Databases Reference
In-Depth Information
13.1.3
Mining Other Kinds of Data
In addition to sequences and graphs, there are many other kinds of semi-structured
or unstructured data, such as spatiotemporal, multimedia, and hypertext data, which
have interesting applications. Such data carry various kinds of semantics, are either
stored in or dynamically streamed through a system, and call for specialized data mining
methodologies. Thus, mining multiple kinds of data, including
spatial data, spatiotem-
poral data, cyber-physical system data, multimedia data, text data
,
web data
, and
data
streams
, are increasingly important tasks in data mining. In this subsection, we overview
the methodologies for mining these kinds of data.
Mining Spatial Data
Spatial data mining
discovers patterns and knowledge from spatial data. Spatial data,
in many cases, refer to geospace-related data stored in geospatial data repositories. The
data can be in “vector” or “raster” formats, or in the form of imagery and geo-referenced
multimedia. Recently, large
geographic data warehouses
have been constructed by inte-
grating thematic and geographically referenced data from multiple sources. From these,
we can construct
spatial data cubes
that contain spatial dimensions and measures, and
support
spatial OLAP
for
multidimensional spatial data analysis
. Spatial data mining can
be performed on spatial data warehouses, spatial databases, and other geospatial data
repositories. Popular topics on geographic knowledge discovery and spatial data min-
ing include
mining spatial associations and co-location patterns
,
spatial clustering
,
spatial
classification
,
spatial modeling
, and
spatial trend and outlier analysis
.
Mining Spatiotemporal Data and Moving Objects
Spatiotemporal data
are data that relate to both space and time.
Spatiotemporal data
mining
refers to the process of discovering patterns and knowledge from spatiotemporal
data. Typical examples of spatiotemporal data mining include discovering the evolution-
ary history of cities and lands, uncovering weather patterns, predicting earthquakes and
hurricanes, and determining global warming trends. Spatiotemporal data mining has
become increasingly important and has far-reaching implications, given the popular-
ity of mobile phones, GPS devices, Internet-based map services, weather services, and
digital Earth, as well as satellite, RFID, sensor, wireless, and video technologies.
Among many kinds of spatiotemporal data,
moving-object data
(i.e., data about mov-
ing objects) are especially important. For example, animal scientists attach telemetry
equipment on wildlife to analyze ecological behavior, mobility managers embed GPS
in cars to better monitor and guide vehicles, and meteorologists use weather satel-
lites and radars to observe hurricanes. Massive-scale moving-object data are becoming
rich, complex, and ubiquitous. Examples of
moving-object data mining
include mining
movement patterns of multiple moving objects
(i.e., the discovery of relationships among
multiple moving objects such as moving clusters, leaders and followers, merge, convoy,
swarm, and pincer, as well as other collective movement patterns). Other examples of
