DESIGNING AND IMPLEMENTING AN EGOVERNMENT ARCHITECTURE THAT ENSURES
QUALITY OF SERVICE AND LEGITIMACY
C. Tatsiopoulos1, Dr.-Ing. G. Vardangalos1 and P.Gouvas2
1,2
European Profiles S.A., 11B Kodrigktonos Str. Athens, Greece
[email protected], [email protected],
[email protected]
ABSTRACT
The QUALEG Project (Quality of Service & Legitimacy in eGovernment - IST507767) aims at enabling
Local Governments to manage their policies in a transparent and trustable way. In this paper, it will be
proposed a flexible, open and modular architecture that can easily be extended. The suggested
architecture will fall in separate and discrete components which are dedicated to some functionalities. In
addition, technologies that were evaluated and will be possibly used for implementation are described.
KEYWORDS
E-government, quality of services, workflows, policy evaluation indicators, intelligent agents
1. INTRODUCTION
The QUALEG Project aims at enabling Local Governments to manage their policies in a
transparent and trustable way. In this paper, it will be proposed a flexible, open and modular
architecture that can easily be extended. The suggested architecture will fall in separate and
discrete components which are dedicated to some functionalities.
The QUALEG solution will be able to create and maintain a well structured representation of
knowledge in the e-government domain, to deliver efficient knowledge management on top of
the information sources, to promote personalized delivery of the most appropriate piece of
knowledge in a form and format that matches the standards of users’ interests, to provide secure
delivery of information to the citizens, to process the collected information and knowledge in
order to develop valuable indicators, and policy evaluation scorecards.
The user requirements have also been taken into account for a first approach of the QUALEG
architecture schema.
The main requirements addressed by the pilots of the project (City of Tarnow- Poland, City of
Nantes - France, City of Saarbrucken - Germany) are the following:
1
These authors have equally contributed to this paper
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The improvement of the process to collect citizen’s opinion through surveys using
questionnaires that will be used to define various scenarios of service offering
The improvement of the management of the data collected about citizen needs in order to
increase the information reliability and the reactivity in the delivery process
The need for access to the back-office legacy information systems and to e-Government
services
The increase of the participation of citizens by supporting debates between citizens,
politicians and civil servants and extracting from them knowledge and policy
recommendations.
The assessment of the satisfaction of citizens and re-formulation policy orientations on
offered services
The measurement of the performance and quality of the services, in order to improve them
and raise the level of citizens satisfaction
Special sections (members area) providing information only for registered users
Repository of information [documents, analyzes, legal acts, experts opinions, links]
2. GENERAL OVERVIEW
Many parameters have to be thoroughly taken into account for an architecture schema to be
formed. First of all, interoperability between modules and existing external legacy systems has
to be ensured. Moreover, reuse of existing modules and open source solutions has to be
considered. A faster implementation and future effortless integration will be possible if the
modules are defined in an autonomous way. So a clear and modular architecture is of major
importance.
Throughout the architecture’s specifications, a lot of constraints have to be taken into
consideration. The major one is the necessity of operating in three different pilot environments.
Each one of these pilots has an infrastructure that meets partially the demands of QUALEG. As
a result, the proposed architecture has to be thought as the outcome of the integration of
different systems, where a system is composed by subsystems and modules, able to carry out a
specific task.
Given the above, an elaborated approach to the architecture is proposed, as depicted in the
following diagram:
Figure 1 – The general QUALEG Architecture
It comprises mainly of the following components:
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Workflow Management System (WMS)
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Datamart
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Ontology Management System
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Questionnaires Composer
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AGORA Service
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Knowledge Extraction
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Intelligent Agents
3. ARCHITECTURE
In this section, we describe the components of the architecture in detail:
3.1. Workflow Management System (WMS)
Workflow Management System will integrate Web Services technology and Workflows. It will
enable representing and reconfiguring government processes by orchestrating existing Web
Services and Knowledge Sources.
Interactions-Dependencies
A complete Workflow workspace consists of a Workflow Composer, a Workflow Execution
engine and finally a Workflow Repository in order to store produced workflows.
The Workflow Composer tool is used to graphically design and specify a workflow. In most
cases, after a workflow design no extra work is necessary and it can be converted automatically
to an application by a code generator.
The composer is used to specify workflow topology, tasks, transitions (control flow and data
flow), data objects, task invocation, roles, and security domains. During the design phase, the
designer is shielded from the underlying details of the runtime environment and infrastructure,
separating the workflow definition from the enactment service on which it will be installed and
executed.
