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The need for integration of all types of client and server applications that were not initially designed to interoperate is gaining popularity. One of the reasons for this popularity is the capability to quickly reconfigure a composite application for a task at hand, both by changing the set of components and the way they are interconnected. Service-Oriented Architecture (SOA) has recently become a popular platform in the IT industry for building such composite applications with the integrated components being provided as Web services. A key limitation of solely Web-service-based integration is that it requires extra programming efforts when integrating non-Web service components, which is not cost-effective. Moreover, with the emergence of new standards, such as Open Service Gateway Initiative (OSGi), the components used in composite applications have grown to include more than just Web services. Our work enables progressive composition of non-Web-service-based components such as portlets, Web applications, native widgets, legacy systems, and Java Beans. Further, we proposed a novel application of semantic annotation together with the standard semantic Web matching algorithm for finding sets of functionally equivalent components out of a large set of available non-Web-service-based components. Once such a set is identified, the user can drag and drop the most suitable component into an Eclipse-based composition canvas. After a set of components has been selected in such a way, they can be connected by data-flow arcs, thus forming an integrated, composite application without any low-level programming and integration efforts. We implemented and conducted extensive experimental study on the above progressive composition framework on IBM's Lotus Expeditor, an extension of an SOA platform called the Eclipse Rich Client Platform (RCP) that complies with the OSGi standard.
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Semantic-based Mashup of Composite
Anne H.H. Ngu, Michael P. Carlson, Quan Z. Sheng, Member, IEEE, and Hye-young Paik
Abstract—The need for integration of all types of client and server applications that were not initially designed to interoperate is gaining
popularity. One of the reasons for this popularity is the capability to quickly reconfigure a composite application for a task at hand, both
by changing the set of components and the way they are interconnected. Service Oriented Architecture (SOA) has recently become
a popular platform in the IT industry for building such composite applications with the integrated components being provided as Web
services. A key limitation of solely Web service based integration is that it requires extra programming efforts when integrating non
Web service components, which is not cost-effective. Moreover, with the emergence of new standards, such as Open Service Gateway
Initiative (OSGi), the components used in composite applications have grown to include more than just Webservices. Our work enables
progressive composition of non Web service based components such as portlets, web applications, native widgets, legacy systems,
and Java Beans. Further, we proposed a novel application of semantic annotation together with the standard semantic web matching
algorithm for finding sets of functionally equivalent components out of a large set of available non Web service based components.
Once such a set is identified the user can drag and drop the most suitable component into an Eclipse based composition canvas. After
a set of components has been selected in such a way, they can be connected by data-flow arcs, thus forming an integrated, composite
application without any low level programming and integration efforts. We implemented and conducted extensive experimental study
on the above progressive composition framework on IBM’s Lotus Expeditor, an extension of a SOA platform called the Eclipse Rich
Client Platform (RCP) that complies with the OSGi standard.
Index Terms—Mashup, composite applications, Web services, semantic Web, semantic annotation.
COmposite applications are a line of business applications
constructed by connecting, or wiring, disparate software
components into combinations that provide a new level of
function to an end user without the requirement to write
any new code. The components that are used to build a
composite application are generally built within a Service
Oriented Architecture (SOA). Many of the first SOA platforms
exclusively relied on Web services (WSDL-based) as compo-
nents in the composite application. The composition is done
generally using process based languages such as BPEL [1].
The Web-service-only integration framework requires extra
programming efforts when integrating with non-Web service
components, which is not cost effective. With the emergence
of new standards, such as Open Service Gateway Initiative
(OSGi) [2], the components used in composite applications
have grown to include more than just Web services. Com-
ponents can be built from web applications, portlets, native
widgets, legacy systems, and Java Beans. There are several
challenges to mashing up non-Web service components into
composite applications, especially when components are de-
veloped at different times, by different groups, using different
technologies, naming conventions, and structures.
Firstly, a given enterprise may have hundreds of similar
components available for mashup in a catalog, but manually
searching and finding compatible and complementary com-
Anne H.H. Ngu is with the Department of Computer Science, Texas State
University, TX 78666-4616, USA. E-mail:
Michael P. Carlson is with IBM, Quan Z. Sheng is with the University of
Adelaide, Hye-young Paik is with the University of New South Wales.
ponents could be a tedious and time consuming task. For
example, in a portal environment, it is possible to query
the system for the available components. However, the list
returned is typically based on criteria that have no relevance
to the application assembler (e.g., in alphabetical or last
update time). Interfaces like Google Code Search [3] allow
the developers to search application code, but it does not allow
them to search using the higher level concepts of a component
or a model. On the other end of the spectrum, having to
manually classify and describe every aspect of components
for browsing and searching can be a painstaking task when
handling a large number of components.
Secondly, none of the existing OSGi environments provides
a way to leverage the semantic search techniques that have
been developed to assist users in locating compatible com-
ponents like in Web service-based composite applications.
Unlike Web services, many non-Web service components
have graphical user interfaces built from technologies such
as portlets, Eclipse Views, and native application windows.
Moreover, there is currently no standard way of categorizing
and cataloging components for use in a composite application.
Rather, components are discovered by assemblers who must
hunt around the Web, in documentation, and searching the
locally installed system. This does not provide an easy and
manageable means of finding and selecting components. De-
pending on the technology used or the type of user interface
being presented, certain components may not be valid for use
in a particular composite application. Discerning this could be
a tedious process up front, or could result in repeated cycles of
trial and error, especially when the target environment supports
a variety of technologies.
After suitable components have been discovered, the as-
sembly of composite applications should not require tedious
and detailed programming as required of a typical software
developer. Users, at least the savvier users, should be able
to compose applications with minimal training. For a call
center in an enterprise, this may mean being able to assemble
a composite application on the fly. This may involve, for
instance, extracting a piece of caller’s information from one
application and feeding it as input to other applications that
are currently running on her desktop for other contextual
information that might help in answering pressing queries.
For a user in a small business, this may mean assembling
a GPS routing application together with the list of errands or
deliveries for the day and producing a more optimized route.
We have developed a novel approach that enables progres-
sive composition of non Web service based components such
as portlets, web applications, native widgets, legacy systems,
and Java Beans. Our approach exploits semantic annotation
together with the standard semantic web matching algorithm
for finding sets of functionally equivalent components out of
a large set of available non Web service based components.
The identified components can be connected by data-flow
arcs in an Eclipse based composition canvas, thereby forming
an integrated, composite application without any low level
programming and integration efforts. The main contributions
of this paper are as follows:
The paper first shows that existing techniques, tech-
nologies, and algorithms used for finding and matching
Web service components (WSDL-based) can be reused,
with only minor changes, for the purpose of finding
compatible and complementary non-Web service based
components for composite applications. These compo-
nents may include graphical user interfaces, which are
not artifacts described in Web service components. By
building on the techniques initially developed for Web
services matching, finding useful and valid components
for composite applications using high level concepts is
possible. This enables the progressive construction of
composite applications by end users from a catalog of
available components without deep knowledge of the
components in the catalog. This is an advantage over
existing mashup tools which require mastering of varying
degrees of programming skills [4], [5].
Being able to automatically find components suitable
for a composite application is critical for any mashup
toolkits. We demonstrate in this paper how the additional
characteristics of components, specifically graphical user
interface details, can be modeled, described using Se-
mantic Annotation for Web Service Description Language
(SAWSDL) [6] and matched in a similar fashion to the
programmatic inputs and outputs of Web service-based
components. Though similar in some respects to Web
service-based components, our experimental study shows
that these additional characteristics of a component allow
for further match processing logic to be used to provide
better results when searching for components.
This paper shows, through sample applications, that by
considering the unique characteristics of a component
(i.e., coexistence of graphical user interface description),
and new techniques of merging semantic descriptions
across multiple components, a much more accurate search
result for compatible components can be achieved.
The paper is organized as follows. Section 2 outlines the
overall architecture of our progressive composition framework.
Section 3 details the concepts of composite application match-
ing, merging multiple components into a single descriptive
format for matching, and modeling of component’s GUI
characteristics. Section 4 provides a set of experiments, results,
and analysis of progressive composition framework based on
semantic web matching technique with SAWSDL annotations.
Section 5 describes the related work and Section 6 provides
the conclusion and future work.
2.1 Application Components
The application components referred to in this paper gener-
ally contain GUI interfaces, built from technologies such as
JFace, JSPs, Servlets/HTML, Eclipse Standard Widget Toolkit
(SWT), Swing, native windowing systems, etc. Like Web
services and Enterprise Java Beans, these application compo-
nents can take programmatic inputs and produce programmatic
outputs. The programmatic inputs will generally cause changes
in the graphical user interface, and user interaction with the
graphical user interface will cause the programmatic outputs
to be fired. An example of an application component referred
to in this paper is a Portlet [7].
2.2 Lotus Expeditor Composite Application Frame-
We adopt the IBM Lotus Expeditor [8] platform to develop
application components and to mashup composite applica-
tions. Lotus Expeditor is an extension of an Eclipse Rich
Client Platform that complies with OSGi standard and SOA
architecture. The Expeditor contains a Composite Application
Infrastructure (CAI) and an associated Composite Application
Editor (CAE). Figure 1 is a simplified architecture diagram of
Lotus Expeditor Framework. CAI is the runtime environment
for the composite applications. It has two main components
called Topology Manager and PropertyBroker. The Topology
Manager is responsible for reading and interpreting the layout
information stored in the composite application. The Property-
Broker is responsible for passing messages between indepen-
dent components within CAI, in other words, it performs data
mediation for composite applications. The CAE editor is used
to assemble, and wire components into composite applications
without the need for the underlying components to be aware of
each other at development time and without the user having
to write any additional codes. The desired components can
simply be dragged and dropped to add them to a composite
application. The adding, removing, and wiring can be done
in an iterative/progressive fashion to allow the assembler to
refine the composite application. This declarative data-flow
Fig. 1. Lotus Expeditor Composite Application Frame-
like wiring of components is one of the main advantages of
Lotus Expeditor. The wired components can be saved in an
XML file and written to local file system, hosted in a web
server/portal server, or placed in Lotus Domino NSF database
for reuse or customization by other users.
The programmatic inputs and outputs of an application
component in CAI are described using WSDL. Typically, the
associated WSDL files for CAI components are created as
part of the component development process. In the current
implementation of Lotus Expeditor, the WSDL files for appli-
cation components in CAI do not include the graphical user
interface type (e.g. JSP, SWT, AWT, Swing, etc.). The com-
posite application assembler must have previous knowledge
of component interfaces they are restricted in and the types of
GUI technologies they can use. For example, if the composite
application deployment platform does not provide support for
portlet interfaces, an assembler must know which components
in the repository are built from portlets and specifically avoid
those when assembling the composite application. Lotus Ex-
peditor also does not provide a way for finding compatible
and complementary components from a catalog of existing
components based on components’ capabilities. We extended
Lotus Expeditor Workbench with a new menu item called
Analyze Composite App that opens a dialog box for user to
search for the desired components to use for composition
based on high-level semantic concepts.
2.3 Sample Composite Applications
In this section, we describe the functionalities of two sample
applications used in the experimental study of our mashup
framework. These two sample applications were selected be-
cause they represent two different types of valid compos-
ite applications. The HotSpotFinder represents a hybrid
composite application that includes Eclipse SWT GUI and a
web application (jWire) accessed via a web browser (RESTful
Web service). The order tracking application represents
a real-world composite application that could be deployed to
a cooperative portal environment like IBM WebSphere Portal
or BEA WebLogic Portal. The HotSpotFinder composite
application is composed of three separate components.
CityStatePicker is implemented as an Eclipse UI
Extension class, which allows a user to select a state and
a city from two separate drop down lists. After both city
and state have been selected, the component publishes
the city and state as a single output.
HotSpotFinder is implemented as an instance of the
Eclipse SWT Browser, which is programmatically driven
to different URLs based on the inputs. In order to provide
interesting content, the JiWire [9] website is accessed
by the browser. The HotSpotFinder takes as input
a city and state. When this input is received, the browser
is directed to a URL (constructed dynamically with city
and state as input) on the JiWire website, which provides
a list of wireless Internet access points in the given
city. By double clicking on an address shown in the
HotSpotFinder, the address is published as an output.
GoogleMapper is implemented as an instance of the
Eclipse SWT Browser, which takes as input an address,
based on this address, the browser loads a map for the
address using Google Maps to provide the actual content.
The above three components can be developed separately
by different programmers using different technologies. In order
for them to be made available for our composition framework,
they must be imported into Lotus Expeditor. The WSDL file
for each component must be created and annotated as shown
later in Section 3.1.
The OrderTracking application is composed of five
individual components, with several inputs and outputs. The
individual components are all built as portlets. The base code
for this scenario was taken from the Cooperative Portlets
sample provided as part of Rational Application Developer 7.0.
The same sample is described in detail in [7]. The code was
reused with only minor changes; small errors were corrected
in the application code. The five components are:
Orders Portlet displays a list of existing orders
for a specific month. The component accepts a month
as input and outputs a month, status, an order id, and
a customer id. Both order id and customer id are pro-
grammatic outputs in the sense that when a user clicks on
one of the listed order id, the order information reflected
in other portlets takes the order id as input.
Order Detail Portlet displays the details of a
specific order that includes quantity, status, sku and
tracking id. The component accepts an order id as input
and only tracking id is output as a programmatic input
to other components.
Tracking Detail Portlet displays the tracking
information related to a specific order. The component
accepts a tracking id as input. Customer name is the only
programmatic output here.
Customer Detail Portlet displays customer in-
formation. The component accepts a customer id and
a customer name as input and does not provide any
programmatic outputs.
Account Detail Portlet displays the account de-
tails of a particular order. The component accepts an
order id as input and does not provide any programmatic
% &
Fig. 2. Sequence of steps in composing an application
2.4 Scenario of Assembling Composite Application
Figure 2 illustrates the sequence of screen shots in Lo-
tus Expeditor Client workbench that results in a simple
HotSpotFinder composite application. Screen A shows the
initial composition workbench. The right panel shows the list
of components (e.g., HotSpotFinder,GoogleMapper,
CityStatePicker,OrderTracker) that are available
for composition as well as links to other available remote
components. The left panel displays all the existing composite
applications of a particular user. The middle panel displays the
in-progress composite application. To start the composition
process, the user must first pick a component from the right
panel. The selected component then becomes the search query.
When the user clicks on Analyze Composite App menu,
a dialog box in screen B is displayed. If there are more
than one components on the canvas, the user has the option
of choosing “individual” or “merged” matching criteria. If
“individual” matching is checked, each of the components’
WSDL in the current composite application will be matched
individually to the target WSDLs in the repository. If not
checked, a merged WSDL file created from all the existing
WSDL files in the current composite application will be used
in the matching process. After the user enters the desired
search criteria at the top of screen and presses the “Find
Matches” button, screen C is displayed. By picking the best
matched component (the one with the highest score) from
the palette and dropping it on the middle panel, screen D
is displayed. The middle panel now has two components
(CityStatePicker and HotSpotFinder) which were
not aware of each other. At this point, the user can right click
on the in-progress composite application which will allow
selection of the “wiring” action from the menu. Screen F
shows the result of wiring the two selected components on the
middle panel. The CityStatePicker component (labeled
“City View”) provides a single output, labeled cityState.
The HotSpotFinder component provides a single input
named SetLocationCityState. The dotted line indi-
cates that the cityState output has been linked to the
SetLocationCityState input. Therefore, when the out-
put cityState is fired, the argument of that output will
be sent as the argument to the SetLocationCityState
input. The composition is now completed and screen G dis-
plays the result of running the composed application within
the Expeditor Workbench.
In this section, we describe the process of creating reusable
components and the techniques, technologies, and algorithms
that can be leveraged to provide progressive mashup of com-
posite applications.
3.1 Building Components
In order to create a component for reuse in a composite
application, a few additional steps are required beyond what
is necessary to create a stand-alone application or component.
However, the majority of the process is similar to creating
standard applications. Obviously, different component frame-
works will advocate slightly different protocols for component
creation. In J2EE-based component systems, the concrete
processes and tools used for developing and deploying a
component will be different from Lotus Expeditor. However
the fundamental principles are the same. One of the first steps
that any developer will need to do when building a component
for an application is to decide on the graphical interface
technology. This may be based on Java Swing, servlet, AWT,
native widgets, portlets, and HTML. The second step is to
locate the data that is to be presented in the graphical user
interface. Often this information consists of records from
a relational database or some other back-end data sources.
The third step is to write a bit of code to deal with the
programmatic input and the output of the component. Up
to this point there is no difference in the process between
creating components for a composite application and creating
a component for a standard application. For components to
be reusable in a composition framework, a few additional
steps must be taken. The first additional step is to decide
which programmatic inputs, outputs, and operations should be
exposed if this component is reusable. The second additional
step is to generate a WSDL file that describes the exposed
programmatic input, output, and operations of the component.
The last additional step is to annotate the generated WSDL file
with semantic information. We assume that there are existing
ontological models that we can use. Otherwise, those ontolog-
ical models must be created first. The following are the steps
involved for building the CityStatePicker component
using the Lotus Expeditor Toolkit:
Develop CityStatePickerView, a Java class which
extends Eclipse UI Extension class. This class is respon-
sible for creating the UI to be displayed to a user in
Lotus Expeditor Rich Client. The UI should allow her to
select a state and then a city within that state. When the
city is selected, the city and state information should be
broadcasted to the PropertyBroker in Expeditor.
Develop a Java class, pubCityState to broadcast UI
events to the PropertyBroker in Expeditor. This is a
helper class for CityStatePickerView.
Create a WSDL, CityStatePicker.wsdl, to expose
the properties (UI inputs) and actions (operations) asso-
ciated with the CityStatePicker that can be reused.
This file is created using properties wizard and the editor
available in the Expeditor Toolkit in our framework.
Annotate the generated WSDL file with semantic infor-
mation on any elements in the WSDL file that will play
a part in matching the component.
Create a manifest file for automatically deploying the
component to OSGi compatible Lotus Expeditor middle-
With the wide availability of Web service development
toolkits, the generation of WSDL file can be automatic. The
semantic annotation of WSDL, however, has to be done
manually. Currently, we use a plain text editor for performing
the annotation. However, we can envision development of a
simple graphical tool to simplify the annotation process such
as the semantic annotation tool available in Kepler system [10].
3.2 Semantic Annotation of Components
The WSDL-based programmatic inputs and outputs of a
reusable component are annotated using suitable ontological
models to enable searching using higher level concepts. We
choose to use SAWSDL annotation scheme because SAWSDL
is currently a recommended W3C standard. This standard does
not dictate a language for representing the specific ontology. It
defines a method of including ontological information written
in any specification languages into a WSDL document. In this
mashup tool, we chose to use ontological model specified in
OWL for annotation. This allows us to leverage the established
semantic web matching techniques developed in OWL for
flexible and intelligent search for compatible components. In
this sense, WSDL+semantic annotation (SAWSDL) is used as
a loose standardization of APIs that can be exposed by the
diverse kinds of components that we want to mashup.
However, using SAWSDL prevents us from including the
emerging annotated RESTful Web services that adhere to the
simpler REST architecture style of service invocation in our
mashup tool. RESTful Web services do not have associated
WSDL files. Instead, they are annotated using a different
scheme called SA-REST [11] which is based on HTML and
RDFa1. Despite its simplicity, SA-REST in its current form
is not suitable for use in the Lotus Expeditor framework. The
annotated CAE (Composite Application Editor) is dependent
on WSDL format for data mediation. Moreover, adopting SA-
REST will require significant changes to the structural match-
ing part of the semantic Web matching algorithm. However,
as described in [12], it is feasible to translate SA-REST into
SAWSDL. Thus RESTful Web services with annotation can
be accommodated in our framework with some additional
effort. Within Lotus Expeditor framework, annotating com-
ponents using either SAWSDL or SA-REST will have similar
limitations when it comes to composing applications. This is
because both require the components to be annotated manually
and a-priori. If a particular capability of a component is
not being annotated, at runtime, it is impossible to leverage
that capability for mashup even if it is useful to utilize that
capability within a composite application.
Semantic annotations (SAWSDL) only work if there is a
unified ontological model. If the ontological model for the
component we are interested in annotating is not available,
it must be created first. Tools such as the Protege-OWL [13]
editor can be used to create OWL-based ontological models.
However, a given enterprise may have a collection of models
that already exist to describe the data and processes used in the
enterprise. Further, a given industry may have a collection of
models that have been already created to describe the unique
characteristics of that industry. If the component being built
is intended for use in a given enterprise or industry, care
should be taken to use existing ontological models where it
makes sense. Since the SAWSDL specification allows multiple
models to be attached to a given element, it may be appropriate
to provide one or more enterprise, industry, and custom
models to a particular element in the component. The different
ontological models must all be represented in OWL formalism
in our framework to leverage using existing semantic Web
matching techniques.
Figure 3 is a WSDL file for CityStatePicker com-
ponent. Lines 5 and 9 import the necessary namespaces
used to add the semantic annotations. Lines 15-17 de-
fine a new message named cityState”, which de-
fines the name of the string that will be output when
a user interacts with this component. Line 16 describes
a message that has been annotated with a reference to
an element in an ontological model. This is shown as
wssem:modelReference="TravelOnt#City" in the
WSDL file. TravelOnt is an OWL-based ontological model
that is available on the Web. With this annotation, we are
describing the message in terms of an OWL class in an
existing ontological model. This message is set as an output
of the pubCityState operation in a portType (lines 18-
22) and included in a portlet type binding (lines 23-35). The
pubCityState operation corresponds to broadcasting the
city and state information to PropertyBroker.
With this markup using the standard grammar defined by
WSDL and SAWSDL, the component’s output can now be
matched against other components’ programmatic inputs using
existing semantic Web service matching technologies. Note
that only properties that are pertinent to reuse are present in
the WSDL file. By including the annotation, the matching
engine is able to match based on capabilities of the com-
ponent as described in the ontological model. For example,
assume the cityState part element was to be compared
against another component’s part element county”. The
text-based matching would not count these as a possible match
because the two strings are not equal (i.e. cityState
!= county”). However, if the county element had a
semantic annotation of TravelOnt#County in its mod-
elReference attribute, the matching logic would be able to
compare the model types City and County. If the model
described a relationship between a City and a County, perhaps
using the hasProperty OWL attribute, it could be determined
that a city is in a county and both are part of a state. Thus, the
two elements will return a matching score because the analysis
would show that these two elements are very closely related.
3.3 Semantic Matching of Components
Describing the semantic knowledge of a component via the
annotation process described above has a unique advantage in
that the additional metadata added to the component’s WSDL
do not change the structure of the component’s description.
Therefore, existing semantic Web services matching technolo-
gies and algorithms can be used directly for matching the
application components. One such set of algorithms, adopted
in our work, is described by T. Syeda-Mahmood [14], which
combines the use of semantic (domain independent) and
ontological (domain dependent) matching for the purpose of
matching Web service components. In the following, we will
briefly illustrate the matching mechanism. Interested readers
are referred to [14] for more details.
Domain Independent Matching. The input to this matching
algorithm is a single WSDL file and the collection of target
01 <?xml version=“1.0” encoding=“UTF-8”?>
02 <definitions name=“edu.txstate.mpc”
03 targetNamespace=“”
04 xmlns=“”
05 xmlns:TravelOnt=“http://localhost:8080/thesis/travel.owl”
06 xmlns:portlet=“”
07 xmlns:soap=“”
08 xmlns:tns=“”
09 xmlns:wssem=“”
10 xmlns:xsd=“”
11 xmlns:xsi=“”>
12 <types>
13 <xsd:schema targetNamespace=“”/>
14 </types>
15 <message name=“cityState”>
16 <part name=“cityState” type=“xsd:string” wssem:modelReference=“TravelOnt#City”/>
17 </message>
18 <portType name=“edu.txstate.mpc Service”>
19 <operation name=“pubCityState”>
20 <output message=“tns:cityState”/>
21 </operation>
22 </portType>
23 <binding name=“edu.txstate.mpcbinding” type=“ Service”>
24 <portlet:binding/>
25 <operation name=“pubCityState”>
26 <portlet:action activeOnStartup=“true” caption=“pubCityState”
27 description=“Announces the city and state” name=“pubCityState”
28 selectOnMultipleMatch=“false” type=“standard”/>
29 <output>
30 <portlet:param boundTo=“request-attribute” caption=“cityState”
31 description=“Published the city/state selected”
32 name=“cityState” partname=“cityState”/>
33 </output>
34 </operation>
35 </binding>
36 </definitions>
Fig. 3. WSDL with semantic markup for
CityStatePicker component
WSDL files to match upon. The output is a target WSDL file
that has the largest ratio of matched attributes (matching score)
or a list of target WSDL files sorted by the ratio of matched
attributes. In order to calculate the matching score, both the
query and the target WSDL files must be pre-processed using
the following procedures:
Word tokenization: By exploiting cues such as changes in
font and presence of delimiter, a multi-term word attribute
is tokenized.
Part-of-speech tagging and filtering: Simple grammar
rules are employed to label tokenized attributes. Stop
words are removed.
Abbreviation expansion: Both domain-independent and
domain-specific vocabularies are used to expand tok-
enized words that is abbreviated. For example, zip is
expanded into zipcode.
Association of tag type with attributes: Each tokenized
attribute is labelled with its specific WSDL element types.
For example, name in Figure 3 is labeled with part tag.
Synonym search: A thesaurus, in this case WordNet [15],
is used to construct a list of synonyms for each tokenized
attribute. For example, the word town is included as a
synonym for a tokenized attribute city”.
Given a pair of matching attributes (A,B) with Aequal to
[name=cityState] and Bequal to [name=county], the
similarity matching score of this pair of attributes is calculated
based on the following formula:
Sem(A,B) = 2 M atch(A,B)/(m+n)(1)
Where mand nare the number of valid tokens in Aand B.
Both Aand Bmust be of the same structural type. In this case
they both must be tagged with Part element of a WSDL file.
Match(A,B)returns the number of matching tokens. Given
a query WSDL file and a collection of target WSDL files to
match upon, the best matched WSDL file is the one that has
the highest Sem(A,B)score.
Attribute Pair Relationship Distance Score
(A,B) EquivalentClass 0.0
(A,B) HasPropertyClass 0.3
(A,B) HasPartClass 0.5
(A,B) SubClassOf 0.7
(A,B) Other 1.0
A simple distance scoring scheme
Domain Dependent Matching. Domain independent match-
ing described above is basically an enhanced keyword-based
matching. Domain dependent matching makes use of ontolog-
ical relationship between tokenized attributes. Only attributes
that are being annotated in the WSDL files can be used for
domain dependent match. The semantic for each attribute
are compared using a custom ontology matching algorithm
from SNOBASE (Semantic Network Ontology Base), an IBM
ontology management framework for loading ontologies from
files and via the Internet [16]. This algorithm takes into
account the relationships between the given attributes, such
as inheritance,hasPart,hasProperty, and equivalent classes.
A simple distance scoring scheme as shown in Table 1 is used.
This scoring scheme gives a coarse indication of semantic
distance between concepts. For example, the distance score
for two attributes that have an equivalent relationship is 0. For
the inheritance relationship (i.e., SubClassOf in Table 1), the
score is 0.7. The matching score between a pair of matching
services Sqand Siis calculated using the following formula:
Match(Sq,Si) = 2 hiX
(1 dist(i, j))/(ni+nq)(2)
Where niis the number of attributes of the query service Sq
and niis the number of annotated attributes present in service
Si,hiis the number of annotated attributes of services Si
that have been matched out of nq, and finally dist(i, j)is
ontological distance score between the jth term in service Si
and a corresponding query term. The best matched WSDL file
to a query WSDL file is given by the one that has the highest
ontological match score.
Final Score. It is calculated using a winner-takes-all approach.
The maximum of the domain independent score and domain
dependent score is reported as the overall matching score. Cur-
rently, the higher score is taken to mean a “better match”. The
advantage of using two different scoring mechanisms in one
framework is that the same matching algorithm is applicable
to either annotated or un-annotated WSDL files. If WSDLs are
all marked up consistently, the algorithm will be able to find
more accurate match based on semantic information. If none
of the WSDLs is annotated, the domain independent matching
can still be applied to return the “matches”.
Discussions. We reused much of the same matching logic and
algorithms from semantic Web services. However, there are
several fundamental differences when dealing with non-Web
services based components. In many cases, when composing
with Web service components, a developer is looking for APIs
that can either:
Match, i.e., using the output from a single Web service
and finding a second Web service that can take it as
input. The developer can continue this process and string
together several Web services choreographed by a specific
process model. This is typically referred to as process-
based Web service composition [17], [18], [19], or
Compose, i.e., starting with a known output and a known
input, through some intelligent search/inference tech-
niques, the system returns one or more services that
will transform the output of the first Web service into
something that can be consumed by the final Web service.
This is typically referred to as dynamic semantic Web
service composition [20], [21], [22].
The difference with respect to our composite applications is
that in most cases the goal is not to put together a single busi-
ness process or tightly link fragments of software processes
for the purpose of automating a specific task; rather, the goal
is to integrate separately created components together “on the
glass” [23] and provide the ability for those applications to
communicate or interact without prior knowledge of each other
or in any specific order. There is no explicit control-flow spec-
ified between the communicating components. A component
can start execution whenever it receives the required input.
This data-flow oriented paradigm of composition made this
framework suitable for users who do not have knowledge of
control-flow constructs in programming or process languages
to compose their applications on the fly. Furthermore, users
do not need to learn how to invoke application specific APIs
in order to compose different applications.
There is no specific begin and end state in our composition
framework. There is also no specific ordering in terms of
the composition process. For example, the next component
to be composed does not have to depend on the previous
component. It depends on what components are already in the
application that we are composing. We advocate progressive
composition process that allows a user to explore or preview
the functionalities of the composite application iteratively and
refine them at any point in time. Users have choices on what
to compose that will adapt to their working style. For example,
when composing an OrderTracking composite applica-
tion, if the user is a salesperson, the application will include
the OrderPortlet (Figure 11). However, if the user is a
customer, it is not appropriate to include the OrderPortlet.
The experiments reported in this section were completed
using the sample applications described in Section 2.3. The
experiments were conducted on a Lenovo ThinkPad T60p
running Microsoft WindowsXP SP2. An Apache HTTP server
in conjunction with an IBM WebSphere Portal Server 6.0 was
used to simulate the component library. In order to make
it easy to drive the test cases and to analyze the results, a
graphical user interface component was created (see Figure 4).
This component reads the currently executing composite ap-
plication and drives the test cases. The “View Filter” and
“Display Filter” sections allow the user to set the graphical
user interface search criteria. The main panel of the window
displays the score associated with each of the target WSDLs
that was included in a search request. The “Find Matches”
button starts the search process. Finally, the “Use individual
matching” checkbox allows the user to specify which type of
matching will be used. If checked, each of the components’
WSDLs in the current composite application will be matched
individually to the target WSDLs in the repository. Otherwise,
a “merged” matching will be used.
In addition to the application components required for the
composition of the two applications described in Section 2.3,
an additional four WSDLs with semantic annotations were
included in the target component repository. These WS-
DLs are SourceInterface.wsdl, SourceInterfaceV1.wsdl, Tar-
getInterface.wsdl, and TargetInterfaceV1.wsdl. These addi-
tional WSDLs were added to the repository to simulate other
components that should not be matched in the context of our
composite applications. These WSDLs describe components
for a retail order system, and are not applicable as components
in the two applications being mashed up using our framework.
4.1 Experiment One - Basic Matching
The first experiment shows simple matching using two com-
ponent WSDLs and the semantic web matching logic. The
input for this scenario is the CityStatePicker.wsdl and the
target is the HotSpotFinder.wsdl.When run through the match-
ing logic, a score of 50 is produced. This is a reasonable
score because of the differences in the two WSDLs. The
CityStatePicker.wsdl file defines a single message, cityState,
and the HotSpotFinder.wsdl defines two messages, city and
address. In order to show the impact of the semantic matching
only, a modified version of the HotSpotFinder.wsdl is used. In
the modified version, the identifying names such as city and
address are replaced with random strings such as vvv and ddd.
Because these do not match fields in the CityStatePicker.wsdl,
the ontological score is always returned. The resultant score in
this case is 37.50. This lower score can be accounted for based
on the fact that only the message elements have been annotated
with additional semantics. Lastly, we remove all the semantic
annotation in the WSDL documents so that only pure keyword
matching can be used. In this run, the matching score drops to
25. Additional changes to the WSDL that remove identifying
city and state keywords while preserving its functionality cause
the score to drop even more. This shows that the semantic
matching algorithm is working as expected and that we have
a valid environment for conducting other experiments.
4.2 Experiment Two - Merged WSDL Matching
The second experiment shows the effect of using a merged
WSDL to find compatible components for a composite ap-
plication. The two inputs for this experiment are Orders.wsdl
and TrackingDetail.wsdl. These are matched against the other
three target WSDLs that are part of the second Order Tracking
Fig. 4. Results of individual matching in experiment 2
Fig. 5. Results of merged matching in experiment 2
application described in Section 2.3, specifically AccountDe-
tail.wsdl, OrdersDetail.wsdl, and CustomerDetails.wsdl. Given
this setup, any of the three target WSDLs could be a
good match because each of them have inputs that can
be satisfied by the available outputs of the Orders and
TrackingDetails components. In this experiment, we
first match the two input components individually against
the other three. The results are shown in Figure 4. The first
four entries in the list are the result of matching against
the Orders.wsdl; the second four results, grouped in the
box, are the result of matching against the TrackingDe-
tails.wsdl. As we can see, the CustomerDetail compo-
nent has the highest overall match value of the possible
choices. This makes sense because the single output of the
TrackingDetails component matches one of the two
inputs to the CustomerDetails component. When we
look at the results for the Orders component, we see the
CustomerDetail component is ranked lower than either
the AccountDetail or the OrderDetail component
and equally scored against the TrackingDetails com-
ponent. The AccountDetail and OrderDetail com-
ponents also scored fairly well in the match against the
TrackingDetails component.
Given this ambiguity, how do we decide which component
to add to the composite application? This is where the merged
WSDL search can assist. If we do a merge WSDL search, we
combine the inputs and outputs of the two given components
and match those against the remaining components in the
Fig. 6. Matching with TrackingDetail
Fig. 7. Matching with TrackingDetail and CustomerDetail
catalog. The results of this search are shown in Figure 5.
In this case, we can now see that the CustomerDetail
component is probably the best component to add to the
composite application.
4.3 Experiment Three - Assembling the Order Track-
ing Composite Application
We have built a number of composite applications using our
mashup platform. In this experiment, we will particularly show
how the order tracking composite application can be built.
The goal is to demonstrate that our mashup framework can be
used for components that were built using different component
technologies. Here, all the components were originally built
as portlets. From looking at the possible starting points, the
Orders component would be the most obvious one to use
since it contains several outputs. However, instead we will use
the TrackingDetail component to show how we can build
the complete application. This is a reasonable choice to begin
with because we are building an order tracking application.
The first step is to add the TrackingDetail component
to the application and run the search. The results, as shown
in Figure 6, tell us that the CustomerDetail component
would be a good one to add at this point.
Once the CustomerDetail component is added to the
application, we can run the analysis again. Figure 7 shows
the results of this process. Looking at the scores, the average
scores have decreased, though only by ten points. You will
Fig. 8. Matching with TrackingDetail, CustomerDetail, and
Fig. 9. Matching with TrackingDetail, CustomerDetail,
Orders, and OrderDetail
also observe that the score for the Orders component has
actually increased by a small amount. Given that there are
three possible components to choose from with equal scores,
we will choose the Orders component because it has shown
a consistent increase in value over the last two searches.
The Orders component is added to the application and
the matching analysis is run again. This time, in Figure 8,
we observe that the overall scores have decreased again, but
there is still a significant difference between the two highest
scores and the third score. There is no real drive to choose one
component over the other based on the scores, so we need
to choose one. Because we are building an order tracking
application, the name OrderDetail seems like a better
choice than AccountDetail. In other experiments not
detailed here, it was seen that choosing the AccountDetail
component eventually lead to the same final composite ap-
plication described in this section. Additionally, the iterative
nature of composite application assembly allows assemblers
to try out components and remove them if they do not prove
to be useful. In this case, if the AccountDetail was found
not to be usable in the application, the assembler could remove
it and instead add the OrderDetail component.
We add the OrderDetail to the application and run the
analysis again. As we see in Figure 9, the top score has again
dropped, but it is significantly higher than the other scores. We
therefore decide to add the AccountDetail component.
Fig. 10. Matching with All Order Detail components
With the AccountDetail component added to the appli-
cation, we run the analysis again and get the results as shown
in Figure 10. The top ranking score is now 9.5238 - much
lower than what we started with and also much lower than
the last component we added. It is reasonable to assume that
the application is now complete. We can now customize the
layout of the application to suit the user’s needs based on the
component we have selected. Once a tracking ID is entered
in “Tracking Detail Portlet”, the related information will be
shown immediately in other components. Figure 11 shows the
assembled order tracking composite application.
4.4 Experiment Four - Effectiveness and Scalability
of the Semantic Matching
We also conducted experiments to study the effectiveness and
scalability of our proposed semantic matching process. To
test the effectiveness of the semantic matching, we added
additional 11 new WSDL files. These WSDLs were down-
loaded from public domain Web service portals [24], [25].
These WSDL files are much more complex in the sense that
they all have multiple messages, operations and bindings as
compared to WSDLs generated for the GUI components used
in experiments 1-3. Without any annotations, all these new
Web services (shown in italics font) have low matching scores
as shown in Figure 12. However, when we annotated the
message for the dictionary.wsdl with TravelOnt#City (i.e. the
same ontology that the source CityStatePicker component is
using), the score increases from 0.0 to 14.28. Figure 13 shows
the resulting scores of running with annotated dictionary
WSDL (we renamed the name of the Dictionary.wsdl file
for this experiment). Note that these experiments were run
in batch mode for efficiency purpose and thus the results are
not displayed in a GUI-based screen as in other experiments.
Interested readers are referred to [14] for a more detailed
discussion on the effectiveness of semantic matching.
To test the scalability of the matching algorithm, we logged
the elapsed time when matching with different number of
target WSDLS. We started with randomly selected 11 WSDLs
(used in the previous experiment) and doubling the number of
WSDLS in the target for each additional run. Thus, we ran
this experiment with 11, 22, 44, 88 and 166 WSDLs. The
memory heap size is set to 1G. Figure 14 shows that as the
number of WSDLs increases exponentially, the time it takes
Fig. 11. Assembled OrderTracking Composite Application
for completing the match increases in a linear fashion. This
demonstrates that the matching algorithm is scalable. The only
limiting factor is the availability of heap memory size of the
computer where the system is running.
4.5 Experiment Analysis
Experiment one and two have shown that the existing Web
services matching code can be used in conjunction with
composite applications. Because the matching logic uses both
text-based matching and semantic matching, the matching
algorithm can be used without adding the semantic markup.
However, as we observed in experiment one, the semantic
matching always provides better results. For example, when
two components are named differently yet provide the same
functionality, the semantic matching is able to find the match.
Experiments three has shown that it is possible to
build a composite application from a collection of differ-
ent components—implemented using different technologies—
using semantic annotations and semantic Web service match-
ing logic. While there is no automatic way to start the build-
ing process, once a starting point is selected, the remaining
compatible components begin to stand out in the repository
searches using semantic-based mashup. As with the assembly
of the OrderTracking application, the components that
could be added to this application really stood out with scores
three or more times greater for components that were not
appropriate. The artificial Source and Target WSDLs that
were added to the repository continue to score low in all cases.
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Fig. 12. Matching with additional none annotated ser-
Increased score
Fig. 13. Matching with annotated dictionary service
This is not surprising as these components describe functions
unrelated to the applications being built in these experiments.
Experiment four further demonstrates that consistently an-
notated Web services will score higher in matches than ran-
domly picked Web services without any annotation. It also
shows that the semantic matching algorithm is scalable. The
time it takes to perform the match is proportional to the
number of Web services.
Despite the fact that the mashup tools share a common
goal—enabling users to create situational applications based
on existing application components—the actual capabilities,
implementation technology and the target audience of these
tools are widely different. For example, IBM’s DAMIA [26],
MashupHub [27], SABRE [28] or Apatar [29] largely target
enterprise intranet environments, whereas Popfly [30] or Intel
Mash Maker [31] are aimed at individual users and private use.
The execution environment of a mashup could be on a server,
client (i.e., browser) or a stand-alone desktop application.
Therefore, to focus our discussion, we compare mashup
tools and approaches with our work mainly from the following
0 1 2 3 4 5
Scalability of the Matching Algorithm
No of target WSDLs
Matching time (in secs)
Fig. 14. Performance of the matching algorithm
three view points: i) types and extensibility of components,
ii) support structure for users to find suitable components,
and iii) alternative mashup programming patterns for wiring
the components. As a number of research works suggest
([32], [28], [33], [34]), most tools provide limited search
and discovery for mashup components. Users still need to
know how to write code (e.g., JavaScript or XML/HTML) and
link the components using technical concepts derived from
programming. The following discussions will highlight that
our work tries to overcome these issues through the use of
composite applications running in Lotus Expeditor.
5.1 Types of Components in Tools
Yahoo! Pipes [35] provides a Web-based means of pulling data
from various data sources, merging and filtering the content
of those sources, transforming the content, and outputting the
content for users to view or for use as input to other pipes.
There are several limitations in Yahoo! Pipes. The first one
is the limited set of inputs and outputs on components. There
is no way to use arbitrary inputs or outputs when using this
application. A component in a composite application should
be able to accept many different types of inputs and provide
many different types of outputs. The second limitation is that
the flow of a pipe is static and sequential. While a user
can configure many different inputs, all of the connections
are executed in a sequential manner until the single output
is reached. With our composite applications, the different
components in the application can communicate with each
other in any manner that the assembler chooses. Finally,
Yahoo! Pipes is a server-based technology. There is no way
for a user to construct and execute a pipe without a network
connection and execute the pipe using locally stored data. A
pipe can be accessed programmatically, like a Web service, but
in order to execute the pipe the user must be able to connect
to the Yahoo! Pipes server. Similar limitations exist in other
Web portal type solutions such as Popfly [30] or Marmite [5].
DAMIA [26] extends the type of data sources for mash up to
enterprise types such as Excel, Notes, Web services, and XML
document rather than just URL based sources as in Yahoo!
Pipes. It has a simple model of treating all data as sequences
of XML. DAMIA offers three kinds of main operators, namely
ingestion,augmentation, and publication. Ingestion brings data
sources into the system. Augmentation provides extensibility
to the system. It allows creation of new mashup operators and
is thus more powerful than the fixed Yahoo! Pipes operators.
Finally, publication operator transforms the output from a
mashup to common output formats such as Atom, RSS or
JSON for the consumption of other components. It relies on
additional tools like QEDWiki2to visualize outputs. DAMIA
focuses on data rather than component mashup. In contrast,
we treat both data and applications as components.
5.2 Mashup Component Search and Discovery
As mentioned earlier, most mashup platforms have inappro-
priate support for component cataloging and querying. There
are a few works that try to address this issue in different ways.
Many works use a Web 2.0 or online community-style
approach. For example, Intel Mash Maker observes the user’s
behaviour (e.g., what kind of data she is interested in) and
recommends an existing mashup that the user would find
useful. It also correlates the user’s behaviour with that of other
users and use the knowledge to suggests mashups defined by
other users on the same Web page. Most Web-based mashup
tools offer a community feature where mashups are tagged,
rated and organized by categories.
For data sources that publish the standard RDF, a tool such
as Semantic Web Pipes [36], which is inspired by Yahoo!
Pipes, offers a way to aggregate data using SPARQL [37] and
RDF-aware operators. In Intel Mash Maker [31], much of the
mashup creation and execution happens on the user’s browser,
directly over the Web pages currently on display. To extract
data from Web pages, the tool uses an RDF schema associated
with each page or a knowledge-base created by a community
of users. Users can formulate XPath queries over the extracted
schema to create intricate data model to manipulate with. A
mashup is created by combining data from multiple pages in
the form of mini-applications (or widgets). DAMIA also offers
a mashup functionality for data sources with available RDF.
Other approaches focus on making “smart” guesses and
recommendations for the users to choose suitable components
for a given situation. A good example is MARIO (Mashup
Automation with Runtime Orchestration and Invocation) [38].
It uses tag-based service description, service selection and
taxonomy. The engine allows a user to explore the space of
available mashups and preview composition results interac-
tively, using tags, via an abstraction called “Wishful Search”.
MARIO offers a light weight planner that works with user
generated tags for goal-driven based composition.
MatchUp [39] introduces the concept of auto-completion,
very much like email addresses in a mail client or search
phrases in a browser, to the creation of mashups. The idea is
based on the observation that mashups developed by different
users typically share common characteristics. The approach
exploits these similarities to make ranked suggestions for
possible “completions” (missing components and connections
between them) for a partial mashup specification. These ap-
proaches share similar goals as ours in providing a rapid
2., it is now part of IBM Lotus
and end-user friendly composition framework via high-level
semantic matching of available services, feeds and flows.
5.3 Alternative Mashup Patterns
Conventional approach to mashup programming is to conceive
mashup as data flow that takes input from multiple sources,
applies transformation and visualizes the results. Normally
the visual metaphor used in this environment is “boxes”
(representing data sources) and “connectors/wires” (represent-
ing the flow). There are mashup tools that follow different
programming patterns.
Karma [33] and UQBE [40] take a mashup as a schema
matching or data integration problem. In this environment,
disparate data sources are “joined” by common attributes as if
joining relational tables. The proposed solution is based on a
premise that it is easier for users to understand data semantics
from concrete examples. Using a progressive approach to
composing data (i.e., the Query By Example principles) is
appealing to the non-programmers, and can be compared to
our approach to suggesting semantically close components.
However, the tools support data integration only and inherently
dependent on domain specific characteristics of underlying
data sources. It is not clear how a data source can be compo-
nentised and reused in a different situational application.
Recently, utilizing spreadsheet (tabular/grid) programming
paradigms in data mashup is suggested. Mashroom [41] adopts
nested relational model as underlying data model to represent
Web-extracted data. A set of mashup operations is defined
over the nested tables (e.g., merging, invoke and link another
service directly on a range of rows in an iterative manner).
Another stream of work that is worth noting is mashup
programming patterns at presentation level. That is, applica-
tion/component integration is achieved purely through com-
ponents that expose user interfaces only. Here, component
models specify characteristics and behaviors of presentation
components and propose an event-based composition model to
specify the composition logic (e.g., MixUp [42]). The focus of
MixUp is on integration of applications at presentation level.
They do not deal with semantic annotation of component and
finding compatible components.
In Smashup (Semantic mashup) [11], the components to
be mashed up is restricted to RESTful Web services that is
semantically annotated using SA-REST. The role of SA-REST
in Smashup is to enable automatic data mediation. Smashup
editor provides an interface where a user can enter the URLs of
the annotated RESTful Web services that need to be mashed
up. Then the user needs to wire the appropriate inputs and
outputs of the selected services. Once the complete service
chain is specified, the user runs a command and the Smashup
editor will generate an HTML form that represents the mashed
up application. The process is similar to our mashup tool.
However, our tool is not restricted to matching up of browser-
based services. Our components can be as diverse as a GUI
component, a widget, a server-side EJB component, a .NET
component, or an Adobe FLASH. We leverage SAWSDL for
the purpose of discovery and matching of services, not for data
mediation. A RESTful Web service without annotation can be
used in our framework just like any other type of components.
For a RESTful Web service with annotation, its SA-REST
has to be converted to SAWSDL before it can be used in our
Finally, Kepler [43] is an open source scientific workflow
system which allows scientists to compose a composite ap-
plication (a.k.a workflow) based on available actors. An actor
can be built from any kind of applications. However, Kepler
is not based on SOA architecture and it requires very skillful
low level Java programming to convert applications into actors
which can be composed within Kepler framework. Although
in our current implementation, we do not provide tools for
developers to convert existing components into annotated com-
ponents that can be used in our mashup tool, our framework
support SOA standard and exposing components’ input and
output as WSDLs with semantic annotation is a small effort
compared to actors programming in Kepler.
One of the most difficult problems faced by users in a rich
client environment is finding compatible and complementary
components in a large catalog of components that have been
built by different groups, at different times, using differ-
ent technologies and programming conventions, as well as
reusing those components as it is in a different application.
In this paper, we have demonstrated that this problem can be
largely solved by applying technologies related to the semantic
web and Web services matching and using a progressive
composition framework. The first technology that can be
applied is Semantic Annotations for WSDL (SAWSDL), as
standardized by the W3C. By adding model references to the
message elements of the WSDL, the properties exposed by the
component can be better described using modeling languages.
Since the semantic modeling attributes can be added to any
elements of the WSDL, the definition of the component could
be further refined and described via annotations. Similarly,
non-functional description of components can be added by
introducing additional elements in the WSDL file. One limita-
tion with using SAWSDL or any other annotation techniques
is that component must be annotated a-priori. If a particular
capability of a component is not being annotated, at runtime, it
is impossible to leverage that capability for mashup even if it is
useful to utilize that capability within a composite application.
Part of our future work includes allowing components to
dynamically expose their capabilities for mashup.
The second technology group that can be applied is the
searching and matching algorithms created for use with Web
services. These algorithms provide a powerful method for
scoring the compatibility of an application component from a
large set of possible component choices based on component
capabilities. This scoring approach simplifies the application
creation process for the composite application assembler by
providing a ranking of potential components. This allows the
assembler to focus on the highest ranked components, skipping
over the lower ranked components, when considering which
items may be compatible in the application being created.
The searching process is further improved based on the fact
that a composite application can be viewed and described as
a single component when searching against a repository of
components. This is done by creating a merged WSDL from
each of the component of the composite application.
As demonstrated in the experiment results, the use of
individual matching may still be valuable, especially when
attempting to distinguish between components that score very
closely to each other. A potential improvement to the analysis
results would be to display the score for each target component
using both the merged matching and individual matching,
when the collection of scores is relatively close. In addition,
both functional and non-functional descriptions are needed in
order to make the matching more valuable for users. We are
currently working on 1) allowing a user to specify only a
specific set of inputs or outputs to consider during matching;
2) allowing a user to specify a weight on certain sets of inputs
or outputs that affect the overall score of the matching.
One advantage of our composition framework is that end
users do not need to concern low-level control-flow constructs
during composition. This may be fine with simple application
that involves a few components. However,in order to compose
complex application that are robust, some forms of control-
flow is necessary. Thus, a larger direction of future work is
combining a service mashup approach with a process based
integration approach. A semantically rich process language
with constructs for conditionals, iterations and methods for
insuring reliability of an integrated application can facilitate
more complex combination of a larger set of applications. It
can also help the analysis of an integration specification for
general properties such as lack of a deadlock or a cycle and
problem domain specific properties such as compliance of an
integrated application to a set of business rules.
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Anne H.H. Ngu is currently an Associate Pro-
fessor with the Department of Computer Sci-
ence at Texas State University-San Marcos.
From 1992-2000, she worked as a Senior Lec-
turer in the School of Computer Science and
Engineering, University of New South Wales
(UNSW). She has held research scientist posi-
tions with Telecordia Technologies and Micro-
electonics and Computer Technology (MCC).
She was a summer faculty scholar at Lawrence
Livermore National Laboratory from 2003-2006.
Her main research interests are in information integration, service
oriented computing, scientific workflows and agent technologies.
Michael P. Carlson is a Senior Software Engi-
neer at IBM, the lead developer for the Lotus
Expeditor Client, and the architect of the Lotus
Expeditor Toolkit. He has been working with
Eclipse technology and OSGi technology since
before they worked with each other. Carlson has
been at IBM since 1998 and has worked on a
variety of products including printers, operating
systems, network appliances, web applications,
software development tools, devices, and desk-
top runtime environments.
Quan Z. Sheng is a senior lecturer in the School
of Computer Science at the University of Ade-
laide. His research interests include service-
oriented architectures, distributed computing,
and pervasive computing. He is the recipient of
Microsoft Research Fellowship in 2003. He is the
author of more than 70 publications. He received
a PhD in computer science from the University
of New South Wales, Sydney, Australia. He is a
member of the IEEE and the ACM.
Hye-young Paik is a lecturer at the School
of Computer Science and Engineering in Uni-
versity of New South Wales. Her research in-
terests include flexible business process mod-
elling, modelling and reuse issues in Web ser-
vice mashups. She is an active member of Web
services research community and publishes in
international journals and conferences regularly.
She received her PhD in computer science from
University of New South Wales, Sydney, Aus-
... With the rising prevalence of web-delivered services, a large body of researchers and practitioners from various fields have devoted themselves to exploring the quality precidition and allocation of web services, contributing different perspectives [12,13,14,15,16,17,18,19,20,21]. Particularly, several previous study efforts for accelerating straightforward and rapid mashup creation are mainly by means of visualization combination tools [22,23,24,25]. Faced with a significant volume and variety of web APIs, however, developers are prone to suffer from information overload so that they often fail to be adequately capable of automatically locating well-suited web APIs without adequate assistance. ...
... SiAlshangiti et al. [27] study a novel Bayesian learning approach that is capable of offering accurate suggestions to developers for successful mashup development. To cope with the cold-start issue for "new user", Wang et al. [23] propose a multiplex interaction-oriented service recommendation (MISR) by incorporating multiplex interactions between services and mashups, i.e., content, implicit neighbor and explicit neighbor, into a deep neural network DNN. Yao et al. [4] integrate the implicit web API coinvocation patterns into matrix factorization as a regulation term, which achieves a relatively high performance in terms of accuracy. ...
With the ever-increasing popularity of Service-oriented Architecture (SoA) and Internet of Things (IoT), a considerable number of enterprises or organizations are attempting to encapsulate their provided complex business services into various lightweight and accessible web APIs (application programming interfaces) with diverse functions. In this situation, a software developer can select a group of preferred web APIs from a massive number of candidates to create a complex mashup economically and quickly based on the keywords typed by the developer. However, traditional keyword-based web API search approaches often suffer from the following difficulties and challenges. First, they often focus more on the functional matching between the candidate web APIs and the mashup to be developed while neglecting the compatibility among different APIs, which probably returns a group of incompatible web APIs and further leads to a mashup development failure. Second, existing approaches often return a web API composition solution to the mashup developer for reference, which narrows the developer's API selection scope considerably and may reduce developer satisfaction heavily. In view of the above challenges and successful application of game theory in the IoT, based on the idea of game theory, we propose a compatible and diverse web APIs recommendation approach for mashup creations, named MCCOMP+DIV, to return multiple sets of diverse and compatible web APIs with higher success rate. Finally, we validate the effectiveness and efficiency of MCCOMP+DIV through a set of experiments based on a real-world web API dataset, i.e., the PW dataset crawled from
... With the development of Web 2.0 and the wide adoption of service-oriented architecture (SOA), many services now expose their features in the form of application programming interfaces (APIs). Multiple APIs can be easily composed into an application, also called mashups [1], that creates and delivers unique new value to customers. This growing phenomenon is called the API economy [2]. ...
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The evolution analysis on Web service ecosystems has become a critical problem as the frequency of service changes on the Internet increases rapidly. Developers need to understand these evolution patterns to assist in their decision-making on service selection. ProgrammableWeb is a popular Web service ecosystem on which several evolution analyses have been conducted in the literature. However, the existing studies have ignored the quality issues of the ProgrammableWeb dataset and the issue of service obsolescence. In this study, we first report the quality issues identified in the ProgrammableWeb dataset from our empirical study. Then, we propose a novel method to correct the relevant evolution analysis data by estimating the life cycle of application programming interfaces (APIs) and mashups. We also reveal how to use three different dynamic network models in the service ecosystem evolution analysis based on the corrected ProgrammableWeb dataset. Our experimental experience iterates the quality issues of the original ProgrammableWeb and highlights several research opportunities.
... Tussyadiah and Fesenmaier (2008), Cobe (2008) and Chen and Lin (2015) demonstrated the substantial potential of blogs for marketing strategies [70-72]. Yu et al. (2008), Ngu et al. (2010) and Patel et al. (2015)provided an overview of current trends of mash-up adoption[73][74][75]. ...
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This paper provided a novel definition of customer knowledge management (CKM) as the logical intersection of customer relationship management (CRM) and knowledge management (KM). The main aim was to investigate the digital technologies supporting small and medium enterprises (SMEs) operating in creative industries in their customer knowledge management strategies. To achieve this aim, a survey involving 73 handicraft and/or retail SMEs operating in luxury jewelry industry was conducted. The survey results pointed out that in a few years the scenario has changed and that surveyed SMEs make more intensive use of traditional technologies supporting customer knowledge management processes rather than more innovative digital technologies, which are also cheap and easy to use. This finding showed the difficulties of SMEs operating in creative industries to be responsive to the rapid technological changes that are affecting CKM, as well as the lack of support from information technology vendors in the decision-making process for choosing adequate digital systems.
... [8] implements MashMaker, which is a visual service orchestration tool. [13] proposed a semantic-based composition platform for various services and applications. [12] proposed a visual components composition environment to development composite service. ...
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The rapid progress of the mobile internet has been promoting the popularity of mobile devices, and mobile application development is getting more pervasive. However, the state of the art development environments has a high learning barrier for users' lack of programming experience. In this paper, instead of traditional programming environments, we take consideration of ordinary users' requirements and propose a WYSIWYG cross-platform web-component-based mobile application creation environment for ordinary users. This environment has a visual editor with a drag-and-drop web component. A web component library model is proposed to standardize customized libraries. A cross-platform application model based on web components is implemented to build applications rapidly. It helps ordinary users generate installing packages within simple operations for multiple platforms. A native plugin model is proposed to assist web components to invoke native functionalities. The experiment result shows that ordinary users could quickly start to create mobile applications in our environment.
In an ever-changing environment, Software as a Service (SaaS) can rarely protect users’ privacy. Being able to manage and control the privacy is therefore an important goal for SaaS. Once the participant of composite service is substituted, it is unclear whether the composite service satisfy user privacy requirement or not. In this paper, we propose a privacy policies automatic update method to enhance user privacy when a service participant change in the composite service. Firstly, we model the privacy policies and service variation rules. Secondly, according to the service variation rules, the privacy policies are automatically generated through the negotiation between user and service composer. Thirdly, we prove the feasibility and applicability of our method with the experiments. When the service quantity is 50, ratio that the services variations are successfully checked by monitor is 81%. Moreover, ratio that the privacy policies are correctly updated is 93.6%.
De nos jours, d’énormes volumes de données sont créés en continu et les utilisateurs s’attendent à ce que ceux-ci soient collectés, stockés et traités quasiment en temps réel. Ainsi, les lacs de données sont devenus une solution attractive par rapport aux entrepôts de données classiques coûteux et fastidieux (nécessitant une démarche ETL), pour les entreprises qui souhaitent stocker leurs données. Malgré leurs volumes, les données stockées dans les lacs de données des entreprises sont souvent incomplètes voire non mises à jour vis-à-vis des besoins (requêtes) des utilisateurs.Les sources de données locales ont donc besoin d’être enrichies. Par ailleurs, la diversité et l’expansion du nombre de sources d’information disponibles sur le web a rendu possible l’extraction des données en temps réel. Ainsi, afin de permettre d’accéder et de récupérer l’information de manière simple et interopérable, les sources de données sont de plus en plus intégrées dans les services Web. Il s’agit plus précisément des services de données, y compris les services DaaS du Cloud Computing. L’enrichissement manuel des sources locales implique plusieurs tâches fastidieuses telles que l’identification des services pertinents, l’extraction et l’intégration de données hétérogènes, la définition des mappings service-source, etc. Dans un tel contexte, nous proposons une nouvelle approche d’intégration de données centrée utilisateur. Le but principal est d’enrichir les sources de données locales avec des données extraites à partir du web via les services de données. Cela permettrait de satisfaire les requêtes des utilisateurs tout en respectant leurs préférences en terme de coût d’exécution et de temps de réponse et en garantissant la qualité des résultats obtenus.
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A mashup is a Web application that integrates data, computation and GUI provided by several systems into a unique tool. The concept originated from the understanding that the number of applications available on the Web and the need for combining them to meet user requirements, are growing very rapidly. This demo presents MatchUp, a system that supports rapid, on-demand, intuitive development of mashups, based on a novel autocompletion mechanism. The key observation guiding the development of MatchUp is that mashups developed by different users typically share common characteristics; they use similar classes of mashup components and glue them together in a similar manner. MatchUp exploits these similarities to predict, given a user's partial mashup specification, what are the most likely potential completions (missing components and connection between them) for the specification. Using a novel ranking algorithm, users are then offered top-k completions from which they choose and refine according to their needs.
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In this paper, we explore the use of domain-independent and domain-specific ontologies to find matching service descriptions. The domain-independent relationships are derived using an English thesaurus after tokenization and part-of-speech tagging. The domain-specific ontological similarity is derived by an inference on the semantic annotations associated with Web service descriptions. Matches due to the two cues are combined to determine an overall semantic similarity score. By combining multiple cues, we show that better relevancy results can be obtained for service matches from a large repository, than could be obtained using any one cue alone.
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We introduce the OWL Plugin, a Semantic Web extension of the Proteg´ e ontology development platform. The OWL Plugin can be used to edit ontologies in the Web Ontology Language (OWL), to access description logic reasoners, and to acquire instances for semantic markup. In many of these features, the OWL Plugin has created and facilitated new practices for building Semantic Web con- tents, often driven by the needs of and feedback from our users. Furthermore, Prot´ ege's flexible open-source platform means that it is easy to integrate custom- tailored components to build real-world applications. This document describes the architecture of the OWL Plugin, walks through its most important features, and discusses some of our design decisions.
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The UQBE is a mashup tool for non-programmers that sup- ports query-by-example (QBE) over a schema made up by the user without knowing the schema of the original sources. Based on automated schema matching with uncertainty, the UQBE system returns the best confident results. The sys- tem lets the user refine them interactively. A tuple in the query result is associated with lineage that is a boolean for- mula over schema matching decisions representing underly- ing conditions on which the corresponding tuple is included in the result. Given binary feedbacks on tuples by the user, which are possibly imprecise, the system solves it as an op- timization problem to refine confidence values of matching decisions. The demo features graphical user interaction on the UQBE system, including querying and refinement. Categories and Subject Descriptors: H.2.4 (Database
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Increasingly large numbers of situational applications are being created by enterprise business users as a by-product of solving day-to-day problems. In efforts to address the demand for such applications, corporate IT is moving toward Web 2.0 architectures. In particular, the corporate intranet is evolving into a platform of readily accessible data and services where communities of business users can assemble and deploy situational applications. Damia is a web style data integration platform being developed to address the data problem presented by such applications, which often access and combine data from a variety of sources. Damia allows business users to quickly and easily create data mashups that combine data from desktop, web, and traditional IT sources into feeds that can be consumed by AJAX, and other types of web applications. This paper describes the key features and design of Damia's data integration engine, which has been packaged with Mashup Hub, an enterprise feed server currently available for download on IBM alphaWorks. Mashup Hub exposes Damia's data integration capabilities in the form of a service that allows users to create hosted data mashups.
As the number of online applications increase so do sources of structured data repositories in the form of RSS, ATOM and lightweight Web services. These can all be covered by the larger umbrella of REST-based Web services. Many users want to bring together these discrete data to form new data sets that contain parts of the original services. This activity is referred to as building a mashup. Until now almost all mashups have been built by hand, which requires a great deal of coding. This solution is of course not scalable since more and more sources are coming online. In this document we propose a way to add semantic metadata to REST-based Web services, called SA-REST services, and a way to create semantic mashups, called Smashups, that rely on the annotated services to remove the hand coding of mashups.