| Abstract |
In recent years, we have witnessed the emergence of a new class of
applications that must deal with large volumes of streaming data.
Examples include financial data analysis on feeds of stock tickers,
sensor-based environmental monitoring, and network traffic monitoring.
Traditional database management systems (DBMS) which are very good at
managing large volumes of stored data, fall short in serving this new
class of applications, which require low-latency processing on live data
from push-based sources. Aurora is a data stream management system (DSMS)
that has been developed to meet these needs.
A DSMS such as Aurora may be subject to higher input rates than its
resources can handle. When input rates exceed system capacity, the system
will become overloaded and Quality of Service (QoS) at system outputs
will fall below acceptable levels. Under these conditions, the system
will shed load by selectively dropping tuples, thus degrading the answer,
in order to improve the observed latency of the results. In this talk,
will present a load shedding framework for data stream management systems
which handles the overload problem in a light-weight manner, while
minimizing the loss in result accuracy and guaranteeing subset results at
query outputs.
One of the triggering factors behind the data stream processing research
has been the rapid development in sensor-based technologies and
applications. In my talk, I will also briefly describe a real sensor
network application. I will show that significant resource efficiency
can be achieved through other forms of load management on streaming
sensor data. |
