Doug Nebert

Bio: Doug Nebert is an academic researcher. The author has contributed to research in topics: Geospatial analysis & Cloud computing. The author has an hindex of 6, co-authored 7 publications receiving 440 citations.

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Abstract: The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential eleme...

Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon's EC2

Abstract: To test the utilization of cloud computing for Geosciences applications, the GEOSS clearinghouse was deployed, maintained and tested on the Amazon Elastic Cloud Computing (EC2) platform. The GEOSS Clearinghouse is a web based Geographic Metadata Catalog System, which manages millions of the metadata of the spatially referenced resources for the Global Earth Observations (GEO). Our experiment reveals that the EC2 cloud computing platform facilitates geospatial applications in the aspects of a) scalability, b) reliability, and c) reducing duplicated efforts among Geosciences communities. Our test of massive data inquiry by concurrent user requests proves that different applications should be justified and optimized when deploying onto the EC2 platform for a better balance of cost and performance.

Big Data and cloud computing: innovation opportunities and challenges

Abstract: Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

Scientific Data Management in the Coming Decade

Abstract: This is a thought piece on data-intensive science requirements for databases and science centers. It argues that peta-scale datasets will be housed by science centers that provide substantial storage and processing for scientists who access the data via smart notebooks. Next-generation science instruments and simulations will generate these peta-scale datasets. The need to publish and share data and the need for generic analysis and visualization tools will finally create a convergence on common metadata standards. Database systems will be judged by their support of these metadata standards and by their ability to manage and access peta-scale datasets. The procedural stream-of-bytes-file-centric approach to data analysis is both too cumbersome and too serial for such large datasets. Non-procedural query and analysis of schematized self-describing data is both easier to use and allows much more parallelism.

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Abstract: The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential eleme...

Interactive maps: What we know and what we need to know

Abstract: This article provides a review of the current state of science regarding cartographic interaction, a complement to the traditional focus within cartography on cartographic rep- resentation. Cartographic interaction is defined as the dialog between a human and map, mediated through a computing device, and is essential to the research into interactive car- tography, geovisualization, and geovisual analytics. The review is structured around six fundamental questions facing a science of cartographic interaction: (1) what is cartographic interaction (e.g., digital versus analog interactions, interaction versus interfaces, stages of interaction, interactive maps versus mapping systems versus map mash-ups); (2) why pro- vide cartographic interaction (e.g., visual thinking, geographic insight, the stages of sci- ence, the cartographic problematic); (3) when should cartographic interaction be provided (e.g., static versus interactive maps, interface complexity, the productivity paradox,flexibil- ity versus constraint, work versus enabling interactions); (4) who should be provided with cartographic interaction (e.g., user-centered design, user ability, expertise, and motivation, adaptive cartography and geocollaboration); (5) where should cartographic interaction be provided (e.g., input capabilities, bandwidth and processing power, display capabilities, mobile mapping and location-based services); and (6) how should cartographic interac- tion be provided (e.g., interaction primitives, objective-based versus operator-based versus operand-based taxonomies, interface styles, interface design)? The article concludes with a summary of research questions facing cartographic interaction and offers an outlook for cartography as a field of study moving forward.