Designing a General Architecture for Data Interchange

Alina Andreica

, Josef Küng

, Gabriela Şerban Czibulla

and Christian Sacarea

Babeş-Bolyai University, Cluj-Napoca, Romania

Johannes Kepler Uniersity, Linz, Austria

Keywords: Data Interchange Model, Academic Data Exchange, Software Solution.

Abstract: The paper describes principles for designing a general framework for automatic data interchange that scopes

all three levels, data, semantic and knowledge. In spite of the huge amount of research already performed

and existing standards and products, there is room to enhance information and knowledge integration.

Consequent to defining the data interchange framework, we are going to apply these principles in

developing and implementing a solution for academic data interchange. Such a solution has the potentiality

for important advantages in academic cooperation and societal benefits.

1 INTRODUCTION AND

WORKING FRAMEWORK

Interoperability is the capability of different systems

to share functionalities or data (Olmedilla et al,

2006). System interoperability has been dealt with

by means of various models (Morris et al, 2004) and

has been extensively researched for business

processes (Ziemann, 2010).

Interoperability may be achieved by: system

integration (Chapman, Kihn, 2009), which has also

been mainly tackled in the literature for business and

organizational processes (Hasselbring, 2000),

(Hasselbring, Pedersen, 2005), a semantic approach,

or data exchanges, with important results for

business processes. Interoperability issues have to be

considered on a three layer basis.

1. A first layer concerns data interoperability.

For this, several standards have been developed, like

XML and SQL standards. They also solve syntactic

interoperability issues. In the last decade, many

instruments have been developed, mainly driven by

the necessity of solving Business-to-Business and e-

commerce problems. For instance, ebXML (OASIS,

2007) has been developed as a new, global standard

for Internet-based B2B e-commerce (Gibb,

Damodaran, 2002). Also, several specifications have

been developed, like AS2 for secure and reliable

transport of data over the Internet, AS4 (AS4 web,

2012) for B2B documents exchange using Web

services, using XML encryption and XML Digital

Signature, or ebMS, a communication mechanism

which has to be implemented for business document

exchange within the ebXML standard.

The Electronic Data Interchange (EDI) model

(Adams et al, 2002) was proposed for standardizing

business information exchange, various formats

being used in this respect: ANSI X.12, XML

(cXML, xCBL, OpenTrans, UBL).

XML (eXtended Markup Language) (XML web,

2010) is a widely used standard for information

structuring and information exchange. XML is often

used as a format for data exchange and integration

between web applications or services within

organizations, and accordingly, integration of XML

data has become an important research problem.

Studies regarding the XML definitions dedicated to

representing database structures already lead to a

XML representation of a database and algorithms for

obtaining it (Abiteboul et al, 2000), (Bourret, 2000).

Analytical processing of data distributed in the

World Wide Web is an important topic for current

research (Heflin, Stuckenschmidt 2012). XML data

integration (Le et al, 2006), (Algergawy et al, 2010)

or RDF-based integration (Karnstedt et al, 2012)

involves reconciliation at different levels: (1) at

schema level, reconciling different representations

of the same entity or property, and (2) at instance

level, determining if different objects coming from

different sources represent the same real-world

entity. An important problem to be solved for XML-

based data integration is the support for integrated

access to the data using advanced query processing

capabilities (Manolescu et al., 2001), (Sattler et al,

214

Andreica A., Küng J., ¸Serban Czibulla G. and Sacarea C..

Designing a General Architecture for Data Interchange.

DOI: 10.5220/0004964002140219

In Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST-2014), pages 214-219

ISBN: 978-989-758-023-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2005). The unique correspondence between the

database schema and its XML representation is

tackled and proved in (Andreica et. al, 2005).

Current approaches for analytical processing on

integrated (Algergawy et al, 2011), (Heise,

Naumann, 2011) or distributed (Vouros et al, 2010)

data, sometimes based on cloud or grid architectures,

give good frameworks for application development.

The learning objects model LOM and the

SCORM standard (SCORM web, 2012) have been

improved in various directions: personalized

adaptive learning frameworks based on user profiles

(Arroyo et al, 2006); activating learning objects

with DBLink (Kassahun et al, 2006); describing how

learning objects should be used within VC-LOM

(Virtual Campus-LOM) (Di Nitto et al, 2006);

universal interoperability layer for educational

networks with Simple Query Interface (SQI).

Currently, various learning standards (Walker,

2012) are being used: standards for moving content

(IMS Common Cartridge (CC-MS web, 2012),

SCORM 2004’s Content Management Component),

data standards, like SIF Association www.sifinfo.org

- and PESC (PESC-CEDS web, 2012), integration

standards - PESC’s Data Transport Standard, SIF

(SIF Specifications web, 2012), IMS Learning Tool

Interoperability (LTI-IMS web, 2012), AICC-CMI

(AICC web, 2012) and some of them creating

interoperability contexts, such as SCORM, DCMI,

PESC or Ed-Fi. The Systems Interoperability

Framework (SIF) Association has achieved relevant

results, proposing specifications for event reporting,

data provisioning, messages and agents.

Among messaging standards, we enumerate:

RosettaNet (Rosetta web, 2011), ebMS (ebXML

Messaging Service) (OASIS, 2007), AS2

(Applicability Statement 2) (AS2 web, 2012).

2. The second layer is the semantic layer.

Semantic interoperability is the ability to share the

meaning of electronic documents using computer

systems. Knowledge discovery, inference, logic are

enabled by semantic interoperability. Semantic

interoperability is mainly achieved using ontologies

(Dicheva et al, 2005), (Yuan et al, 2010).

The Resource Description Framework (RDF)

(Lassila, Swick, 1999), a data model of metadata

instances, is recommended by the W3C (World

Wide Web Consortium) in order to solve semantic

interoperability problems. SHOE (Simple HTML

Ontology Extensions) (Heflin, Hendler, 2000)

introduces an ontology-based knowledge

representation language designed for the Web that

supports interoperability by sharing and reusing

ontologies. The Open Group (Open web, 2012)

focuses on developing a particular open standard to

facilitate semantic interoperability: the Universal

Data Element Framework – UDEF (UDEF web,

2012). UDEF framework is integrated with the

Resource Description Framework (RDF) and

especially used for describing business operations.

3. The third layer is the knowledge layer and it

addresses the conceptual structure of the shared data,

information and knowledge. Knowledge sharing

over computer information systems is a major task

for the interoperability approach. The first step is to

identify and automatically discover knowledge. This

process is based on a common cultural and social

background, enabling the user to identify and use the

extracted knowledge. Technically, the process uses

the previous two layers, and is based on the

Conceptual Knowledge Processing paradigm

(Stumme, Wille 2000). Transparency about the

identified knowledge has to be ensured. Active

knowledge systems enable to capture and represent

knowledge, as well as to reason and to draw

appropriate conclusions (Hitzler, Schärfe, 2009).

We consider that the Open Internet of Things

standards (OpenIoT, 2013) may be used as an

efficient framework for data interchange.

2 DATA INTERCHANGE

PRINCIPLES

We aim at proposing a general data interchange

model as a data interchange and interaction model,

valid for various fields. The framework we design is

based on the principles described below.

We use standards for the agent based system

development, and for the communication between

the agents within the multi-agent system, in the form

of FIPA-ACL (FIPA web) messages.

We adapt agent-oriented methodologies - like

Gaia (Wooldridge et al, 2000) - for MAS

development influence interoperability within the

system. Ontologies (Gruber, 1993) are used for

achieving semantic interoperability in the multi-

agent educational system. Ontologies are powerful

tools for sharing knowledge sources in a scalable,

adaptable and extensible manner and as well for

reaching semantic interoperability among

heterogeneous, distributed systems.

We use a multi-agent architecture (Weiss, 1999)

for designing the data exchange model proposal in

order to benefit from the advantages that agent based

technology offers: decentralization, extensibility,

robustness, maintainability, flexibility. In order to

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preserve confidentiality of the interchanged data, as

well as for security reasons, the mobile agent

(Nwana, 1996) technology is used. We provide self-

adapting communicating objects, which work on

distributed datasets not only supporting the

exchange but also the analysis of distributed sources.

The knowledge layer is based on the Conceptual

Knowledge Processing paradigm, which makes use

of concept lattices, i.e., knowledge maps displaying

concepts and their hierarchies, with a clear semantic

and a very high expressivity. The methods used in

this respect are knowledge discovery, knowledge

acquisition, knowledge development, knowledge

distribution and knowledge sharing. They are based

on Formal Concept Analysis (Ganter et al, 2005),

the mathematical theory of concepts and their

hierarchies, which is a widely accepted standard of

knowledge processing and representation.

We use the Open Internet of Things standards

(OpenIoT, 2013) as a framework for data

interchange; the “Utility/Application Plane” and

“Virtualized Plane” (OpenIoT, 2013) layers provide

a flexible framework for information communication

and exchange, including cases of cloud hosted data.

