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Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of these complex entities. Further, this book introduces a contribution to the definition of reference models for Collaborative Networks.

Collaborative Networks: Reference Modeling provides valuable elements for researchers, PhD students, engineers, managers, and leading practitioners interested in collaborative systems and networked society.

Inhaltsverzeichnis

Frontmatter

Introduction

1. Overview

The area of collaborative networks is already extended over two decades of research and development since the first results on virtual enterprises were published. A large number of research projects and pilot applications contributed to the worldwide establishment of the area since then, generating a vast amount of concepts, mechanisms, models, systems, approaches, etc. In order to facilitate its smooth progress, it is necessary to invest on a theoretical foundation that gives a solid basis for further developments.

2. Motivation for a theoretical foundation for collaborative networks

A growing number of collaborative networks can be observed in many domain areas. However, the developments and even the understanding of these cases have suffered from ad-hoc approaches, being urgent to establish a proper theoretical foundation for the area. Furthermore, the fast developments in the area and the nature of the paradigm configure the emergence of a new discipline, which needs to be built on a sounder theoretical basis.

3. Related work on reference modeling for collaborative networks

Several international research and development initiatives have led to development of models for organizations and organization interactions. These models and their approaches constitute a background for development of reference models for collaborative networks. A brief survey of work on modeling the enterprises, enterprise architectures, and early contributions to reference models of virtual enterprises is provided. Finally an identification of the main modeling requirements for collaborative networks is made.

Towards a CN reference model

4. Overview

A reference model for collaborative networks can be a very important instrument to both the education of the newcomers as well as facilitation of communication among researchers and practitioners involved in the area, and thus contributing to its smooth development and evolution. This section offers a contribution to the elaboration of a comprehensive reference model for CNs.

5. Reference modeling: Needs and basic terminology

A reference model for collaborative networks is a fundamental instrument for the smooth development of the area. It is therefore important to understand the reference modeling process and associated terminology. This chapters makes a brief historic analysis, introduces basic concepts and perspectives for reference modeling.

6. Collaboration forms

In order to facilitate a better understanding among professionals involved in collaborative networks, a clarification of the base concepts of networking, coordination, cooperation, and collaboration is made. A taxonomy of the main organizational forms of collaborative networks is introduced and working definitions for those forms are proposed.

7. The ARCON modeling framework

A framework is defined for ARCON reference modeling, introducing multiple modeling perspectives of: Environment characteristics, life cycle stages, and modeling intents. This novel modeling framework takes into account contributions from previous related works, mainly on enterprise modeling, and extends them further to the context of collaborative networked organizations, aiming at provision of a comprehensive environment for modeling the variety of cases of collaborative, namely the VO Breeding Environment, Virtual Organization, Professional Virtual Community, and Virtual Team.

8. ARCON reference models for collaborative networks

Following the ARCON modeling framework, a comprehensive set of concepts and entities, covering both the Endogenous Elements and Exogenous Interactions perspectives of collaborative networks, are collected and defined. Such collection represents a first proposal for a reference model for collaborative networks. The establishment of a recognized reference model is certainly a long-term activity of which this work represents a first step.

9. A comprehensive semantic indexing schema for ARCON

In order to formally and systematically address the elements in the ARCON models for CNs, a schema of their unique identification needs to be developed. This chapter introduces an approach for comprehensive and semantic “indexing” of both meta-elements, e.g. the Componential dimension of the Endogenous sub-space of ARCON’s reference modeling framework, and each individual element, e.g. the specific resource or market strategy belonging to the ARCON reference model of the CN. The main contribution of the introduced semantic indexing-schema is to the formalization process of the ARCON m. Furthermore, the indexing schema facilitates: (1) dynamic systematic evolution, (2) organized physical storage, (3) semi-automated processing and derivation of both elements and meta-elements of ARCON.
E. Ermilova, H. Afsarmanesh

10. Further steps on CN reference modeling

Establishing a reference model for Collaborative Networks is a long-term endeavor. The ARCON proposal is a first contribution for a comprehensive model but it needs to be continued and improved. A set of guidelines for an evolution process are defined and potential participants in this process are identified.

Modeling Tools And Approaches

11. Overview

Modeling is one of the key activities in understanding, designing, implementing, and operating software systems. It is at the very heart of any scientific and engineering activity. As such, many disciplines and research fields have developed a large portfolio of modeling theories, approaches, and tools, some of which can be potentially applied in the area of collaborative networks.
This section includes an analysis of the most promising contributions, covering a wide spectrum of modeling purposes. The second chapter introduces an extensive list of modeling tools and theories and, for each one, it offers a brief synopsis and key references. An applicability map is then introduced to help the reader identify which tools / approaches best fit his / her specific modeling needs.

12. A survey of modeling methods and tools

A large portfolio of modeling tools and theories, developed in different disciplines, have a good applicability potential in collaborative networks. A brief survey of those promising approaches and a set of bases references are presented. A map of their application potential is also included.

13. A survey of soft modeling approaches for collaborative networks

A large number of aspects in collaborative networks are difficult to capture with traditional modeling approaches due to the inherent imprecision and incompleteness of information. Soft modeling approaches are specifically developed to handle such cases and thus have a high potential to the establishment of more effective and close to reality models. Computational intelligence methods are complemented with other approaches such as qualitative reasoning, complexity theories, chaos theory, etc.

