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Talks & Seminars
Title: Distance Bounds for Spatial Queries on Polyhedral Surfaces
Dr. Manohar Kaul, IIT-Hyderabad
Date & Time: November 26, 2015 11:00
Venue: Conference Room, ā€˜Cā€™ Block, 01st Floor, Dept. of CSE, Kanwal Rekhi (KReSIT) Bldg
Abstract:
The increasing availability of massive and accurate laser data enables the processing of spatial queries on terrains (e.g., k-nearest neighbor and reverse nearest neighbor queries). As shortest-path computation, an integral element of query processing, is inherently expensive on terrains, a key approach to enabling efficient query processing is to reduce the need for exact shortest-path computation in query processing. Two different distance bounds were proposed to speedup these spatial queries, where one of them depended on the quality of the underlying triangulation, while the other was a constant-ratio bound independent of the triangulation's quality. Use of these new bounds speeds up query processing by reducing the need for exact distance computations. Speedups of nearly an order of magnitude are demonstrated empirically for well-known spatial queries.
Speaker Profile:
Manohar Kaul received his B.Engg (Honors) and Masters degrees from Latrobe University, Australia and Uppsala University, Sweden, respectively. He also worked in industry for nearly 8 years, primarily for Oracle as a senior systems/ database architect, specializing in handling very large datasets for mission-critical applications, especially in the Utilities, Banking and Telecommunication sectors. In 2014, he received his Ph.D. from Aarhus University, Denmark under the supervision of Prof. C.S. Jensen. He later worked for a year at DIMA in TU-Berlin as a postdoc under Prof. Volker Markl, designing scalable machine learning methods for Apache Flink, an open-source platform for distributed stream and batch processing. He has published papers at top-tier database conferences and has also received a Best-paper award at MDM'2013 (a targetted spatial conference). From July this year, he joined IIT-Hyderabad as a visiting faculty. His research interests include spatial transformations/indexing methods, scalable machine learning and large scale distributed matrix computations.
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