Wednesday, April 09, 2014, 10:00am
Associate Professor & Reader, University of Melbourne
"MELODY-JOIN: Efficient Earth Mover’s Distance Similarity Joins using MapReduce"
Abstract: The Earth Mover’s Distance (EMD) similarity join retrieves pairs of records with EMD below a given threshold. It has a number of important emerging applications such as near duplicate image retrieval and pattern recognition. However, the computational cost of EMD is super cubic to the number of bins in a histogram that is used to represent a data record. Therefore the EMD similarity join operation is prohibitive for large datasets. This is the first paper that specifically addresses EMD similarity join at large scale and we propose a MapReduce based framework named MELODY-JOIN to approach this problem. Our solution beats an alternative by an order of magnitude.
Bio: Prof. Rui Zhang is an Associate Professor and Reader at the University of Melbourne and Assistant Dean (Collaboration) of Melbourne School of Engineering. He has been awarded the prestigious Future Fellowship by the Australian Research Council in 2012. He obtained his Bachelor's degree from Tsinghua University in 2001 and PhD from National University of Singapore in 2006. He has been a visiting scholar in AT&T Labs-Research and Microsoft Research before and is now a regular visiting researcher at Microsoft Research Asia in Beijing. He has authored over 60 publications in prestigious conferences and journals. His research interest is spatial and temporal data analytics, as well as general database and mining techniques including indexing, moving object management, data streams and sequence databases. He regularly serves as PC members of top conferences in data management and mining such as SIGMOD, VLDB, ICDE and KDD. He is an associate editor of Distributed and Parallel Databases.
Hosted by: EECS Prof. Goce Trajcevski & CS Division