Prof. Chen Wei, Zhejiang University, China
Dr. Wei Chen is a professor in State Key Lab of CAD&CG
at Zhejiang University
, P.R.China. From June 2000 to June 2002, he was a joint Ph.D student in Fraunhofer Institute for Graphics
, Darmstadt, Germany and received his Ph.D degree in July 2002. His Ph.D advisors were Prof.Qunsheng Peng
, and Prof.Georgios Sakas
. From July. 2006 to Sep. 2008, Dr. Wei Chen was a visiting scholar at Purdue University, working in PURPL
with Prof.David S. Ebert
. In December 2009, Dr.Wei Chen was promoted as a full professor of Zhejiang University. He has performed research in visualization and visual analysis and published more than 30 IEEE/ACM Transactions and IEEE VIS papers. His current research interests include visualization, visual analytics and bio-medical image computing.
Prof. Qigang Gao, Dalhousie University, Canada
Dr. Qigang Gao is a professor of Computer Science at Dalhousie University, Canada. He received both PhD and MASc degrees from the University of Waterloo, Canada in 1993 and 1988 respectively. His research interests include Computer vision & Pattern recognition, Data mining and Data warehousing, Web-based intelligent information systems, and Cloud computing. He has supervised/co-supervised 52 graduate students at both PhD and Masters levels, and published (authored/co-authored) 110 peer-reviewed research papers & book articles in the related areas. He has also served on the organizing and technical program committees of many conferences, and served as reviewer as well. Speaker URL: https://web.cs.dal.ca/~qggao/
Prof. Dr. S.Vasavi, VR Siddhartha Engineering College, India
Dr S.Vasavi is working as a Professor in Computer Science & Engineering Department with 20 years of experience. She pursued her MS from BITS, Pilani and PhD from Acharya Nagarjuna University. She currently holds R&D projects from UGC and ISRO-ADRIN. She published 44 papers in various Scopus indexed, Google Scholar Indexed conferences and journals. She filed two patents. She is the recipient of UGC International travel grant in the year 2015, for her visit to ICOIP 2015,USA and TEQIP grant for her visit to ICICT 2016, Thailand. She is the Keynote speaker for the IEEE international conference ICOIP conducted at Singapore in July 2017. She visited reputed universities at U.S.A, Singapore and Thailand. She also Visited Argonne National Laboratory, A multidisciplinary Science and Engineering Research Center, that address vital national challenges in Clean Energy, Environment, Technology and National Security. She is an IEEE member, life member of Computer society of India (CSI), Member Machine Intelligence Research Labs , Washington , USA. Her Research Areas are Bigdata analytics: Image object Classification. Reviewer for Scopus Indexed journals [Inderscience, Image vision and computing, Bigdata Journal] and conferences [Springer, IEEE, Elsevier]. Received Best Teacher Award, Vishitta Mahila Award, Conferred Outstanding Women in Engineering Award.
Title: Framework for GeoSpatial Query Processing by Integrating Cassandra with Hadoop
Abstract: Now-a-days we are moving towards digitization and making all our devices such as sensors, cameras connected to Internet producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this work we mainly investigated on storage and retrieval of geospatial data by integrating Hadoop and Cassandra using Prefix-based partitioning and Cassandra’s default partitioning algorithm i.e Murmur3partitioner .techniques. Geohash value is generated, that acts as a partition key and also helps in effective search. Hence the time taken for retrieving data is optimized. When user requests for Spatial queries like finding nearest locations , searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results showed that Prefix-based partitioning technique is efficient than Murmur3 partitioning technique
Prof. Shihab A. Hameed, International Islamic University Malaysia, Malaysia
To be added..