Visualising London's Suburbs

2 - 4 April 200

GiViP: A Visual Profiler for Distributed Graph Processing Systems

Analyzing large-scale graphs provides valuable insights in different application scenarios. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data. We show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

Doppler lidar observations of sensible heat flux and intercomparisons with a ground-based energy balance station and WRF model output

This is an open access article - Copyright @ 2009 E. Schweizerbart'sche VerlagsbuchhandlungDuring the Convective and Orographically induced Precipitation Study (COPS), a scanning Doppler lidar was deployed at Achern, Baden-Wüttemberg, Germany from 13th June to 16th August 2007. Vertical velocity profiles ('rays') through the boundary layer were measured every 3 seconds with vertical profiles of horizontal wind velocity being derived from performing azimuth scans every 30 minutes. During Intense Observation Periods radiosondes were launched from the site. In this paper, a case study of convective boundary layer development on 15th July 2007 is investigated. Estimates of eddy dissipation rate are made from the vertically pointing lidar data and used as one input to the velocity-temperature co-variance equation to estimate sensible heat flux. The sensible heat flux values calculated from Doppler lidar data are compared with a surface based energy balance station and output from the Weather Research and Forecasting (WRF) model.Funding is obtained from NER

SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing

Mobile Cloud Computing (MCC) is the state-ofthe- art mobile computing technology aims to alleviate resource poverty of mobile devices. Recently, several approaches and techniques have been proposed to augment mobile devices by leveraging cloud computing. However, long-WAN latency and trust are still two major issues in MCC that hinder its vision. In this paper, we analyze MCC and discuss its issues. We leverage Service Oriented Architecture (SOA) to propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our architecture consists of three major layers, namely SOA, arbitrator, and infrastructure. The main strength of this architecture is in its multi-tier infrastructure layer which leverages infrastructures from three main sources of Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top of the infrastructure layer, an arbitrator layer is designed to classify Services and allocate them the suitable resources based on several metrics such as resource requirement, latency and security. Utilizing SAMI facilitate development and deployment of service-based platform-neutral mobile applications.Comment: 6 full pages, accepted for publication in IEEE MobiCC'12 conference, MobiCC 2012:IEEE Workshop on Mobile Cloud Computing, Beijing, Chin

Seamless Online Distribution of Amundsen Multibeam Data

Since 2003, all underway multibeam and sub-bottom data from the Canadian Coast Guard Ship Amundsen has been posted online within approximately six months of the end of each cruise. A custom interface allowing the user to access 15\u27 latitude by 30\u27 longitude mapsheets was implemented in 2006, allowing the user to download the bathymetric and backscatter data at 10 metre resolution. While this interface matched the underlying data management scheme implemented at the University of New Brunswick, the zoom and pan capability was at a fixed scale with limited contextual data. In the past few years, with the introduction of web-based geographic information systems (GIS) (e.g. Google Maps, Yahoo Maps, Bing Maps), there have been thousands of maps published online. These online GIS programs are a suitable platform to display the seven years of Amundsen coverage within the context of the GIS-served satellite imagery and allow the user to freely browse all data in a familiar interface. The challenge, however, for serving up third party data through these map engines is to efficiently cope with the multiple zoom levels and changing resolutions. Custom tiling software was developed to take all the raw data from the seven years of Amundsen (and others\u27) multibeam coverage and convert it into multiple scale resolution images suitable for interpretation by Google Maps. The images were stored in a pyramid structure utilizing Google\u27s map projection and uniquely named to reflect their georeferencing and resolution. This image pyramid is then accessed by Google Maps according to the user\u27s current zoom level to optimize visualization. This multi-resolution data is served up on demand from the University of New Brunswick for dynamic overlay on Google\u27s satellite data. This web interface allows any interested parties to easily view multibeam and sub-bottom data from the Pacific Ocean through the Canadian Arctic Archipelago and into the Atlantic Ocean. The broad overview helps to understand regional trends and then focus on areas of interest at high resolutions to see particular features. The web interface also provides a link to the 15\u27 by 30\u27 mapsheet model with full source traceability

A Network of Portable, Low-Cost, X-Band Radars

Radar is a unique tool to get an overview on the weather situation, given its high spatio- temporal resolution. Over 60 years, researchers have been investigating ways for obtaining the best use of radar. As a result we often find assurances on how much radar is a useful tool, and it is! After this initial statement, however, regularly comes a long list on how to increase the accuracy of radar or in what direction to move for improving it. Perhaps we should rather ask: is the resulting data good enough for our application? The answers are often more complicated than desired. At first, some people expect miracles. Then, when their wishes are disappointed, they discard radar as a tool: both attitudes are wrong; radar is a unique tool to obtain an excellent overview on what is happening: when and where it is happening. At short ranges, we may even get good quantitative data. But at longer ranges it may be impossible to obtain the desired precision, e.g. the precision needed to alert people living in small catchments in mountainous terrain. We would have to set the critical limit for an alert so low that this limit would lead to an unacceptable rate of false alarm

Planning Support Systems: Progress, Predictions, and Speculations on the Shape of Things to Come

In this paper, we review the brief history of planning support systems, sketching the way both the fields of planning and the software that supports and informs various planning tasks have fragmented and diversified. This is due to many forces which range from changing conceptions of what planning is for and who should be involved, to the rapid dissemination of computers and their software, set against the general quest to build ever more generalized software products applicable to as many activities as possible. We identify two main drivers – the move to visualization which dominates our very interaction with the computer and the move to disseminate and share software data and ideas across the web. We attempt a brief and somewhat unsatisfactory classification of tools for PSS in terms of the planning process and the software that has evolved, but this does serve to point up the state-ofthe- art and to focus our attention on the near and medium term future. We illustrate many of these issues with three exemplars: first a land usetransportation model (LUTM) as part of a concern for climate change, second a visualization of cities in their third dimension which is driving an interest in what places look like and in London, a concern for high buildings, and finally various web-based services we are developing to share spatial data which in turn suggests ways in which stakeholders can begin to define urban issues collaboratively. All these are elements in the larger scheme of things – in the development of online collaboratories for planning support. Our review far from comprehensive and our examples are simply indicative, not definitive. We conclude with some brief suggestions for the future

Modelling photochemistry in alpine valleys

Peer reviewe

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U