Public Seminar by Prof Hanan Samet, University of Maryland

Who: Prof Hanan Samet, Department of Computer Science, University of Maryland.
When: Thursday 22nd March, 11:00 am – 12:00 pm.
Where: PAR-Old Geology-G04 (Theatre 1), The University of Melbourne
Moving object databases arise in numerous applications such as traffic monitoring, crowd tracking, and games.  They all require keeping track of objects that move and thus the database of objects must be constantly updated.  The cover fieldtree (more commonly known as the loose quadtree and the loose octree, depending on the dimension of the underlying space) is designed to overcome the drawback of spatial data structures that associate objects with their minimum enclosing quadtree (octree) cells which is that the size of these cells depends more on the position of the objects and less on their size.  In fact, the size of these cells may be as large as the entire space from which the objects are drawn.  The loose quadtree (octree) overcomes this drawback by expanding the size of the space that is spanned by each quadtree (octree) cell c of width w by a cell expansion factor p (p>0) so that the expanded cell is of width (1+p)*w and an object is associated with its minimum enclosing expanded quadtree (octree)
cell. It is shown that for an object o with minimum bounding hypercube box b of radius r (i.e., half the length of a side of the hypercube), the maximum possible width w of the minimum enclosing expanded
quadtree cell c is just a function of r and p, and is independent of the position of o.  Normalizing w via division by 2r enables calculating the range of possible expanded quadtree cell sizes as a function of p.  For p>= 0.5 the range consists of just two values and usually just one value for p>=1.  This makes updating very simple and fast as for p >= 0.5, there are at most two possible new cells associated with the moved object and thus the update can be done in O(1) time.  Experiments with random data showed that the update time to support motion in such an environment is minimized when p is infinitesimally less than 1, with as much as a one order of magnitude increase in the number of updates that can be handled vis-a-vis the p=0 case in a given unit of time.  Similar results for updates were obtained for an N-body simulation where improved query performance and scalability were also observed.  Finally, in order amplify the result, a video titled “Crates and Barrels” is available which is an N-body simulation of 14,000 objects.  The video as well as a JAVA applet that illustrates the behavior of the loose quadtree are both available from here.
* Appeared in the Proceedings of the {ACM SIGMOD  Conference, pp. 169–180, New York, June 2013
** Joint work with Jagan Sankaranarayanan and Michael Auerbach
Hanan Samet is a Distinguished University Professor of Computer Science at the University of Maryland, College Park and is a member of the Institute for Computer Studies.  He is also a member of the Computer Vision Laboratory at the Center for Automation Research where he leads a number of research projects on the use of hierarchical data structures for database applications involving spatial data.  He has a Ph.D from Stanford University.  His doctoral dissertation dealt with proving the correctness of translations of LISP programs which was the first work in translation validation and the related concept of proof carrying code.  He is the author of the recent book “Foundations of Multidimensional and Metric Data Structures” published by Morgan-Kaufmann, San Francisco, CA, in 2006, an award winner in the 2006 best book in Computer and Information Science competition of the Professional and Scholarly Publishers (PSP) Group of the American Publishers Association (AAP), and of the first two books on spatial data  structures titled “Design and Analysis of Spatial Data Structures” and “Applications of Spatial Data Structures:  Computer Graphics, Image Processing and GIS” published by Addison-Wesley, Reading, MA, 1990.  He is the Founding Editor-In-Chief of the ACM Transactions on Spatial Algorithms and System (TSAS), the founding chair of ACM SIGSPATIAL, a recipient of the 2009 UCGIS Research Award, 2011 ACM Paris Kanellakis Theory and Practice Award, the 2010 CMPS Board of Visitors Award at the University of Maryland, the 2014 IEEE Computer Society Wallace McDowell Award, and a Fellow of the ACM, IEEE, AAAS, IAPR (International Association for Pattern Recognition), and UCGIS (University Consortium for Geographic Science).  He received best paper awards in the 2007 Computers & Graphics Journal,  the 2008 ACM SIGMOD and SIGSPATIAL ACMGIS Conferences, the 2012 SIGSPATIAL MobiGIS Workshop, and the 2013 SIGSPATIAL GIR Workshop, as well as a best demo paper award at the 2011 and 2016 SIGSPATIAL ACMGIS Conferences.  His paper at the 2009 IEEE International Conference on Data Engineering (ICDE) was selected as one of the best papers for publication in the IEEE Transactions on Knowledge and Data Engineering.  He was elected to the ACM Council as the Capitol Region Representative for the term 1989-1991, and was an ACM Distinguished Speaker for the term 2008-2015.