=Paper=
{{Paper
|id=Vol-1164/PaperVision14
|storemode=property
|title=IDECSE: A Semantic Integrated Development Environment for Composite Services Engineering
|pdfUrl=https://ceur-ws.org/Vol-1164/PaperVision14.pdf
|volume=Vol-1164
|dblpUrl=https://dblp.org/rec/conf/caise/AbidMRDA14
}}
==IDECSE: A Semantic Integrated Development Environment for Composite Services Engineering==
https://ceur-ws.org/Vol-1164/PaperVision14.pdf
IDECSE: A Semantic Integrated Development
Environment for Composite Services Engineering
Ahmed Abid1 , Nizar Messai1 , Mohsen Rouached2 , Thomas Devogele1 and
Mohamed Abid3
1
LI, University Francois Rabelais Tours, France
ahmed.abid@etu.univ-tours.fr,
{nizar.messai,thomas.devogele}@univ-tours.fr
2
CCIT, Taif University, Saudi Arabia
m.rouached@tu.edu.sa
3
CES Laboratory, Sfax University, Tunisia
mohamed.abid@enis.rnu.tn
Abstract. In this paper, we introduce IDECSE a new integrated ap-
proach for composite services engineering which is based on Semantic
Web and Data Mining. IDECSE considers semantics in all the composi-
tion steps: user query, semantic classification of services in the registry,
composing services, and verifying the composition process. By consid-
ering semantics for describing, discovering, composing, and monitoring
services, IDECSE addresses the challenge of fully automating the discov-
ery, composition and monitoring processes while reducing development
time and cost. IDECSE appeals for data mining techniques, namely For-
mal Concept Analysis, for classifying and mining services into service
registry in order to anticipate relevant services search and reduce ser-
vices search space.
Keywords: Web Service Composition, Semantic Web, Data mining.
1 Introduction
Web services are software applications that can be advertised, located and in-
voked across the Web. Nowadays, an increasing amount of organizations imple-
ment their business core and outsource their services over Internet. Thus the
ability to effectively select and integrate different services at run-time is an im-
portant step towards the development of Web service applications. If no single
Web service can satisfy the functionality required by the user, there should be a
possibility to create and compose new Web services from existing ones. Consid-
erable academic research and industrial efforts have focused on various aspects
of Web service composition ranging from service discovery, to composite ser-
vice specification and deployment. In this context, important initiatives have
been conducted to provide tools and languages that allow an efficient integra-
tion of heterogeneous services. Standard languages such as UDDI4 , WSDL5 , and
4
http://uddi.org/pubs/uddi
5
http://www.w3.org/TR/wsdl
106 Pre-proceedings of CAISE’14 Forum
SOAP6 were proposed to define standard ways for service discovery, description,
and invocation. WSBPEL7 has focused on representing service compositions
where the process flow and bindings between services are known a priori. Later
on, following the emergence of the Semantic Web and the fast growth of its re-
lated technologies, enhancing Web services description by a semantic level has
became one of the basic requirements for efficient services discovery and com-
position. Since then, several standardization efforts have been done to provide
languages which allow to semantically describe Web services on the one hand,
and which support efficient automation of the discovery and composition tasks
by formal reasoning on services description on the other hand. Standard lan-
guages, mainly Web Ontology Language for Services (OWL-S)8 and Web Service
Modeling Ontology (WSMO)9 , were proposed to allow considering semantic as-
pects in the description and reasoning about services. Based on such languages,
many frameworks was proposed for services composition and deployment [1–4].
Despite these efforts and progresses, Web services composition remains a chal-
lenging task for the following reasons. First, the number of available services is
dramatically increasing. This requires composition frameworks to be accurate,
scalable, and reliable to look up and select the most appropriate services for users
requirements. Second, services are developed by different organizations based on
different types of models and platforms. This heterogeneity in services modeling
creates semantic gaps between the presentation of their specification. This re-
quires composition frameworks to provide efficient tools to support bridging such
gaps. Third, Web services can be created and updated rapidly. This requires the
composition frameworks should be able to dynamically detect and interact with
such changes at run-time. Fourth, specifying composition requirements needs the
use of high-level languages that are easy to understand, in order to allow end
users to express their functional and non-functional requirements in an effective
way. Fifth, in case of failure to fulfill user’s goal, the composition process should
be able to iteratively refining the goal specification in an intuitive way to build
composite services. Finally, run-time monitoring and adaptation strategies are
primordial to ensure the correctness and the scalability of the composition envi-
ronments. While numerous composition approaches have been developed, very
little has been done towards dealing with these challenges. In this context, this
paper introduces IDECSE, Integrated Development Environment for Composite
Services Engineering, which considers semantics in all the composition steps: an-
alyzing user query, semantic classification of services in the registry, composing
services, and verifying the composition process. This approach aims to provide
an easy way to specify functional and non-functional requirements of composite
services in a precise and declarative manner, to guide the user through the com-
position process, while allowing modification or feedback, and finally to enable
generating outputs in a deployable language. The rest of the paper is organized
6
http://www.w3.org/TR/SOAP
7
http://www-106.ibm.com/developerworks/webservices/library/ws-bpel
8
http://www.w3.org/Submission/OWL-S
9
http://www.w3.org/Submission/WSMO
IDECSE: A Semantic Environment for Composite Services Engineering 107
as follows. Section 2, presents the architecture of the proposed framework and
details its modules. Section 3 briefly reviews the best known existing approaches
before comparing them to IDECSE. Section 4 concludes the paper and outlines
current and future work.
2 IDECSE Framework
The IDECSE follows the generic architecture of Web services composition frame-
works [5] which contain the following components: Translator, Process Gen-
erator, Evaluator, Execution Engine and Service Repository. Figure 1 shows
the main parts of a composition framework in a high-level of abstraction (i.e.
without considering particular algorithms, languages or platforms). The Service
Requester consumes services, following their requirements, offered by service
providers, whereas the service provider produces services and puts them into
the Service Repository (steps 1 and 2). The Translator translates between the
external languages used by the Requester and the internal languages used by the
Process Generator (steps 3 and 4). The Process Generator produces plans that
combine the available services from the service repository to satisfy the Request
(step 5). The evaluator then evaluates all produced plans and returns the best
one for execution(steps 6 and 7). Finally, the Execution Engine executes the plan
and returns the result to the service requester (step 8).
Fig. 1. General framework for Web service composition
IDECSE enhances this architecture to address the challenge of fully inte-
grating semantics in all stages of the composition global life-cycle. First user
requirements are more understood using refinement techniques such as gener-
alization or specification of concepts from a given ontology. Second, IDECSE
appeals for data mining techniques for classifying and mining services into ser-
vice registry based on semantic relations. Third, IDECSE is based on two types
of reasoners: a similarity-based reasoner and a logic-based one. The IDECSE ar-
chitecture is depicted in Figure 2. It consists of five modules covering the global
composition life-cycle (i.e. specification, classification, composition, deployment,
and monitoring ). These modules are described in the following sub-sections.
108 Pre-proceedings of CAISE’14 Forum
2.1 Service Request Module
The Service Request module translates user requirements to an internal language
to be used either by the Service Classification module or the Service Reasoning
module. The Graphical Query Editor relies on domain ontology to ”understand”
the requirements before enriching them through adding new ontology concepts
based on semantic relations such as generalization, specialization, etc. The Query
is then parsed to extract functional and non-functional requirements. Functional
requirements are modeled using the IOPE Extractor, which extract the Input,
Output, Precondition and Effects. Non-functional requirements are specified as
QoS parameters. Extracted user requirements are then modeled as a new re-
quested service called SR . Given a domain ontology O, a user query Q modeled
as SR , consists of a set of provided inputs SRin ⊆ O, a set of desired outputs
SRout ⊆ O, a set of preconditions SRpre ⊆ O, a set of effects SRef f ⊆ O, and a set
of quality of service constraints SRqos = {(q1 , v1 , w1 ), (q2 , v2 , w2 ), ..., (qk , vk , wk )},
where qi(i=1,2,...,k) is a quality criterion, vi is the required value for criterion qi ,
Pk
and wi is the weight assigned to this criterion such that i=1 wi = 1, and
k the Pnumber of quality
P criteria involved in the query. We can model SR as
SR = IOP E + QoS.
