13 Aug 2003
Evaluating Probabilistic Queries over Imprecise Data
Speaker: Reynold CHENG
Abstract
Many applications employ sensors for monitoring entities such as
temperature and wind speed. A centralized database tracks these entities
to enable query processing. Due to continuous changes in these values and
limited resources (e.g., network bandwidth and battery power), it is often
infeasible to store the exact values at all times. A similar situation
exists for moving object environments that track the constantly changing
locations of objects. In this environment, it is possible for database
queries to produce incorrect or invalid results based upon old data.
However, if the degree of error (or uncertainty) between the actual value
and the database value is controlled, one can place more confidence in the
answers to queries. More generally, query answers can be augmented with
probabilistic estimates of the validity of the answers. In this paper we
study probabilistic query evaluation based upon uncertain data. A
classification of queries is made based upon the nature of the result set.
For each class, we develop algorithms for computing probabilistic answers.
We address the important issue of measuring the quality of the answers to
these queries, and provide algorithms for efficiently pulling data from
relevant sensors or moving objects in order to improve the quality of the
executing queries. Extensive experiments are performed to examine the
effectiveness of several data update policies.
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