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

Digital entrepreneurship in a resource-scarce context: A focus on entrepreneurial digital competencies

Purpose – Thepurpose of this paper is to criticallyexplorehow context asan antecedent to entrepreneurial digital competencies (EDCs) influences digital entrepreneurship in a resource-scarce environment. Design/methodology/approach – The data comprises semi-structured interviews with 16 digital entrepreneurs, as owner-managers of small digital businesses in Cameroon. Findings – The results reveal the ways in which EDCs shape the entry (or start-up) choices and post-entry strategic decisions of digital entrepreneurs in response to context-specific opportunities and challenges associated with digital entrepreneurship. Research limitations/implications – The data comes from one African country and 16 digital businesses thus the research setting limits the generalisability of the results. Practical implications – This paper highlights important implications for encouraging digital entrepreneurship by focussing on institutional, technology and local dimensions of context and measures to develop the entrepreneurial and digital competencies. This includes policy interventions to develop the information and communication technology (ICT) infrastructure, transport and local distribution infrastructure, and training opportunities to develop the EDCs of digital entrepreneurs. Originality/value – Whereas the capabilities to adopt and use ICTs and the internet by small businesses have been examined, this is among the first theoretically sensitised study linking context, EDCs and digital entrepreneurship

Slashdot, open news and informated media: exploring the intersection of imagined futures and web publishing technology

"In this essay, my interest is in how imagined media futures are implicated in the work of producing novel web publishing technology. I explore the issue through an account of the emergence of Slashdot, the tech news and discussion site that by 1999 had implemented a number of recommendation features now associated with social media and web 2.0 platforms. Specifically, I aim to understand the connection between the development of Slashdot’s influential content-management system (CMS) - an elaborate publishing infrastructure called “Slash” that allowed editors to choose reader submissions for publication and automatically distributed the work of moderating the comments sections among trusted users - and two distinct visions of a web-enabled transformation of media production.

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars