Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
ACM Conference on Knowledge Discovery and Data Mining
Estimating the cardinality of unions and intersections of sets is a problem of interest in OLAP. Large data applications often require the use of approximate methods based on small sketches of the data. We give new estimators for the cardinality of unions and intersection and show they approximate an optimal estimation procedure. These estimators enable the improved accuracy of the streaming MinCount sketch to be exploited in distributed settings. Both theoretical and empirical results demonstrate substantial improvements over existing methods.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Barlas Oğuz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih