Changing Event Sequences30.412012
A unified view of the apriori-based algorithms for frequent episode discovery250.882012
Efficient Episode Mining of Dynamic Event Streams90.522012
An IR-based evaluation framework for web search query segmentation130.622012
Discovering injective episodes with general partial orders150.672012
The State of Data Privacy.00.342011
ZDVUE: prioritization of javascript attacks to discover new vulnerabilities40.432011
A Learning Framework for Self-Tuning Histograms00.342011
Discovering excitatory relationships using dynamic Bayesian networks90.522011
Unsupervised query segmentation using only query logs220.822011
Noiseless database privacy130.622011
Lexical co-occurrence, statistical significance, and word association90.522010
Discovering frequent patterns in sensitive data933.322010
Discovering general partial orders in event streams00.342009
Inferring Dynamic Bayesian Networks using Frequent Episode Mining20.442009
Temporal data mining for root-cause analysis of machine faults in automotive assembly lines10.392009
Stream prediction using a generative model based on frequent episodes in event sequences341.382008
A fast algorithm for finding frequent episodes in event streams682.592007
Discovering Frequent Generalized Episodes When Events Persist for Different Durations331.592007
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection522.582005