The Workflow Execution engine is used to install and administer workflow definitions
(schema), and to start workflow instances. When a workflow is installed, the engine activates all
of the necessary task schedulers to carry out the execution of instances. The execution engine is
implemented as an object and has an interface that allows clients to interact with it. The engine
does not participate in any task scheduling activities. It is only necessary at the time a new
workflow is installed or modified.
Web service registry is done by UDDI Server. The WMS repository is responsible for
maintaining information about workflow definitions and associated workflow applications. The
repository tool allows users to retrieve, update, and store workflow definitions. A user can
browse the contents of the repository and find already existing workflow definitions fragments
(either sub-workflows or individual tasks) to be incorporated into a workflow being created.
The repository service is also available to the enactment service; it provides the necessary
information about a workflow application to be started.
Semantic enrichment of Web Services (Semantic Web Engine)
Semantic Web Engine will enable exporting data from legacy / information systems thanks to a
meta-data model based on the specific reference ontology in order to publish a new Web service
or Knowledge source.
Web services are advertised in UDDI registries. The current mechanism for browsing web
services supported by UDDI is not powerful enough for automated discovery. This happens
because there is a lack of semantics in the discovery process. So UDDI is less effective, even
though it provides an interface for keyword and taxonomy based searching.
In order to browse semantic Web services, we can introduce semantics in the description itself
and then using semantic matching algorithms to find the required services. Ontologies have
been identified as the basis for semantic annotation that can be used for discovery. So in order
to enrich web services, we should follow a procedure for semantic specification, annotation,
discovery, composition and orchestration of Web services.
Technologies
For the web services orchestration, norms that can be used are WS-CAF, BPML, and
BPEL4WS that have compliance to the notion of web services choreography. In order to have
the implementation of the above norms, OSS blocks that should be chosen because of their
compliance to one of the selected norms are JBPM, Open for Business, OS Workflow.
For the semantic enhancement of the Web Services, in order to publish and then browse
semantically web services, two main blocks can be considered by now, BPEL4WS and DAMLS.
The Workflows should provide support in the Build-time functions, in the Run-time control
functions concerned with managing the workflow processes and in the Run-time interactions
with human users and IT application tools. Such Workflow OSS blocks are Bonita, Xflow and
WmfOpen.
3.2 Datamart
Datamart component will store indicators that relate both to performance of government
services, and satisfaction of citizens collected through questionnaires. Also this component will
monitor the workflow management system.
This component comprises of services and interfaces with other components.
Datamart Knowledge
Since indicators in specific fields have to be created, the necessity of knowledge extraction is
strong. Datamart knowledge is filled by Knowledge Extraction component by using knowledge
extraction algorithms. Citizens can perform semantic document search from QUALEG front end
by using semantic matching of the Datamart Knowledge. Given a specific ontology (created by
a knowledge expert), all electronic content such as forum-discussion-board data, emails have to
be classified dynamically and stored.
Datamart Indicators
Service performance indicators are stored in Datamart by Performance Analyser. Analyser
extracts performance indicators from the Web services handled in the system. A first set of
performance indicators will be based on the monitoring of the workflows that orchestrate the
Web services that wraps access to the underlying information systems and a second one will be
based on the direct handling of the Web Service meta-data to pick up statistical information
from the underlying information systems.
Citizen Satisfaction indicators are generated from the Policy ontology and will be associated to
available Public Services, Policy Action Plans and Policy Strategic Orientation.
Satisfaction Questionnaires are pushed to Citizens using the Agora Service and will address the
indicators generated and stored in the Policy Evaluation Scorecard.
Opinion analysis extracts knowledge from the content of the Agora service and reflects the
opinion of the various categories of users. It will rely on the identification of semantic
descriptors defined to the QUALEG ontology by parsing free text stored in the Agora service.
In addition, Policy Evaluation Indicators that are also stored in Datamart, are accessible by
Agora through a report generator.
Datamart Services
Datamart interfaces are responsible for communicating with external modules. At first,
Datamart takes into account the e-government domain ontology, which is created and
maintained by the Ontology Management component. Therefore a sufficient interface to this
component exists. Furthermore, since a major task of QUALEG is to ensure service
performance, a Datamart service is the monitoring of the workflows that coordinate the
semantic enhanced web services (Performance analyser).
Technologies
Datamart component comprises of several sub modules 1) Service Performance, 2) Citizens’
Satisfaction and 3) Opinion Analysis. Each one involves state of the art technology regarding
web services and databases. Since this component will store all indicators formed by external
modules a clear interface will exist in order to provide functionality to the AGORA component.
Since AGORA is actually QUALEG’s public interface, Service Performance, Citizens’
Satisfaction and Opinion Analysis will be published as web service.
3.3 Ontology Management System
The “Ontology Management System”, will analyze documents and information relating to a
policy in order to provide a reference ontology useable for indexing documents, and automating
access to Web Services and Knowledge Sources. An important issue will be the inclusion in the
ontology meta-data of a model for:
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Citizen expectations in relation to a given policy,
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Policy Strategic Orientation,
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The associated Policy Action Plan.
Interactions-Dependencies
Ontology Management system will be used for building an ontology suitable to the QUALEG
scope for both wrapping access to existing information systems and for indexing documents in
relation to quality of service, policy evaluation and policy orientation.
It should define semantic attributes for the enrichment of Web Services. This implies that there
is connectivity with Semantic Web Engine. The ontology will contain concepts relevant to the
public administration as well interests that the users declare that will be of importance to them
in relevance with policies executed by the public administrations.
The ontology will be initially structured in a tool, that will offer a way to represent it in a
relational Database and then probably via an API to be maintained, in a semi-automated way.
The produced ontologies are stored in the Ontology Repository. An additional GUI will be
provided to users in case they want to maintain manually the ontology.
In order to build the QUALEG Ontology, there should be a knowledge engineer working tightly
with the pilot user to define the concepts that should be organised. In order to identify the
concepts of the ontology, first of all interviews will take place between knowledge engineer and
pilot user. Then, the ontology with its concepts will be easier designed. A feedback from the
pilot user will be needed to confirm its consistency.
Technologies
Throughout the ontology creation there is a necessity for specific tools in order to support all
stages of ontology lifecycle, namely creating, populating, validating, deploying, maintaining,
and evolving. Such ontology management tools (commercial and not) were evaluated (KAON,
Protégé). Issues like storage, versioning should be confronted by the use of such a management
tool.
3.4 Questionnaires Composer
Specific services of the AGORA should be evaluated by the end-users. This task is undertaken
by the Questionnaire component. This component will generate a specific form per user (user
profiling) and QUALEG service (semi-automation). This form will be completed by the users in
order to assess each service.
Interactions-Dependencies
Questionnaires will be constructed according to the user that accesses the system which implies
a user profiling mechanism. Distribution of questionnaires should be done asynchronously
based on user profiling. The process of composing a questionnaire should derive from existing
QUALEG published services. The filled questionnaires are stored in a repository.
3.4.1 Questionnaire Analyzer (Policy Evaluation Scorecards)
A Policy Evaluation Scorecard (PES) will be used for storing the performance indicators.
Internal performance indicators will be “exported” exploiting WSDL for enabling transactional
application and stored to AGORA database. Also for both subcomponents, database schemas
have to be defined in relevance with existing infrastructures.
Technologies
Dynamic forms should be generated using an existing dynamic html technology like JSP and
PHP. Database connectivity should be accomplished with existing JDBC APIs.
3.5 AGORA Service
Agora Service will offer a Knowledge space for debates between Citizens, Politicians and
Parties, Civil Society. It provides access to policy evaluation indicators and a mechanism for
semantic document search. Agora Service also includes the published Questionnaires area and
common topics area (General info/Municipality information and news).
In order to foster the participation of Citizens in local democracy discussions a knowledge and
debate space, that we call an Agora will be developed. Online searches and thematic moderated
forums between different actors: Citizens, Politicians and Parties, Civil Society, Civil Servants
will be performed.
Interactions-Dependencies
Agora can be defined as a web portal that contains several modules. These modules will
facilitate QUALEG functionality. In particular, it will support:
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Registration Module
General Information Module
o General Information regarding the Municipality and the projects achieved and foreseen
o News Section: general and/or targeted news will be published and sent by email
 Profiling: SubGroup Perspective Module
o The user will be offered some services (information, news, etc) according to his
profile/interests.
o For the Communication facilities, only subgroup of users will be available according to
the role of the user connected.
 Search Module
o
o
o
o
Search amongst Topics/Forums
Simple Text search by entering KeyWords
Ontology driven Search : User has to respond to question built through the ontology
Transact with the Knowledge Base: Retrieve and/or Insert contents inside of it, exchange
Opinions with other users on the retrieved contents
 Communication Module : Discussions and Debates
 User Implication Module
o Contributions of Citizens both to Policy Evaluation (filling in questionnaires) and to the
thematic debates
o Proposals for new policy orientations and action plans.
o Access to formal data such as Policy Evaluation Indicators
 Administration Module
o Complete web based administration
Agora interacts with the Datamart module which is responsible for the definition and storage of
indicators. It also encompasses the satisfaction questionnaires that are pushed to citizens.
Finally, the information stored in AGORA Database is accessed by Knowledge Extraction
module and is parsed so as semantic descriptors, belonging to the Policy Ontology, to be
identified.
Figure 1 – AGORA interactions
Technologies
The best solution at the moment for implementing such an Agora Service will be to use an open
source content management tools like Jetspeed and PHPNuke. These tools provide a clear
development API so as external modules to be deployed (JSP portlet or PHP module).
3.6 Knowledge Extraction
It is a module that extracts from the debates, mails, docs semantic descriptors from free text
using the QUALEG ontology for parsing the text. This module will implement methods in
association to ontology representation. Generating knowledge requires combining techniques
used for text mining and for data mining to identify best practices.
Text mining relies on the use of ontologies for categorizing information. Data Mining facilities
are already widely used in knowledge based enterprises (learning enterprises) to get consistent
and useful access to corporate data bases. A major aim for combining these two approaches is to
define a common meta-data model that would enable representing results in a single knowledge
space using the QUALEG ontology as a reference meta-data model for both text and data
mining.
Interactions-Dependencies
Collector Agents should interact with Agora to get its content and perform analysis in order to
get information for the opinion of citizens for e-government services and give this information
to Knowledge Extraction module to identify semantic descriptors of the Policy Ontology.
Knowledge extraction module will perform algorithms for term extraction and concept
association extraction. It will be able to create and maintain ontologies through text-mining.
Text Mining uses unstructured textual information and tries to discover structure and implicit
meanings “hidden” within the text. The extracted “knowledge” is stored in a repository.
Technologies
The main approaches for the development of systems that aim to extract information and
knowledge from text are the performance-based approaches and knowledge-based ones. In the
former case, designers are concerned with the effective behaviour of the system and not
necessarily with the means used to obtain that behaviour. The most common performance-based
algorithms are statistical methods and neural networks.
On the other hand, knowledge-based systems use representations of knowledge, such as the
meaning of words, relationships between facts, and rules for getting in conclusions in domains.
Open Source blocks that semi-automatic create ontologies by applying text mining algorithms
are Text-to-Onto and Armadillo.
3.7 Intelligent Agents
The majority of QUALEG’s services act asynchronously. Therefore there is a strong need to
synchronize part of these services. For example, when new electronic data are created (news,
mails, forums) knowledge extraction tools should be notified. This procedure is a thread
oriented one. Every parallel application encounters multiple problems. Agents can undertake all
tasks that are parallel and thread-oriented.
Agents can be classified in groups that will be used into the system. These are Agents
responsible for triggering the knowledge extraction process and Agents responsible for
providing alerting mechanisms to the Citizens.
Interactions-Dependencies
Agent based software is demanding as far as dependencies are concerned. Agents interact both
with databases (AGORA’s Database and Datamart Database) and with the WMS.
Technologies
During the evaluation of the existing technologies, the most significant platforms have been
analyzed (JADE, Grasshoper, FipaOS). These platforms have a clear development API and
facilitate the implementation of the agents mentioned above. Furthermore these platforms assist
interaction with the ontology since they are semantically enriched.
4. CONCLUSIONS AND FUTURE WORK
In this paper, we proposed a flexible, open and modular architecture that can easily be extended.
The suggested architecture should fall in separate and discrete components which are dedicated
to specific functionalities.
The QUALEG solution will be able to create and maintain a well structured representation of
knowledge in the e-government domain, to deliver efficient knowledge management on top of
the information sources, to promote personalized delivery of the most appropriate piece of
knowledge in a form and format that matches the standards of users’ interests, to provide secure
delivery of information to the citizens, to process the collected information and knowledge in
order to develop valuable indicators, and policy evaluation scorecards.
In near future, many parameters have to be thoroughly taken into account in order to be formed
a final architecture schema. Interoperability has to be provided among the different QUALEG
modules and existing external legacy systems has to be ensured. Moreover, reuse of existing
modules and open source solutions has to be considered. A faster implementation and future
effortless integration will be possible if the modules are defined in an autonomous way. So a
clear and modular architecture is of major importance. Furthermore, the quality of service has to
be ensured, as well as the security during the interchange of data.
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