| 概要： |
With the upcoming widespread availability of sensors, more and more
applications depend on physical phenomena. Up-to-date real world
information is embedded into business processes, in production
environments, or in mobile applications, so that such context-aware
applications can adapt their behavior to the current situation of their
user or environment. Another example are so-called SCADA systems
(supervisory control and data acquisition), where complex installations
(e.g., energy grids or power plants) are monitored and controlled.
In general, data can be managed either by a database management system
(DBMS), or directly by the application. The first approach has many
advantages: information demands can be declared by queries and kept
separately from the application. When the information demand changes,
only the query has to be changed (which dramatically decreases software
maintenance costs). In addition, a DBMS can optimize the query
execution, so that the requested data is retrieved efficiently from the
systems.
However, if applications depend on real-world data, the amount of data,
the update rate, and low latency requirements often prevent the storage
in a DBMS. Thus, the data streams from the sensors are managed directly
by applications, with all its drawbacks.
The goal of data stream management is to provide the same flexibility
and data independence for data streams as for stored data.
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