2.2 Service Classification Module
To deal with the important number of Web services and instead of considering
the whole service registry, this module allows to classify available services seman-
tically into classes according to their similarities. Its second role is to return only
relevant services to SR from the registry. The module contains four components
which are Service Projector, Service Description Extractor, Service Similarity
Calculator and Relevant Service Selector. The Service Projector selects services
capabilities based on syntactical and semantic description for each service (i.e.
WSDL and OWL-S description for each service) into one interface. The Service
Description Extractor, extracts useful parameters from service capabilities. The
Service Similarity Calculator Sub-Module is based on data mining techniques
for classifying services into classes according to their relevance and similarity
in order to anticipate relevant services search and reduce services search space.
The Formal Concept Analysis (FCA) [6] formalism and its extension to com-
plex data called Similarity-based Formal Concept Analysis (SFCA) [7] are used
as data mining techniques for this purpose. This module contains three main
components:
1. the Context Builder is responsible for preparing the input data-set to the
classification module. It selects the main properties of Web Services and
creates a tabular representation where the rows correspond to the Web Ser-
vices, the columns correspond to the services capabilities (descriptions) such
as type of input or the ontology that the input refers to, and finally table
cells contain values of these properties for each service.
IDECSE: A Semantic Environment for Composite Services Engineering 109
Fig. 2. IDECSE Architecture
2. the Similarity Comparator relies on a set of mathematical formulas based on
the semantic distance between concepts from the Context Builder and then
between services in the registry.
3. the Lattice Builder enables to compute the lattice structure corresponding
to the input table generated by the Context Builder. The lattice structure
reflects services grouping possibilities based on their common or similar prop-
erties.
Once the lattice is built, services are grouped into classes according to their
similarities. The Relevant Service Selector identifies the most relevant classes of
services from the lattice (lattice interpreter), which is more likely to answer the
query. The set of relevant services can then be outputted with the appropriate
rank with respect to the query needs (Service Ranking) to the next module.
110 Pre-proceedings of CAISE’14 Forum
2.3 Service Reasoning Module
The Service Reasoning module identifies the candidate composition plans that
realize the goal through a similarity-based reasoner or a logic-based one. The
Similarity Reasoner is based on the value of semantic similarity calculated by
Service Similarity Comparator module. When the semantic similarity value be-
tween SR and one or more existing services in the registry is higher than a given
threshold then SR is satisfied. Otherwise the Logic Reasoner is called to identify
the plan of services that achieves the goal of SR . The main components of the
Logic Reasoner module are:
– Reasoner: checks the ontology consistency in addition to handling the main-
tenance of state including preconditions evaluation and effects application.
– Filter: avoids redundancy from the plan by identifying service types with
potential relevance to the goal and checks the dependency relationships be-
tween each two consecutive service types.
– Matchmaker: allows querying the service registry for available services in
order to match the preconditions of a Web service with the effects of another.
– Abstract Planner: It can be considered as the main component of this module
and is responsible for generating a set of abstract plans.
To determine an Abstract Plan, the composition is reduced to a planning prob-
lem. A Plan is formalized as a proof of the goal to answer the user query. A Plan
P = {Ai }i=1..n is a sequence of n actions. Each action Ai applies on a state Ei
to produce a state Ei+1 : ∀i ∈ {0, .., n − 1}, Ei ∧ Ai |= Ei+1 . Starting from the
initial state E0 the plan P produces the goal G: E0 ∧ P |= G.
2.4 Service Execution Module
The service Execution Module translates the abstract plan into an executable
one by associating to each service type its specific instances using the Service
Instances Registry. The plan generated by the logic reasoner is considered as
a template for the composite service and drives the process of matching each
service type to a corresponding service instance. The Service Execution Module
is mainly composed of two main components: The Executable Plan Generator
considers non-functional requirements of the goal and enables to concretize the
abstract plan generated by the Service Reasoning Module. The Executable Com-
position Analyzer generates executable code and invokes the execution engine
in the Service Monitoring module. Different works was proposed in order to im-
plement the Executable Plan Generator, we can use for example the algorithm
presented in [8]. This algorithm takes as input a composition plan, the QoS per-
missible values imposed by the user, and their weights and generates as output
a composition plan that satisfies the requirements of the user.
2.5 Service Monitoring Module
Monitoring deals with the actual execution of the composite service and is re-
sponsible for monitoring the execution and recording violation of any require-
ment of the goal service at runtime. If a violation event occurs, an adaptation
IDECSE: A Semantic Environment for Composite Services Engineering 111
engine is triggered to handle this violation. There are two inputs to a monitor-
ing framework, a set of constraints that the process must obey and events or
messages generated during the execution. A processing engine ensures that all
events comply with the constraints and reports any exception.
3 Related Works
A considerable number of research efforts have focused on various aspects of
Web service compositions ranging from semantic service discovery to seman-
tic specification, deployment, and monitoring. MoSCoE (Modeling Web Service
Composition and Execution) [3] aims to provide a model-driven framework for
an automatically composition of services. MoSCoE allows service providers to
publish their services in a standard and semantic way and uses UML state ma-
chines for visually representing composite services. The Composition process
in MoSCoE is based on the three steps of abstraction, composition and refine-
ment. However user preferences and QoS were not addressed but only outlined
as future work. In [9], authors combine semantic service descriptions with the
invocations of the WSDL descriptions allowing to execute the composed services
on the Web. The process includes matching services to the user at each step
of a composition and filtering the possibilities by using semantic descriptions of
the services. The generated composition is then directly executable through the
WSDL grounding of the services. [10] presents a framework for service composi-
tion based on functional aspects, in which services are chained according to their
functional and semantic description (IOPEs). The proposed framework uses the
Causal Link Matrix (CLM) formalism in order to facilitate the computation of
the final service composition as a semantic graph. The set of possible solutions
are then pruned, at composition time, in order to rank the service compositions
according to some fixed criteria. These criteria can be defined based on the se-
mantic similarity of component services and/or the non-functional properties of
the compositions calculated by aggregating the non-functional properties of the
component services. In [11] and [12], Description Logics (DL) frameworks for
Semantic Web service composition are proposed. The specification of Semantic
Web services is reduced to preconditions, which define logical conditions that
should be satisfied prior to the service invocation and effects, which are the
result of the execution of the service. The proposed approaches for services com-
position use backward chaining search algorithms to find potential candidate
services. Thus the composition process is done automatically and dynamically.
IDECSE builds on the approaches mentioned above and aims to provide a
declarative approach to service composition engineering to achieve a full manip-
ulation of the composition process. IDECSE appeals for data mining techniques
for classifying and mining services into service registry in order to anticipate rele-
vant services search and reduce services search space. It also considers monitoring
and adaptation concerns, which are not incorporated in the above approaches.
IDECSE provides an easy way to specify functional and non-functional require-
112 Pre-proceedings of CAISE’14 Forum
ments of composite services in a semantic and declarative manner, guides the
user through the composition process, while allowing modifications or feedbacks.
4 Conclusion
This paper describes IDECSE, a new semantic integrated approach for com-
posite services engineering. Compared to existing approaches IDECSE consid-
ers semantics in all the composition global life-cycle, addresses the challenge of
fully automating the composition processes, uses data mining techniques such as
SFCA for classifying and mining services, and proposes new reasoning, monitor-
ing, and adaptation techniques. Our work in progress includes the improvement
and implementation of the different modules of the proposed architecture. We
also plan to extend the framework to include additional features such as failure
handling, and an interactive visual environment for testing composite services.
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