Workshop on E-Business 

Abstract: On-demand provisioning of scalable and reliable compute services, along with a cost model that charges consumers based on actual service usage, has been an objective in distributed computing research and industry for a while. Cloud Computing promises to deliver on this objective: consumers are able to rent infrastructure in the Cloud as needed, deploy applications and store data, and access them via Web protocols on a pay-per-use basis. The acceptance of Cloud Computing, however, depends on the ability for Cloud Computing providers and consumers to implement a model for business value co-creation. Therefore, a systematic approach to measure costs and benefits of Cloud Computing is needed. In this paper, we discuss the need for valuation of Cloud Computing, identify key components, and structure these components in a framework. The framework assists decision makers in estimating Cloud Computing costs and to compare these costs to conventional IT solutions. We demonstrate by means of representative use cases how our framework can be applied to real world scenarios.

Abstract: The rapid growth in the number of ontologies has not met with the wide adoption of ontology in practice. Ontology evaluation can promote ontology use by facilitating the selection of a good ontology. Despite that a host of ontology evaluation methodologies are available, many of them are fragmentary and strongly tied to ontology development methodologies. Based on a review of extant ontology evaluation methods, we propose a framework for ontology evaluation. The framework provides a holistic view of ontology evaluation, suggesting both fundamental ontology dimensions and concrete criteria.

Abstract: M&A plays an increasingly important role in the contemporary business environment. Companies usually conduct M&A to pursue complementarity from other companies for preserving and/or extending their competitive advantages. For the given bidder company, a critical first step to the success of M&A activities is the appropriate selection of target companies. However, existing studies on M&A prediction incur several limitations, such as the exclusion of technological variables in M&A prediction models and the omission of the profile of the respective bidder company and its compatibility with candidate target companies. In response to these limitations, we propose an M&A prediction technique which not only encompasses technological variables derived from patent analysis as prediction indictors but also takes into account the profiles of both bidder and candidate target companies when building an M&A prediction model. We collect a set of real-world M&A cases to evaluate the proposed technique. The evaluation results are encouraging and will serve as a basis for future studies.

Abstract: Thirty four professionals who are part of a community of practice in the field of health related emergency response management provided information about the sources of information that they currently use the most, as well as their unmet information needs, and the kinds of information systems tools they would like to have. This professional community relies heavily on the Web, but they report severe information overload, in terms of not easily being able to find the kinds of information they want, amid the deluge of information that is there. In particular, they would find a system that uses social tagging and social recommender system features to be very useful for accessing relevant documents in the “gray literature.” We suggest that services such as these will be increasingly important for professional communities in general.