Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally thought to execute programs in two phases: the initial warmup phase determines which parts of a program would most benefit from dynamic compilation, before JIT compiling those parts into machine code; subsequently the program is said to be at a steady state of peak performance. Measurement methodologies almost always discard data collected during the warmup phase such that reported measurements focus entirely on peak performance. We introduce a fully automated statistical approach, based on changepoint analysis, which allows us to determine if a program has reached a steady state and, if so, whether that represents peak performance or not. Using this, we show that even when run in the most controlled of circumstances, small, deterministic, widely studied microbenchmarks often fail to reach a steady state of peak performance on a variety of common VMs. Repeating our experiment on 3 different machines, we found that at most 43.5% of pairs consistently reach a steady state of peak performance.

Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of a wide variety of data formats, dataset sizes, data complexity, data use cases, and data sharing practices. Improvements in high throughput DNA sequencing, sustained institutional support for large sensor networks, and sky surveys with large-format digital cameras have created massive quantities of data. At the same time, the combination of increasingly diverse research teams and data aggregation in portals (e.g. for biodiversity data, GBIF or iDigBio) necessitates increased coordination among data collectors and institutions. As a consequence, “data” can now mean anything from petabytes of information stored in professionally-maintained databases, through spreadsheets on a single computer, to hand-written tables in lab notebooks on shelves. All remain important, but data curation practices must continue to keep pace with the changes brought about by new forms and practices of data collection and storage.

ABSTRACT Wireless sensor networks (WSNs) typically gather data at a discrete number of locations. However, it is desirable to be able to design applications and reason about the data in more abstract forms than in points of data. By bestowing the ability to predict inter‐node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. This interpolation capability is realised as a service of the network. In this paper, the ‘map’ style of presentation has been identified as a suitable sense data visualisation format. Although map generation is essentially a problem of interpolation between points, a new WSN service, called the map generation service, which is based on a Shepard interpolation method, is presented. A modified Shepard method that aims to deal with the special characteristics of WSNs is proposed. It requires small storage, can be localised and integrates the information about the application domain to further reduce the map generation cost and improve the mapping accuracy. Empirical analysis has shown that the map generation service is an accurate, a flexible and an efficient method. Copyright © 2011 John Wiley & Sons, Ltd.

In our exciting world of pervasive computing and always-available mobile internet, meeting the educational needs of students has seen a growing trend toward collaborative electronic and mobile learning systems that build on the vision of Web 2.0. However, other trends relevant to modern students must not be ignored, including data freedom, brokerage and interconnectivity. Such factors are associated with the Internet of Things and the vision for Web 3.0, and so include the needs for greater consideration of data context and educational personalization so important to the future of campus-based, distance and vocational study. Therefore, future education can be expected to require a deeper technological connection between students and learning environments, in a manner requiring significant use of sensors, mobile devices, cloud computing and rich-media visualization. This paper considers the challenges associated with adopting such a futuristic concept as a means of enriching learning materials and environments within a university context. It will be concluded that much of the technology required to embrace the vision of Web 3.0 in education already exists, but that further research in key areas is required for the concept to achieve its full potential.

Wireless sensor networks (WSN) have been useful in a variety of domains. These types of networks have an intimate interaction, via sensors, with the physical environment they operate within. The part of the world with which an application is concerned is defined as that application's domain. This paper advocates that the application domain can serve as a supplement to sense data analysis, interpretation, and visualisation methods and tools. To achieve this, we propose a multi-dimensional application domain-driven (M-DAD) information extraction and visualisation framework that uses the application domain to accurately visualise multimodal sense data. M-DAD harnesses the inherent redundancies and relationships among the collected sense data as information about a specific event of interest in a WSN is usually captured in multiple sensed modalities. The proposed mapping framework utilises these correlations, defined in the application domain, to visualise a sense modality, e.g. soil nitrate levels, using other related but independent sense modalities, e.g. temperature and pH, which results in higher accuracy visualisations than visualising from a single sense modality. The primary experimental results demonstrate that the proposed framework performs as well or better than methods without its extended capabilities.

Efficient and reliable information extraction in wireless sensor networks continues to be an area of abundant research. Current investigations follow two general themes: agent-based systems and query-based systems. The ASQue Sensor Query language (ASQue) was put forward by the Cogent Computing Research Centre as an applicative query language specifically designed to address the particular constraints imposed by sensor networks. A parser for the language and its formal specification were seen as an essential first step in being able to issue correct, purposeful queries to the network. A simple specification model that could be incrementally refined through to implementation was considered essential in accomplishing this goal. The purpose of this paper, therefore, is two-fold. First, to report the motivation for the formal specification of the ASQue parser, and second, to describe the construction of the parser from specification to implementation using the B-method.

The sciences often find it difficult to present complex ideas to audiences in ways that are informative and interesting. This is problematic, when the public needs to enter into a debate about new ideas, during conferences when vast amounts of information on diverse subjects are presented in a very short time, and in lectures when experts present fundamental principles to novices. Given our relatively short attention spans, boredom thresholds and limited capacity to assimilate new information, it is essential that the medium is an effective conveyor of the message, and in itself helps the receiver to remain alert and focused whilst also presenting information in a clear and unambiguous manner. The development of a new area of research, cogent computing, provided an opportunity to produce an effective, animation-based information communication tool, in the form of an application scenario, to be used for communicating to students, wider audiences and the design and development team itself, technical aspects of pervasive computing