3 ACADEMIC APPLICATION

CASE

A specific application case of the data interchange

model we are developing is the academic field. In

this respect, system inter-operability capabilities,

based on data interchange techniques, provide

support for student or teacher mobility and e-

learning content exchange, relevant for networks of

universities with common programs. We note that e-

learning content exchanges are important mainly in

partner educational networks.

We take into account specific academic

information, such as study levels, specializations,

courses, students, teachers, grades, as well as e-

learning content resources. This information

standardisation has important benefits for

exchanging information in various systems,

including cloud services. Efficient data interchange

components increase academic activity proficiency,

with important societal benefits.

The proposed model for academic information

exchange –fig. 1, supports semantic interoperability.

This framework may also be used for advanced data

analysis. We propose a Reference Interaction Model

for defining the data content that is needed to

provide an explicit representation of semantic and

lexical connections that exist between academic

entities and we implement intelligent agents. The

interaction framework among the functional

components of the academic information system

environment is built using actors and transactions.

We define Integration Profiles for sharing

information within academic institutions and across

networks. Integration Profiles address data analysis

and interoperability issues related to information

access for academic actors and students, academic

workflow, security and administration infrastructure,

as well as for potential community or business

actors. Each profile defines the actors, transactions

and information content required to fulfil common

interactions between academic entities or services

provided to community stakeholders.

Multi-agent systems (MAS) are appropriate for

modelling the academic domain, which involves

interactions between various organizations with

different (possibly opposite) goals (Shen, Barthes,

2001), where flexible autonomous actions are

required for achieving the goals. Ontologies and

agent technologies may be combined in order to

successfully enable heterogeneous knowledge

sharing. We develop components for exploring and

reasoning on large-scale educational data to better

understand learners' educational evolution, assess

their progress and evaluate learning environments.

We use abstract methods for agents’ database

access (Han, Kamber, 2006), and particularize them

for certain standard academic information system

technologies. We exploit the advantages of using

intelligent agents as a support for the active data

mining (Agrawal, Psaila, 1995). For example, when

new data is added, a triggering agent can notify the

main mining application, so that new data can be

compared to the already mined data. Another

scenario regards sending alerts and notifications in

critical situations (i.e., possible frauds).

Data mining (Han, Kamber, 2006) and knowledge

discovery techniques are used to find interesting

relationships and patterns within academic data (e.g.

attributes of students, assessments). Machine

learning techniques, such as reinforcement learning

(Sutton, Barto, 1998), are useful for learning

teaching strategies in an adaptive and intelligent

educational system. Intelligent triggering software

agents are efficient for sending alerts in critical

situations (i.e., frauds, etc.).

We are implementing distributed data mining

techniques (Domenico, Trunfio, 2010) (e.g. fraud

detection, students’ profiling) within the educational

system. The agent based model is used within the

distributed data mining architecture, the mobile

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Figure 1: Heterogeneous sources, General Information Model and Semantic Background Knowledge.

agent model being useful for preserving academic

data confidentiality.

Machine learning techniques (Mitchell, 1997) are

used in order to enrich the educational system with

the characteristics of intelligence and adaptability.

We develop and use computational intelligence and

machine learning techniques (e.g. learning and

teaching, intelligent tutoring, adaptive information

filtering, etc): relational association rules mining

(Şerban et al, 2008) for predictive modelling (e.g

predicting the most appropriate specialization for a

student, predicting the students’ grades, etc); fuzzy to

deal with imprecision, uncertainty, partial truth;

unsupervised classification techniques, such as

clustering (Jain, 2010) and self-organizing maps

(Kohonen et al, 2001) in order to uncover hidden

patterns within academic data (students’

profiling,identifying groups of students sharing

common interests, etc); reinforcement learning

techniques (Sutton, Barto, 1998) in order to discover

users’ (students, teachers, etc. ) preferences, to

recommend specific tasks to students, according to

their preferences, to develop optimal teaching

strategies by adapting tutoring to students’ needs;

approaches that ensure privacy - e.g. (Tran, Küng,

Quoc 2011) proposes a particular k-anonymity

technique that does not affect the association rule

mining quality.

We are developing intelligent techniques for data

cleaning within academic data collections, using

techniques that detect and correct data errors. Data

cleaning is an important pre-processing step in a

data mining process; various computational

intelligence techniques such as relational association

rules (Şerban et al, 2008) are used in this respect.

