Database Reference
In-Depth Information
Chapter 10
SENSING FOR MOBILE OBJECTS
Nicholas D. Larusso
UC Santa Barbara
Dept. of Computer Science
nlarusso@cs.ucsb.edu
Ambuj K. Singh
UC Santa Barbara
Dept. of Computer Science
ambuj@cs.ucsb.edu
Abstract
Recent advances in affordable positioning hardware and software have
made the availability of location data ubiquitous. Personal devices such
as tablet PCs, smart phones and even sport watches are all able to col-
lect and store a user's location over time, providing an ever-growing
supply of spatiotemporal data. Managing this plethora of data is a rel-
atively new challenge and there has been a great deal of research in the
recent years devoted to the problems that arise from spatiotemporal
data. This topic chapter surveys recent developments in the techniques
used for the management and mining of spatiotemporal data. We focus
our survey on three main areas: (i)
data management
, which includes
indexing and querying mobile objects, (ii)
tracking
, making use of noisy
location observations to infer an object's actual or future position, and
(iii)
mining
, extracting interesting patterns from spatiotemporal data.
First, we cover recent advances in database systems for managing spa-
tiotemporal data, including index structures and ecient algorithms for
processing queries. Next, we review the problem of tracking for mobile
objects to estimate an object's location given a sequence of noisy obser-
vations. We discuss some of the common approaches used for tracking
and examine some recent work which focuses specifically on tracking
vehicles using a road network. Then we review the recent literature on
mining spatiotemporal data. We conclude by discussing some interest-
ing areas of future research.
