Managing Uncertainty in Moving-Object and Sensor Databases

Speaker:        Reynold Cheng
                Department of Computer Science
                Purdue University
                USA

Topic:          "Managing Uncertainty in Moving-Object and Sensor Databases"
                Wednesday, 7 July 2004

Time:           3:00 pm - 4:00 pm

Venue:          Lecture Theatre F
                (Leung Yat Sing Lecture Theatre, near lift nos. 25/26)
                HKUST

ABSTRACT:

In a moving-object database system, locations of objects are constantly
reported to the database.  These location values are subsequently used to
answer user queries.  Due to continuous changes in locations, as well as
limited resources (e.g., network bandwidth and battery power), it is
infeasible for the database to keep track of the actual location of every
moving object. Queries that use the stale values provided by the database
can produce incorrect answers.  However, if the degree of uncertainty
between the actual location value and the database value is limited, one
can place more confidence in answers to the queries. More generally, query
answers can be augmented with probabilistic guarantees of the validity of
the answers.  To answer these probabilistic queries, different solutions
are required, depending on the moving pattern of an object, as well as the
nature of the query. We first investigate the interesting issue of
modeling ``uncertainty'' for a moving-object.  Based on the uncertainty
models, we will discuss how uncertain location data can be queried, where
algorithms for range queries and nearest-neighbor queries will be
presented.

We observe that uncertainty management techniques for a moving-object
database can be generalized to a vast class of sensor-based applications,
which typically involve the monitoring of continuously changing entities
(e.g., temperature and pressure).  I will talk about a taxonomy of
uncertainty models for general sensor data. These classes of models
describe uncertainty in different levels of precision. Next, we will
present a classification scheme for probabilistic queries for uncertain
sensor data, and briefly discuss how probabilistic queries are executed in
each class. We also examine the important issue of measuring the quality
of answers to probabilistic queries.


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Biography:

Reynold Cheng received the B.Eng. degree in Computer Engineering and the
M.Phil degree in Computer Science from the University of Hong Kong, in
1998 and 2000, respectively.  He received his MSc degree in Computer
Science in Purdue University in 2003, and is currently a PhD candidate in
Purdue University.  His research interests include uncertainty management
in sensor streams and moving-object databases, and concurrency control in
real-time databases.