Database Reference
In-Depth Information
om:Observation: id =
CIMIS S33 ETO
Description:
Observation with remote streaming result
name =
CIMIS STATION 33 ETO
TimePeriod
beginPosition:
2008-02-10T12:00:00:00
endPosition:
future
Result
xlink:href=
“http://comet.ucdavis.edu:9090/CIMIS/?ch=/S33/eto”
xlink:role=
“application/octect-stream”
Figure 10.9
Example of an O&M observation response.
entry point for all incoming stream data sources. This intermediate component
would allow the implementation of various possible connections. An RBNB
system
79
can be used as the entry point. In this case, the hyperlink shown
in Figure 10.9 points directly to the corresponding channel in the RBNB
server. However, various other types of connections are possible, including a
TransducerML (TML) stream as a wrapper for the original, native stream,
a TML stream as a wrapper for the RBNB stream, and the RBNB stream
directly. The capabilities document would advertise the supported connection
types so a client application can choose the one it is able to use.
In summary, as shown in Figure 10.7, right, both the prediction and the
real-time ETo values can be displayed in chart form next to the respective
station locations, and showed in the context of eco-regions (vector data) and
current ETo maps (raster data), thus providing users and scientists with a
vivid interface to monitor ETo values and easily compare them with weather
model prediction over time.
It finally should be noted that the integration platform outlined above pro-
vides transparent and uniform access to heterogeneous geospatial data sources.
However, in general, certain integration tasks such as resolving structural and
semantic heterogeneities (see Section 10.2.4) still need to be explicitly real-
ized at the client side and integration platform. These tasks include match-
ing vector-based objects from two or more sources, selecting respective non-
spatial attributes, and resolving general conflation aspects among data to
be integrated. A viable approach to support such tasks is through scientific
workflows (see Chapter 13), where the logic of conflict-resolving techniques is
implemented in the form of actors.
10.5 Conclusions
With the amount of geospatial data growing at unprecedented rates, its ef-
fective sharing, exchange, and integration becomes a more critical necessity
than ever before. We have seen that this goal involves not only dealing with