Existing SIF (SIF Specifications web) standards

are adapted for performing the exchange,

management and integration of electronic academic

information. It is important to develop means and

specifications that ensure messaging standards for

academic transactions in order to achieve

interoperability. Such standards increase the

effectiveness and efficiency of academic information

delivery within and among academic organizations.

4 CONCLUSIONS

We describe the principles we are applying in

designing a general framework for data interchange

between information systems. The general data

interchange model is extremely useful for ensuring

entity cooperation in various fields. The model will

be applied for the academic field.

The data interchange model we design is going

to provide: advanced data exchange services using

self-adaptive software communicating objects which

provide academic IT services within distributed

architectures; a standardization framework which

supports information management and data

exchange, ensuring interoperability in software

technologies and services; a platform independent

software solution for academic data exchange, with

important social and collaborative advantages – as a

flexible software tool for sustaining academic

communication and cooperation. The solution meets

all privacy requirements of academic institutions and

national laws.

The application case for the academic field will

provide important advantages for increasing

academic competitiveness, with a significant societal

impact on academic institutional cooperation,

student and teacher exchanges, efficient information

Information

Model University 1

Information

Model University 2

Information

Model University 3

General

Information Model

Semantic

Back

round

DesigningaGeneralArchitectureforDataInterchange

217

management and access to data analysis facilities for

academic and community stakeholders. A relevant

advantage of the solution is its flexibility and

efficiency, including real-time response features,

efficient information exchange (only relevant data is

exchanged), with minimal resources involved.

REFERENCES

Abiteboul, S., Hull, R., Vianu, V., 1995. Foundations of

Databases, Addison-Wesley Publishing Company.

Abiteboul, S., Buneman, P., Suciu, D., 2000. Data on the

Web: From Relations to Semistructured Data and

XML, Morgan Kaufmann Publishers.

Adams, S.; Hardas, D.; Iossein, A. and Kaiman, C., 2002.

BizTalk Unleashed. Indianapolis, Indiana: Sams

Publishing. p. 966.

Agrawal, R., Psaila, G., 1995. Active Data Mining, KDD

1995, p. 3-8.

Aggarwal, R; Charu C.; Wang, H. (Eds.), 2010. Managing

and Mining Graph Data, Advances in Database

Systems 40, Springer.

AICC – CMI Guidelines for Interoperability AICC

http://aicc.org/docs/tech/cmi001v4.pdf, Acc Dec 2012.

Algergawy, A., Mesiti, M., Nayak, R.; Saake, G., 2011.

XML data clustering: An overview, ACM Comput.

Surv. 43(4), p. 25.

Algergawy, A., Nayak, R; Saake, G, 2010. Element

similarity measures in XML schema matching. Inf.

Sci. 180(24), p. 4975-4998.

Andreica, A-B; Stuparu, D; Mantu, I, 2005. Symbolic

Modelling of Database Representations, Proceedings

of the International Symposium on Symbolic and

Numeric Algorithms for Scientific Computing

SYNASC 2005, P.59 - 62.

Aroyo, L., Dolog, P., Houben, G-J., Kravcik, M., Naeve,

A., Nilsson, M., Wild, F., 2006, Interoperability in

Personalized Adaptive Learning, Educational

Technology & Society, 9 (2), 4-18.

AS2 –MIME-based Secure Peer-to-Peer Business Data

Interchange Using HTTP, Applicability Statement 2

(AS2), RFC 4130, http://tools.ietf.org/html/rfc4130 ,

Accessed December 2013.

AS4 – AS4: Web Services for B2B, GS1 eTG White

Paper, 2011 http://www.gs1.org/docs/ecom/AS4_-

_A_new_tools_for_the_B2B_toolbox.pdf.

Bourret, R. P., 2000. XML Database Products,

http://www.rpbourret.com/xml/ , acc November 2011.

CC-IMS – “Common Cartridges Frequently Asked

Questions”, IMS Global Learning Consortium,

http://www.imsglobal.org/cc/ccfaqs.html#3, Dec 2012.

Chapman, C, Kihn, L-A., 2009. Information system

integration, enabling control and performance,

Accounting, Organizations and Society, Volume 34,

Issue 2, P. 151–169.

Chen, J Y; Lonardi, S (Eds), 2009. Biological Data

Mining, Chapman and Hall/CRC; 1st edition.

Dang, T. K., Küng, J., Van Quoc, P. H., 2011. Protecting

Privacy While Discovering and Maintaining

Association Rules,Conference: New Technologies,

Mobility and Security - NTMS, IEEE, p 1-5.

