Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Trading Data for Discounts: An Exploration of Unstructured Data Through Machine Learning in Wearable Technology

The development of computing sensor devices with the capability of tracking an individual’s activity changed the way we live and move. The data collected and generated from wearable technology provides implications to the user for leading a healthy, more active lifestyle; however, the potential data uses extend beyond the user. Significant opportunity exists in the insurance industry as it relates to discounting premiums. The purpose of this research was to provide insight as to whether insurance companies should consider offering discount on premiums for policyholders who use wearable technology to track their personal fitness by identifying and suggesting potential groups of consumers to target these discounts toward. Using the platform R, researchers collected and analyzed tweets about four leading wearable technology companies including Fitbit, Jawbone, Misfit, and Withings. Both unsupervised and supervised learning techniques were pursued during the study in the form of topic modeling and artificial intelligence. Through detailed analysis, researchers determined that companies may want to consider reducing premiums for wearable technology users who use the devices for weight loss, as it would benefit both policyholders and insurance companies

Meetings and Meeting Modeling in Smart Environments

In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear