Accurate Data Approximation in Constrained Environments
Accurate Data Approximation in Constrained Environments
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Date
2005-06-15
Authors
Deligiannakis, Antonios
Advisor
Roussopoulos, Nick
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Abstract
Several data reduction techniques have been proposed recently as
methods for providing fast and fairly accurate answers to complex
queries over large quantities of data. Their use has been widespread,
due to the multiple benefits that they may offer in several
constrained environments and applications. Compressed data representations
require less space to store, less bandwidth to communicate and can
provide, due to their size, very fast response times to
queries. Sensor networks represent a typical constrained environment,
due to the limited processing, storage and battery capabilities of the
sensor nodes.
Large-scale sensor networks require tight data handling and data
dissemination techniques. Transmitting a full-resolution data
feed from each sensor back to the base-station is often prohibitive
due to (i) limited bandwidth that may not be sufficient to sustain a
continuous feed from all sensors and (ii) increased power consumption
due to the wireless multi-hop communication. In order to minimize the
volume of the transmitted data, we can apply two well data reduction
techniques: aggregation and approximation.
In this dissertation we propose novel data reduction techniques for
the transmission of measurements collected in sensor network
environments. We first study the problem of summarizing multi-valued
data feeds generated at a single sensor node, a step necessary for the
transmission of large amounts of historical information collected at
the node. The transmission of these measurements may either be
periodic (i.e., when a certain amount of measurements has been
collected), or in response to a query from the base station. We then
also consider the approximate evaluation of aggregate
continuous queries. A continuous query is a query that runs
continuously until explicitly terminated by the user. These queries
can be used to obtain a live-estimate of some (aggregated) quantity,
such as the total number of moving objects detected by the sensors.