Dicheva, D., Sosnovsky, S., Gavrilova, T., Brusilovsky,

P., 2005. Ontological Web Portal for Educational

Ontologies, Proc. of the Workshop on Applications of

Semantic Web Technologies for e-Learning.

Di Nitto, E., Mainetti, L., Monga, M., Sbatella, L.,

Tedesco, R., 2006. Supporting Interoperability and

Reusability of Learning Objects: The Virtual Campus

Approach, Educational Tech. & Society, 9 (2), 33-50.

FIPA – The Foundation for Intelligent Physical Agents,

www.fipa.org , Accessed December 2012.

Ganter, B., Stumme, G., Wille, R., 2005. Formal Concept

Analysis - Foundations and Applications, Springer.

Gibb, B. K., Damodaran, S., 2002. ebXML: Concepts and

Applications, John Wiley & Sons, Inc. New York.

Gruber, T. R. 1993, A translation approach to portable

ontology specifications. Knowledge Acquisition,

volume 5, pp. 199–220.

Han, J., Kamber, M., 2006. Data Mining: Concepts and

Techniques, The Morgan Kaufmann Series in Data

Management Systems, Morgan Kaufmann Publishers.

Han, J.; Gao, J., 2009. Research Challenges for Data

Mining in Science and Engineering, H. Kargupta et al.,

(eds.), Next Generation of Data Mining, Chapman &

Hall, 3-28.

Han, J., Kamber, M., Pei, J., 2011. Data Mining: Concepts

and Techniques, 3rd edition, Morgan Kaufmann.

Hasselbring, W., 2000. ”Information System Integration”,

Communications of the ACM, 43 (6). pp. 32-36.

Hasselbring, W., Pedersen, S., 2005. “Metamodelling of

Domain-Specific Standards for Semantic

Interoperability”, Lecture Notes in Computer Science,

3782, pp. 557 – 559.

Heflin, J., Hendler, J., 2000. Semantic Interoperability on

the Web http://www.cs.umd.edu/projects/plus/SHOE/

pubs/extreme2000.pdf.

Heflin, J., Stuckenschmidt, H., 2012, Web-scale semantic

information processing,. J. Web Sem. 10, p. 1-2.

Heise, A., Naumann, F., 2012. Integrating open

government data with stratosphere for more

transparency, J. Web Sem. 14, p. 45-56.

Hitzler, Pascal, Schärfe, Henrik, 2009, Conceptual

Structures in Practice, Taylor and Francis Group.

HIS-Building A Roadmap for Health Information Systems

Interoperability for Public Health, 2007. Dec.2012

http://www.ihe.net/technical_framework/upload/ihe_p

hdsc_public_health_white_paper_2007_10_11.pdf.

Hong, Tzung-Pei; Chen, Chun-Hao; Tseng, Vincent S.,

2010, A Summary of Genetic-Fuzzy Data Mining

Techniques, GrC 2010, pp. 28-29.

IHE – Integrating the Healthcare Enterprise, www.ihe.net,

Accessed December 2012.

IMS CC – IMS Common Cartridge

http://www.imsglobal.org/cc/, Access December 2012.

Jain, Anil K., 2010, Data clustering: 50 years beyond K-

means. Pattern Recogn. Lett. 31, 8 (June 2010), pp.

651-666. DOI=10.1016/j.patrec.2009.09.011.

WEBIST2014-InternationalConferenceonWebInformationSystemsandTechnologies

218

Karnstedt, M., Sattler, K-U, Hauswirth M., 2012. Scalable

distributed indexing and query processing over Linked

Data, J. Web Sem. 10, p. 3-32.

Kassahun, A., Beulens, A., Hartog, R., 2006. Providing

Author-Defined State Data Storage to Learning

Objects, Educational Tech. & Society, 9(2), 19-32.

Kohonen, T., Schroeder, M. R. and Huang, T. S. (Eds.) ,

2001. Self-Organizing Maps (3rd ed.). Springer-

Verlag New York, Inc., Secaucus, NJ, USA.

Lassila, O., Swick, R., 1999. Resource Description

Framework (RDF) Model and Syntax Specification,

W3C (World-Wide Web Consortium)

http://www.w3.org/TR/1999/REC-rdf-syntax-

19990222 , Accessed December 2013.

Le, D. X., Rahayu, J. W., and Pardede, E., 2006. Dynamic

approach for integrating web data warehouses. In

Proc. of International Conference on Computational

Science and Its Applications (ICCSA). 207–216.

