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192 Selected Studies on Software and Information Systems
wrappers are often insufficient to perform this task. The problem of having high quality
ontologies influences the Semantic web generally. Hence, this is not only the problem of
using SmartWeb in different domains and situations. Some problems concerning this topic
are discussed in [9] which deals with creation of ontological test bases for experimental
evaluation of Semantic web applications.
Even if we have already described the goal in an acceptable form, there are still other
problems. Despite of existing web service composition tools, the construction of an optimal
composite web service can not be always achieved in sufficiently short time even if the
necessary sub-services are available. This is due the complexity of this task (discussed in
section 6.3.2).
When composite web service is constructed, other services are required to be organized
into one. Only available web services which are able to be executed can be taken into
account. Here the problems of discovering suitable services mentioned in section 6.3.1 must
be considered. The composition method must also deal with situation when it is not possible
to build a composite service, which fully satisfies the user goal.
The web services can be considered as transformations, which taking a set of input parameters,
produce a set of output parameters. These can be described as ws :(i 1 ,...,i n ) −→
(o 1 ,...,o m ). Before the constructed composite web service can be executed, the input parameters
must be provided. The problem is that the user only defines the output parameters.
It is not clear how to define the input parameters if we have described only the outputs. In
example from Figure 6-12 the goal is to create forecast maps for a defined area and term.
Based on this the presented composite service is constructed. This service composition is
only the scheme which says in which order the services will be called and how will they shift
the data between themselves.
It is clear that the input for the Compute forecast service is the output of the Create meteo
model service. The input for the Create meteo forecast maps service is the output of the Compute
forecast service. The problem is that the inputs of the first, Create meteo model service are
not provided by other service(s). From the description of the service only the type of the
input parameters are known. The concrete input data for this service must be defined and
provided other way. Automatic definition of these data presents a hard problem. There is
no universal method how to do this. It is a domain dependent problem and often domain
expert knowledge is required for this task. In the meteorological forecast map example,
a meteorologist must define different parameters required to create a meteorological model
from which the forecast for the specified time and area can be generated. Hence, the definition
of the input data is hard to realize in automatic manner or even impossible.
One possible solution may be the description of the data transformation the service
performs. This means that we know what output parameters will be produced by a service if
we know the input. For example a description of the web service which multiplies the input
with two must explain that this service doubles the input. This still not solve the problem. The
problem is solved only if we know also the inverse transformation ws −1 : (o 1 ,...,o m ) −→
(i 1 ,...,i n ). In our example it means that the service description stands that the inverse
transformation is the division by two. If this information is available, we are able to determine
the required input parameters based on the output parameters. Problem is that it is not always
possible to define the inverse function. An example is the service realizing addition of two
numbers. The addition has no inverse function. Solutions for this case are not known.