Abstract
The digitalization of youth signifies their complete immersion, active participation and involvement in the production, consumption and sharing of digital content using various interconnected/interfaced digital devices in their social network interactions. A prerequisite to successful commercial communication with young people is having a good understanding of new media, along with their social and psychological framework. The behaviour, motivation and emotions of youth in general and in relation to digital technologies, especially the meaning attached to mobile phones, the Internet (mainly social network sites) and games (computer-based and portable) should also be addressed if advertisers aim to reach this target group. © 2011, IGI Global.

Abstract
Web 2.0 and Enterprise 2.0 concepts offer a whole new set of collaborative tools that allow new approaches to market research, in order to explore continuously and ever fast-growing social and media environments. Simultaneously, the exponential growth of online social networks, along with a combination of computer-based tools, is contributing to the construction of new kinds of research communities, in which respondents interact with researchers as well as with each other. Furthermore, by studying the networks, researchers are able to manage multiple data sources - user-generated contents. The main purpose of this paper is to propose a new concept of Distributed Informal Information Systems for Innovation that arises from the interaction of the accumulated stock of knowledge emerging at the individual (micro) level. A descriptive study is to unveil and report when and how market research professionals use social networks for their work, creating, therefore, distributed information systems for innovation.

Abstract
The amount of information available in the MEDLINE database makes it very hard for a researcher to retrieve a reasonable amount of relevant documents using a simple query language interface. Automatic Classification of documents may be a valuable technology to help reducing the amount of documents retrieved for each query. To accomplish this process it is of capital importance to use appropriate pre-processing techniques on the data. The main goal of this study is to analyse the impact of pre-processing techniques in text Classification of MEDLINE documents. We have assessed the effect of combining different pre-processing techniques together with several classification algorithms available in the WEKA tool. Our experiments show that the application of pruning, stemming and WordNet reduces significantly the number of attributes and improves the accuracy of the results.

Abstract
Organ transplantation is a highly complex decision process that requires expert decisions. The major problem in a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straight-forward task because a complex network of relations exists between the immunological and the clinical variables that influence the receiver's acceptance of the transplanted organ. Currently the process of analysis of these variables involves a careful study by the clinical transplant team. The number and complexity of causal dependencies among variables make the manual process very slow. In this paper we assess the usefulness of Machine Learning algorithms as a tool to improve and speed up the decisions of a transplant team. We achieve that objective by analysing past real cases and constructing models as set of rules. Such models are accurate and understandable by experts. Copyright 2010 ACM.

Abstract
Collaborative Data Mining (CDM) develops techniques to solve complex problems of data analysis requiring sets of experts in different domains that may be geographically separate. An important issue in CDM is the sharing of experience among the different experts. In this paper we report on a framework that enables users with different expertise to perform data analysis activities and profit, in a collaborative fashion, from expertise and results of other researchers. The collaborative process is supported by web services that seek for relevant knowledge available among the collaborative web sites. We have successfully designed and deployed a prototype for collaborative Data Mining in domains of Molecular Biology and Chemoinformatics.

Abstract