LTI-IMS – “Learning Tools Interoperability”. IMS Global

Learning Consortium , Accessed December 2012

http://www.imsglobal.org/toolsinteroperability2.cfm ,

Manolescu, I., Florescu, D., Kossmann, D., 2001.

Answering XML Queries on Heterogeneous, VLDB

'01 Proceedings of the 27th International Conference

on Very Large Data Bases. 241-250.

Mitchell, Tom, 1997. Machine Learning, McGraw Hill.

Morris, E., Levine, L., Meyers, C., Place, P., Plakosh, D.,

2004. System of Systems Interoperability (SOSI): Final

Report, Software Engineering Institute, Carnegie

Mellon University, Technical Report CMU/SEI-2004-

TR-004, http://www.sei.cmu.edu/reports/04tr004.pdf.

Nwana, H.S., 1996. Software Agents: An Overview.

Knowledge Engineering Review.

OASIS – OASIS ebXML Messaging Services Version 3.0:

Part 1, Core Features, 2007. http://docs.oasis-

open.org/ebxml-msg/ebms/v3.0/core/ebms_core-3.0-

spec.pdf , Accessed December 2012.

Open – The Open Group, Accessed December 2012

http://www.opengroup.org/subjectareas/si.

OpenIoT - Open Internet of Things architecture

https://github.com/OpenIotOrg/openiot/wiki/OpenIoT-

Architecture , Accessed December 2013.

PESC-CEDS, PESC Common Education Data Standards

http://www.pesc.org/interior.php?page_id=208

Accessed December 2012.

Rosetta – Rosettanet Overview: Clusters, Segments, and

PIPs (ver 02.13.00), 2011 http://www.rosettanet.org/

TheStandards/RosettaNetStandards/PIPOverview/tabi

d/3482/Default.aspx.

Sattler, K., Geist, I., Schallehn, E., 2005. Concept-based

Querying in Mediator Systems. VLDB Journal, 14(1),

97–111.

SCORM – Shared Content Object Reference Model

http://scorm.com/ , Accesed December 2012.

Shen, W., Norrie, D.H., Barthes, J. A., 2001, Multi-Agent

System for Concurrent Intelligent Design and

Manufacturing, Taylor & Francis.

SIF Specifications, http://www.sifinfo.org/us/sif-

specification.asp, Accessed December 2012.

Sutton, R S., Barto, A G, 1998. Reinforcement Learning:

An Introduction, A Bradford Book. The MIT Press,

Cambridge, Massachusetts.

Stumme, G., Wille, R., 2000. Begriffliche

Wissensverarbeitung / Conceptual Knowledge

Processing, Springer Verlag.

Şerban, G., Czibula, I. G., Câmpan, A., 2008. Medical

Diagnosis Prediction using Relational Association

Rules, International Conference on Theory and

Applications of Mathematics and Informatics

(ICTAMI'07), Alba-Iulia, pp. 339-352.

UDEF – Universal Data Element Framework,

http://www.opengroup.org/udef/, Acc December 2013.

Vouros, G. A., Papasalouros, A.; Tzonas, K., Valarakos, A

G., Kotis, K., Quiané-Ruiz, J-A.; Lamarre, P.,

Valduriez, P., 2010. A semantic information system for

services and traded resources in Grid e-markets,

Future Generation Comp. Syst. 26(7), p. 916-933.

Walker: E., 2012. Primer on K-20 Education

Interoperability Standards, Publication of the

Software & Information Industry Association.

Witten, I. H., Frank, E., Hall, Mark A., 2011. Data

Mining: Practical Machine Learning Tools and

Techniques (3 ed.), Elsevier, ISBN 978-0-12-374856-0.

Weiss, G. E., 1999. Multiagent Systems: A Modern

Approach to Distributed Artificial Intelligence, MIT

Press.

Wooldridge, M., Jennings, N. R., Kinny, D., 2000. The

Gaia Methodology for Agent-Oriented Analysis and

Design, Autonomous Agents and Multi-Agent

Systems 3, pp. 285-312.

XML standard, http://www.w3.org/TR/xml11/#charsets ,

accessed November 2012.

Yuan, L., Wilson, S., Cooper, A., Campbell, L. M., 2010.

The Future of Interoperability and Standards in

Education – System and Process, CETIS White paper.

Ziemann, Jörg, 2010. Architecture of Interoperable

Information Systems - An Enterprise Model-Based for

Describing and Enacting Collaborative Business

Processes, Logos Verlag, Berlin.

DesigningaGeneralArchitectureforDataInterchange

219