CS 260: Seminar on Spatial and Spatiotemporal Databases
Course Description:
In this course, we will discuss various issues arising in the context of
the management of spatial and spatiotemporal data. We will look exclusively at
readings from the research literature.
Prerequisites:
Students must have taken a course in data bases.
Class times:
Tuesdays & Thursdays 12:40pm - 2:00pm. The class meets in WCH 139.
Office hours:
By appointment. Tel: 827-5318
E-mail: ravi@cs.ucr.edu.
Grading:
Class participation: 40%, project: 60%.
Papers
Antonin Guttman: R-Trees: A Dynamic Index Structure for Spatial Searching.
SIGMOD Conference 1984: 47-57, R-tree.pdf
Efficient Processing of Spatial Joins Using R-Trees
Spatial Joins Using Seeded Trees
Spatial Hash Joins
Indexing Methods for Moving Object Databases: Games and Other Applications
UV-diagram: a voronoi diagram for uncertain spatial databases
Continuous Intersection Joins Over Moving Objects
Processing of extreme moving-object update and query workloads in main memory
Elite: an elastic infrastructure for big spatiotemporal trajectories
Efficient Anomaly Monitoring Over Moving Object Trajectory Streams
PIST: An Efficient and Practical Indexing Technique for Historical
Spatio-Temporal Point Data
Robust and Fast Similarity Search for Moving Object Trajectories
A query integrity assurance scheme for accessing outsourced spatial databases
Blind evaluation of location based queries using space transformation to
preserve location privacy
Mining, Indexing, and Querying Historical Spatiotemporal Data
Speed Partitioning for Indexing Moving Objects
Spatiotemporal Data Mining: A Computational Perspective
Primal or Dual: Which Promises Faster Spatiotemporal Search?
The Bdual-Tree: indexing moving objects by space filling curves in the dual
space
Using Efficient Polynomial Approximations for Spatiotemporal Trajectories