Mining Data Streams - Mining of Massive Datasets - page 126

4.1.1 A Data-Stream-Management System

In analogy to a database-management system, we can view a stream processor as a kind
of data-management system, the high-level organization of which is suggested in Fig. 4.1 .
Any number of streams can enter the system. Each stream can provide elements at its own
schedule; they need not have the same data rates or data types, and the time between ele-
ments of one stream need not be uniform. The fact that the rate of arrival of stream elements
is not under the control of the system distinguishes stream processing from the processing
of data that goes on within a database-management system. The latter system controls the
rate at which data is read from the disk, and therefore never has to worry about data getting
lost as it attempts to execute queries.

Figure 4.1 A data-stream-management system

Streams may be archived in a large archival store , but we assume it is not possible to
answer queries from the archival store. It could be examined only under special circum-
stances using time-consuming retrieval processes. There is also a working store , into which
summaries or parts of streams may be placed, and which can be used for answering queries.
The working store might be disk, or it might be main memory, depending on how fast we
need to process queries. But either way, it is of sufficiently limited capacity that it cannot
store all the data from all the streams.

4.1.2 Examples of Stream Sources

Before proceeding, let us consider some of the ways in which stream data arises naturally.

Sensor Data

Imagine a temperature sensor bobbing about in the ocean, sending back to a base station a
reading of the surface temperature each hour. The data produced by this sensor is a stream
of real numbers. It is not a very interesting stream, since the data rate is so low. It would not