Database Reference
In-Depth Information
cause of the massive volume of the data which is received over time. A
special case of query processing in sensor data is that of event detection,
wherein continuous queries are posed on the sensor data in order to de-
tect the underlying events. The main challenge in event processing is
that the high level semantic events are often a complex function of the
underlying raw sensor data. In some cases, the event-query cannot be
posed exactly, since the event detection process is ambiguously related
to the underlying data. Methods for query processing of sensor data
are discussed in Chapter 3. Specialized methods for event processing of
sensor data are discussed in Chapter 4.
Mining Sensor Data
A variety of data mining methods such as clus-
tering, classification, frequent pattern mining, and outlier detection are
oftenappliedtosensordatainordertoextractactionableinsights.This
data usually needs to be compressed and filtered for more effective min-
ing and analysis. The main challenge is that conventional mining al-
gorithms are often not designed for real time processing of the data.
Therefore, new algorithms for sensor data stream processing need to
perform the analytics in a single pass in real time. In addition, the sen-
sor scenario may often require
in-network
processing, wherein the data is
processed to higher level representations before further processing. This
reduces the transmission costs, and the data overload from a storage
perspective. The problems of stream compression [3] and stream mining
are therefore tightly integrated together from an eciency perspective.
For example, compression and hidden variable modeling provides sum-
marized representations which can be leveraged for applications such as
forecasting and outlier analysis. A survey of methods for dimensional-
ity reduction, compression and filtering of sensor streams is provided in
Chapter 5. This chapter studies the issue of stream correlation analysis,
compression across streams in terms of hidden variables, and compres-
sion across time in a given stream. The application of these concepts to
a few stream mining problems is also studied in the same chapter. A
number of methods for real-time sensor stream mining, processing and
analytics are discussed in Chapters 6 and 7. Specific methods for mining
sensor streams in the distributed setting are presented in Chapter 8.
Social Sensing Applications and Mobile Data
The popularity of
mobile phones and other sensor-enabled devices has lead to a plethora
of “socially-aware data” which can be mined in the context of a wide
variety of applications. This trend has lead to the integration of sensors
and dynamic social networks. A number of architectural, privacy and
trust issues arise in the collection of socially aware sensor data. These
