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

Livelisystems: a conceptual framework integrating social, ecosystem, development and evolutionary theory

Human activity poses multiple environmental challenges for ecosystems that have intrinsic value and also support that activity. Our ability to address these challenges is constrained, inter alia, by weaknesses in cross disciplinary understandings of interactive processes of change in socio-ecological systems. This paper draws on complementary insights from social and biological sciences to propose a ‘livelisystems’ framework of multi-scale, dynamic change across social and biological systems. This describes how material, informational and relational assets, asset services and asset pathways interact in systems with embedded and emergent properties undergoing a variety of structural transformations. Related characteristics of ‘higher’ (notably human) livelisystems and change processes are identified as the greater relative importance of (a) informational, relational and extrinsic (as opposed to material and intrinsic) assets, (b) teleological (as opposed to natural) selection, and (c) innovational (as opposed to mutational) change. The framework provides valuable insights into social and environmental challenges posed by global and local change, globalization, poverty, modernization, and growth in the anthropocene. Its potential for improving inter-disciplinary and multi-scale understanding is discussed, notably by examination of human adaptation to bio-diversity and eco-system service change following the spread of Lantana camera in the Western Ghats, India

Tracking decision-making during architectural design

There is a powerful cocktail of circumstances governing the way decisions are made during the architectural design process of a building project. There is considerable potential for misunderstandings, inappropriate changes, change which give rise to unforeseen difficulties, decisions which are not notified to all interested parties, and many other similar problems. The paper presents research conducted within the frame of the EPSRC funded ADS project aiming at addressing the problems linked with the evolution and changing environment of project information to support better decision-making. The paper presents the conceptual framework as well as the software environment that has been developed to support decision-making during building projects, and reports on work carried out on the application of the approach to the architectural design stage. This decision-tracking environment has been evaluated and validated by professionals and practitioners from industry using several instruments as described in the paper