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

Towards memory supporting personal information management tools

In this article we discuss re-retrieving personal information objects and relate the task to recovering from lapse(s) in memory. We propose that fundamentally it is lapses in memory that impede users from successfully re-finding the information they need. Our hypothesis is that by learning more about memory lapses in non-computing contexts and how people cope and recover from these lapses, we can better inform the design of PIM tools and improve the user's ability to re-access and re-use objects. We describe a diary study that investigates the everyday memory problems of 25 people from a wide range of backgrounds. Based on the findings, we present a series of principles that we hypothesize will improve the design of personal information management tools. This hypothesis is validated by an evaluation of a tool for managing personal photographs, which was designed with respect to our findings. The evaluation suggests that users' performance when re-finding objects can be improved by building personal information management tools to support characteristics of human memory

A literature review of connectedness to nature and its potential for environmental management

Understanding how people's relationships with nature form, how they influence personal values and attitudes, and what behavioural implications they may have could provide more insight into how connectedness to nature (CNT) can effectively contribute to environmental management goals. This paper undertakes a review of literature published over the past decade (2002e2011) on SCOPUS; and describes the current state of knowledge regarding CNT, assesses any efforts towards the spatial mapping of CNT for environmental management, and identifies measures of CNT defined in the broader literature. This review suggests that there is quite some overlap in the literature on CNT concepts, and that more effort needs to be made towards multi-disciplinary research which explores how CNT can be useful to environmental planning and conservation research on the field. It also further corroborates the need and relevance of applying more social and affective strategies to promote conservation behaviour. The main progress in CNT theory seems to have been made in the development of measurement tools, and it is clear that there is a strong convergent validity amongst the different measures due to their similarity, and functional associations. Further efforts towards the exploration of multi-dimensional measures is recommended since they consistently stand out as showing better results. The geographic visualisation of CNT constructs is another area of research that deserves attention since it can provide a unique point of view towards guiding participatory protected area planning and management.peer-reviewe

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield