Chaowei Yang

Using spatial principles to optimize distributed computing for enabling the physical science discoveries

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.

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) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.

Distributed geospatial information processing: sharing distributed geospatial resources to support Digital Earth

Abstract This paper introduces a new concept, distributed geospatial information processing (DGIP), which refers to the process of geospatial information residing on computers geographically dispersed and connected through computer networks, and the contribution of DGIP to Digital Earth (DE). The DGIP plays a critical role in integrating the widely distributed geospatial resources to support the DE envisioned to utilise a wide variety of information. This paper addresses this role from three different aspects: 1) sharing Earth data, information, and services through geospatial interoperability supported by standardisation of contents and interfaces; 2) sharing computing and software resources through a GeoCyberinfrastructure supported by DGIP middleware; and 3) sharing knowledge within and across domains through ontology and semantic searches. Observing the long-term process for the research and development of an operational DE, we discuss and expect some practical contributions of the DGIP to the DE.

Redefining the possibility of digital Earth and geosciences with spatial cloud computing

Abstract Global challenges (such as economy and natural hazards) and technology advancements have triggered international leaders and organizations to rethink geosciences and Digital Earth in the new decade. The next generation visions pose grand challenges for infrastructure, especially computing infrastructure. The gradual establishment of cloud computing as a primary infrastructure provides new capabilities to meet the challenges. This paper reviews research conducted using cloud computing to address geoscience and Digital Earth needs within the context of an integrated Earth system. We also introduce the five papers selected through a rigorous review process as exemplar research in using cloud capabilities to address the challenges. The literature and research demonstrate that spatial cloud computing provides unprecedented new capabilities to enable Digital Earth and geosciences in the twenty-first century in several aspects: (1) virtually unlimited computing power for addressing big data storage, sharing, processing, and knowledge discovering challenges, (2) elastic, flexible, and easy-to-use computing infrastructure to facilitate the building of the next generation geospatial cyberinfrastructure, CyberGIS, CloudGIS, and Digital Earth, (3) seamless integration environment that enables mashing up observation, data, models, problems, and citizens, (4) research opportunities triggered by global challenges that may lead to breakthroughs in relevant fields including infrastructure building, GIScience, computer science, and geosciences, and (5) collaboration supported by cloud computing and across science domains, agencies, countries to collectively address global challenges from policy, management, system engineering, acquisition, and operation aspects.

Introduction to distributed geographic information processing research

Distributed geographic information processing (DGIP) refers to the processing of geographic information across dispersed processing units through computer networks and other communication channels. DGIP has become increasingly important in the past decade with the popularization of computer networks, the growth of distributed data repositories, and the collaboration of researchers, developers, and users among multiple disciplines using geographic information. DGIP focuses on the technical research on how to allocate and process geographic information resources in a distributed environment to achieve a specific application objective (such as the implementation of virtual globes). The geographic information resources may include sensors, geographic data, models, information, knowledge, visualization tools, computers, computer networks, software components, architecture, security strategies, applications, and human resources. This introduction to DGIP research defines six research areas: (a) DGIP architecture, including service-oriented architecture (SOA) and Federal Enterprise Architecture (FEA), (b) spatial computing issues for leveraging and allocating computing power to process geographic information, (c) geographic information-processing models for decoupling and integrating models for specific or cross application domains, (d) interoperability, defining the standards and interfaces for sharing processing units, (e) intelligence in DGIP for leveraging knowledge, and (f) applied sciences. The papers selected for this special issue cover all six areas. DGIP will become increasingly important with the globalization of our daily lives across planet Earth and the need to leverage distributed geographic information resources for problem solving and decision making in the global environment.

Utilize cloud computing to support dust storm forecasting

Abstract The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences. Dust storms have interannual variabilities and are typical disruptive events. The computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and costs. With the capability of providing a large, elastic, and virtualized pool of computational resources, cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance cluster. This paper examines the viability for cloud computing to support dust storm forecasting. Through a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing (HPC) cluster, we find that cloud computing is emerging as a credible solution for (1) supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting, (2) performing high-resolution dust storm forecasting when required, (3) supporting concurrent computing requirements, (4) supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex, 05 July 2011 as example, and (5) reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.

Evaluating the “geographical awareness” of individuals: an exploratory analysis of twitter data

A major theme in the geographical studies of social media content such as tweets from Twitter is to extract the locations of content providers (e.g., Twitter users) in order to track their movements or activity patterns. This framework also has been used to detect the dispersion of ideas over space and time. Another theme is to assess how the interaction of these providers may vary between the physical and virtual spaces. However, few geographical studies have explored if social media content can be used to examine the relationship between the characteristics of content providers and their geographical knowledge at different spatial scales. We expected that in general, ones awareness of the local geography should be higher than that of places farther away. In this paper, we explored if such pattern of geographical awareness in the physical space is reflected in the social media content. We reported our detailed examinations of tweets from a set of individuals who have provided substantial information in their profiles. Using text-mining methods, including natural language processing (NLP) techniques, we identified place names mentioned in the tweets and geocoded them. These locations were analyzed in a geographical-hierarchical manner to build a geographical awareness profile for each individual. While these geographical awareness profiles vary quite dramatically, their variations can be explained by the users’ characteristics, which were interpreted from their tweet content. This study demonstrates how social media content may be used to assess the geographical awareness characteristics of a biased sample population.

Evaluating open-source cloud computing solutions for geosciences

Many organizations start to adopt cloud computing for better utilizing computing resources by taking advantage of its scalability, cost reduction, and easy to access characteristics. Many private or community cloud computing platforms are being built using open-source cloud solutions. However, little has been done to systematically compare and evaluate the features and performance of open-source solutions in supporting Geosciences. This paper provides a comprehensive study of three open-source cloud solutions, including OpenNebula, Eucalyptus, and CloudStack. We compared a variety of features, capabilities, technologies and performances including: (1) general features and supported services for cloud resource creation and management, (2) advanced capabilities for networking and security, and (3) the performance of the cloud solutions in provisioning and operating the cloud resources as well as the performance of virtual machines initiated and managed by the cloud solutions in supporting selected geoscience applications. Our study found that: (1) no significant performance differences in central processing unit (CPU), memory and I/O of virtual machines created and managed by different solutions, (2) OpenNebula has the fastest internal network while both Eucalyptus and CloudStack have better virtual machine isolation and security strategies, (3) Cloudstack has the fastest operations in handling virtual machines, images, snapshots, volumes and networking, followed by OpenNebula, and (4) the selected cloud computing solutions are capable for supporting concurrent intensive web applications, computing intensive applications, and small-scale model simulations without intensive data communication.

Monitoring and evaluating the quality of Web Map Service resources for optimizing map composition over the internet to support decision making

Over the past 10 years, there have been great advances in the interoperability technologies in geographic information science. More than 10,000 map layers are available online today through Open Geospatial Consortium (OGC) specified interfaces, such as Web Map Service (WMS), Web Feature Service (WFS), and Web Coverage Service (WCS). These map layers are persistently serving the geospatial communities; however, our empirical study found that their potential value has not been fully exploited. Frequently, a targeted map cannot be composed because some published map servers are unavailable. This problem becomes more serious when a map is composed of several layers from different servers. These services are geographically distributed and maintained by various hosts; therefore, simply waiting for service improvement on the host side cannot solve this problem. In this paper, we proposed a new approach and developed a mechanism that allows clients to select the best map layers at run-time. The selection is based on the results of continuous monitoring and evaluation of the quality of WMSs. Based on Service Oriented Architecture (SOA), this approach includes quality monitoring and evaluation modules. Quality factors are taken into account during the process of registration, search, and bind. The OGC capability document is extended to include WMS quality information. Three prototype systems were developed in this study to demonstrate: (a) how WMS layers are monitored and evaluated, (b) how the subjective evaluation of WMS quality by a user is collected, and (c) how this can be a feasible method to fuse WMS resources suitable for decision making.

Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure

Increasing interests in a global environment and climate change have led to studies focused on the changes in the multinational Arctic region. To facilitate Arctic research, a spatial data infrastructure (SDI), where Arctic data, information, and services are shared and integrated in a seamless manner, particularly in light of todays climate change scenarios, is urgently needed. In this paper, we utilize the knowledge-based approach and the spatial web portal technology to prototype an Arctic SDI (ASDI) by proposing (1) a hybrid approach for efficient service discovery from distributed web catalogs and the dynamic Internet; (2) a domain knowledge base to model the latent semantic relationships among scientific data and services; and (3) an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining. A study of the influence of solid water dynamics to the bio-habitat of the Arctic region is used as an example to demonstrate the prototype.