Modeling examples

14. Overview

Due to the multi-faceted perspectives of collaborative networks and the wide variety of complex aspects that need to be addressed, there is no single modeling formalism or theory that can properly cover all needed modeling aspects. Very often it is necessary to combine different modeling formalisms and/or theories in order to get a more holistic perspective of CNs.
Each modeling tool / system is usually developed to sufficiently cover certain aspects within its respective discipline(s). Therefore usually several independent modeling tools and/or systems are applied in research, to model different aspects of CNs. Nevertheless, while keeping their independence, some forms of interoperation / composition among these modeling tools and systems are necessary.

15. A multi-model approach to analyze inter-organizational trust in VBEs

The perceptions, preferences and interpretations of trust differ among the organizations depending on their purposes for establishing trust relationships with others. As a result, different organizations consider different aspects when assessing the trust level of other organizations. Thus a number of complex aspects must be addressed to comprehensively cover the trust objectives of organizations which in turn make it difficult to model and analyze these aspects. Consequently, it is hard to thoroughly cover the needed trust aspects by applying a single modeling tool, system or approach. Integrating models and supporting their interoperability, a challenge on its own, is suggested in this chapter for addressing the analysis of inter-organizational trust. This chapter analyzes and proposes a number of specific models that can be applied to comprehensively cover the fundamental aspects related to interorganizational trust in Virtual organizations Breeding Environments (VBEs).
S. S. Msanjila, H. Afsarmanesh

16. Networked partner selection with robust portfolio modeling

This chapter illustrates the applicability of mathematical decision-analysis in VO partner selection. The approach allows for multiple criteria, which can also relate to inter-organizational issues such as collaboration history between partner candidates. Moreover, the approach is soft in the sense that it allows interval parameter data, instead of point estimates. Using the RPM method, Pareto-efficient VO configurations can be identified and the robustness of the candidates can be analyzed. The results suggest that the models are very useful in practical decision-making situations.
T. Jarimo, K. Korpiaho

17. Modeling collaboration preparedness assessment

Information incompleteness and imprecision are typical difficulties when assessing the collaboration preparedness of a candidate to join a collaborative network. Bayesian belief networks and Rough Sets are examples of modeling approaches that can be used in these cases. The use of these approaches depends on the type of collaborative network considered, namely long term or goal oriented, and on the available data necessary to perform the assessment. Combination of different modeling techniques is also useful in this context. In order to illustrate the suggested approach, a number of modeling experiments are described and achieved results are briefly discussed.
J. Rosas, L. M. Camarinha-Matos

18. A benefit analysis model for collaborative networks

The identification and characterization of collaboration benefits is an important element for the wide adoption of the collaborative networks paradigm. In order to establish a basis for analysis of benefits in collaborative networks (related to the behavioral dimension in the ARCON reference model) this chapter introduces an approach for the analysis of benefits in collaborative processes for enterprise networks. The potential application of some suggested indicators and the emergence of a “collaborative spirit” based on the reciprocity mechanism derived from this analysis are also discussed in a VO breeding environment context.
A. Abreu, L. M. Camarinha-Matos

19. An approach in value systems modeling

Although Value Systems play an important role in collaborative networks, the concept is still ill defined. This chapter contributes to a formal model and analysis of value systems using various modeling formalisms. Examples of applicability of these models are also given.
P. Macedo, L. M. Camarinha-Matos

20. Selection of a virtual organization coordinator

Collaborative Networks (CNs) have created new needs from technological, organizational and human viewpoints in terms of models, methodologies, methods and work techniques, as well as in what concerns the involved resources – mainly the human ones. The modeling example presented in this work analyses the process of searching and selecting an individual to act as coordinator in an environment that results from this new business model. The example is also meant to support decision-making – ‘what to do’ and ‘how to do’ in order to guide an oriented search for individual competences to achieve an adequate management for a Virtual Organization (VO) that is being created or that has recently been created.
A. A. Pereira-Klen, E. R. Klen, L. Loss, J. A. Crispim, J. P. Sousa

21. Modeling the value of expectations in collaborative networked organizations

The goal of modeling the value of expectations in Collaborative Networked Organizations (CNO) is to review the project behind that CNO and have a basis to decide on whether to go on, to optimize, or to stop the project. In Virtual Organizations, where several actors work on common projects, expectations may differ widely: while one actor might believe in the chances of a project, others might see or face critical factors that may prohibit the project from succeeding. A schema on how to gather, cluster and evaluate expectations in a Virtual Organization can create an input for the decision process on the future of the target project.
S. Wiesner, F. Graser, K. D. Thoben

22. Prospective performance measurement in virtual organizations

The goal of prospective performance measurement is to support consortium building in Virtual Organizations. Through identification of possible partners and their potential contributions for realizing an order and comparison of possible consortia, the performance measurement can be used to identify and to evaluate the optimal network configuration. On the other hand, potential alternatives for partner selection can be identified and assessed, for example to guarantee the capacity to act, even if a partner omits. The crisp part of prospective performance measurement lies in recording well defined past performance data. This data is then used to forecast future performance by means of soft modeling. In a final step the forecast can be interpreted by traditional methods again.
M. Seifert, S. Wiesner, K. D. Thoben

Erratum

Without Abstract

Backmatter

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