Grid-VirtuE: a layered architecture for grid virtual enterprises

A grid virtual enterprise is a community of independent enterprises concerned with a particular sector of the economy. Its members (nodes) are small or medium size enterprises (SME) engaged in bilateral transactions. An important principle of a grid virtual enterprise is the lack of any global "guiding force", with each member of the community making its own independent decisions. In this paper we describe Grid-VirtuE, a three-layer architecture for grid virtual enterprises. The top layer of the architecture, representing its ultimate purpose, is an environment in which grid virtual enterprises can be modeled and implemented. This layer is supported by middleware infrastructure for grids, providing a host of grid services, such as node-to-node communication, bilateral transactions, and data collection. The bottom layer is essentially a distributed data warehouse for storing, sharing and analyzing the large amounts of data generated by the grid. Among other functionalities, the warehouse handles the dissemination of data among the members of the grid; it confronts issues of data magnitude with an aging mechanism that aggregates old data at a lower level of detail; and it incorporates privacy-preserving features that retain the confidentiality of individual members. Warehouse information is also used for data and process mining, aimed at analyzing the behavior of the enterprise, and subsequently inducing evolutionary changes that will improve its performance.A grid virtual enterprise is a community of independent enterprises concerned with a particular sector of the economy. Its members (nodes) are small or medium size enterprises (SME) engaged in bilateral transactions. An important principle of a grid virtual enterprise is the lack of any global "guiding force", with each member of the community making its own independent decisions. In this paper we describe Grid-VirtuE, a three-layer architecture for grid virtual enterprises. The top layer of the architecture, representing its ultimate purpose, is an environment in which grid virtual enterprises can be modeled and implemented. This layer is supported by middleware infrastructure for grids, providing a host of grid services, such as node-to-node communication, bilateral transactions, and data collection. The bottom layer is essentially a distributed data warehouse for storing, sharing and analyzing the large amounts of data generated by the grid. Among other functionalities, the warehouse handles the dissemination of data among the members of the grid; it confronts issues of data magnitude with an aging mechanism that aggregates old data at a lower level of detail; and it incorporates privacy-preserving features that retain the confidentiality of individual members. Warehouse information is also used for data and process mining, aimed at analyzing the behavior of the enterprise, and subsequently inducing evolutionary changes that will improve its performance.Monograph's chapter

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Integrating information and knowledge for enterprise innovation

It has widely been accepted that enterprise integration, can be a source of socio-technical and cultural problems within organisations wishing to provide a focussed end-to-end business service. This can cause possible “straitjacketing” of business process architectures, thus suppressing responsive business re-engineering and competitive advantage for some companies. Accordingly, the current typology and emergent forms of Enterprise Resource Planning (ERP) and Enterprise Application Integration (EAI) technologies are set in the context of understanding information and knowledge integration philosophies. As such, key influences and trends in emerging IS integration choices, for end-to-end, cost-effective and flexible knowledge integration, are examined. As touch points across and outside organisations proliferate, via work-flow and relationship management-driven value innovation, aspects of knowledge refinement and knowledge integration pose challenges to maximising the potential of innovation and sustainable success, within enterprises. This is in terms of the increasing propensity for data fragmentation and the lack of effective information management, in the light of information overload. Furthermore, the nature of IS mediation which is inherent within decision making and workflow-based business processes, provides the basis for evaluation of the effects of information and knowledge integration. Hence, the authors propose a conceptual, holistic evaluation framework which encompasses these ideas. It is thus argued that such trends, and their implications regarding enterprise IS integration to engender sustainable competitive advantage, require fundamental re-thinking