The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

Supporting social innovation through visualisations of community interactions

Online communities that form through the introduction of sociotechnical platforms require significant effort to cultivate and sustain. Providing open, transparent information on community behaviour can motivate participation from community members themselves, while also providing platform administrators with detailed interaction dynamics. However, challenges arise in both understanding what information is conducive to engagement and sustainability, and then how best to represent this information to platform stakeholders. Towards a better understanding of these challenges, we present the design, implementation, and evaluation of a set of simple visualisations integrated into a Collective Awareness Platform for Social Innovation platform titled commonfare.net. We discuss the promise and challenge of bringing social innovation into the digital age, in terms of supporting sustained platform use and collective action, and how the introduction of community visualisations has been directed towards achieving this goal

Automatically building research reading lists

All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting exist-ing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of cita-tions. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node’s im-portance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evalua-tion, including both an offline analysis and a user study, of the performance of the algorithms. Results from these stud-ies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists

Iterative Collaborative Ranking of Customers and Providers

This paper introduces a new application: predicting the Internet provider-customer market. We cast the problem in the collaborative filtering framework, where we use current and past customer-provider relationships to compute for each Internet customer a ranking of potential future service providers. Furthermore, for each Internet service provider (ISP), we rank potential future customers. We develop a novel iterative ranking algorithm that draws inspiration from several sources, including collaborative filtering, webpage ranking, and kernel methods. Further analysis of our algorithm shows that it can be formulated in terms of an affine eigenvalue problem. Experiments on the actual Internet customer-provider data show promising results

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas