Chaowei Phil Yang

Performance‐improving techniques in web‐based GIS

WebGIS (also known as web‐based GIS and Internet GIS) denotes a type of Geographic Information System (GIS), whose client is implemented in a Web browser. WebGISs have been developed and used extensively in real‐world applications. However, when such a complex web‐based system involves the dissemination of large volumes of data and/or massive user interactions, its performance can become an issue. In this paper, we first identify several major potential performance problems with WebGIS. Then, we discuss several possible techniques to improve the performance. These techniques include the use of pyramids and hash indices on the server side to handle large images. To resolve server‐side conflicts originating from concurrent massive access and user interactions, we suggest clustering and multithreading techniques. Multithreading is also used to break down the long sequential, layer‐based data access to concurrent data access on the client side. Caching is suggested as a means to enhance concurrent data access for the same datasets on both the server and the client sides. The technique of client‐side dynamic data requests is used to improve data transmission. Compressed binary representation is implemented on both sides to reduce transmission volume. We also compare the performance of a prototype WebGIS with and without these techniques.

An optimized framework for seamlessly integrating OGC Web Services to support geospatial sciences

OGC Web Services (OWS) are essential building blocks for the national and global spatial data infrastructure (NSDI and GSDI) and the geospatial cyberinfrastructure (GCI). Web Map Service (WMS), Web Feature Service (WFS), Web Coverage Service (WCS), and Catalogue Service for Web (CSW) have been increasingly adopted to serve scientific data. Interoperable services can facilitate the integration of different scientific applications by searching, finding, and utilizing the large number of scientific data and Web services. However, these services are widely dispersed and hard to be found and utilized with acceptable performance. This is especially true when developing a science application to seamlessly integrate multiple geographically dispersed services. Focusing on the integration of distributed OWS resources, we propose a layer-based service-oriented integration framework and relevant optimization technologies to search and utilize relevant resources. Specifically, (1) an AJAX (Asynchronous JAvaScript and eXtensible Markup Language)-based synchronous multi-catalogue search is proposed and utilized to enhance the multi-catalogue searching performance; (2) a layer-based search engine with spatial, temporal, and performance criteria is proposed and used for identifying better services; (3) a service capabilities clearinghouse (SCCH) is proposed and developed to address the service issues identified by a statistical experiment. A science application of data correlation analysis is used as an example to demonstrate the performance enhancement of the proposed framework.

Mining geophysical parameters through decision-tree analysis to determine correlation with tropical cyclone development

Correlations between geophysical parameters and tropical cyclones are essential in understanding and predicting the formation of tropical cyclones. Previous studies show that sea surface temperature and vertical wind shear significantly influence the formation and frequent changes of tropical cyclones. This paper presents the utilization of a new approach, data mining, to discover the collective contributions to tropical cyclones from sea surface temperature, atmospheric water vapor, vertical wind shear, and zonal stretching deformation. A decision tree using the C4.5 algorithm was generated to illustrate the influence of geophysical parameters on the formation of tropical cyclone in weighted correlations. From the decision tree, we also induced decision rules to reveal the quantitative regularities and co-effects of [sea surface temperature, vertical wind shear], [atmospheric water vapor, vertical wind shear], [sea surface temperature, atmospheric water vapor, zonal stretching deformation], [sea surface temperature, vertical wind shear, atmospheric water vapor, zonal stretching deformation], and other combinations to tropical cyclone formation. The research improved previous findings in (1) preparing more precise criteria for future tropical cyclone prediction, and (2) applying data mining algorithms in studying tropical cyclones.

Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery

Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upon in this paper as a critical step for extracting usage patterns. However, reconstructing user sessions from raw web logs has always been difficult, as a session identifier tends to be missing in most data portals. To address this problem, we propose two session identification methods, including time-clustering-based and time-referrer-based methods. We also present the workflow of session reconstruction and discuss the approach of selecting appropriate thresholds for relevant steps in the workflow. The proposed session identification methods and workflow are proven to be able to extract data access patterns for further pattern analyses of user behavior and improvement of data discovery for more relevancy data ranking, suggestion, and navigation.

Forming a global monitoring mechanism and a spatiotemporal performance model for geospatial services

Geographic information service (GIService) has become popular in the last decade to develop applications for addressing global challenges. Performance is one of the most important criteria to help users select distributed online GIService for developing geospatial applications including natural hazards and emergency responses. However, performance accuracy is limited by the single-location-based evaluation mechanism while service performance is dynamic in space and time between end-users and services. We propose a spatiotemporal performance evaluation mechanism to improve the accuracy. Specially, a cloud and volunteer computing mechanism is proposed to collect performance information of globally distributed GIServices. A global spatiotemporal performance model is designed to integrate spatiotemporal dynamics for better performance evaluation for users from different regions at different times. This model is tested to support GIService selection in global spatial data infrastructures (SDIs). The experiment confirms that the proposed model provides more accurate evaluations for global users and better supports geospatial resource utilizations in SDIs than previous mechanisms. The methodology can be adopted to improve the services of other regional and global distributed operational systems.

Introduction to big geospatial data research

The phenomenal growth of digital data is posing increasing challenges for both scientists and engineers. The challenge is multifaceted due to the ever-growing size, constant flow, high dimension and trustworthiness of the content. This special issue captures several state-of-the-art technological research on improving understanding geospatial science changes brought about by the availability of both new types of data such as social media data and advanced computing technologies such as geospatial cyberinfrastructure and cloud computing.

Implementing computing techniques to accelerate Network GIS

We have witnessed the accumulation of petabyes of geospatial data in the past decades, and, currently, terabytes of data are collected every day. These geospatial data are crucial in supporting decision making and emergency response. It becomes increasingly important to deliver these datasets in a timely fashion to the decision support or emergency response systems. Network GIS provides a vehicle to facilitate this delivering process. But to deliver efficiently such large volume of data and to handle large number of concurrent users, the performance of Network GIS needs to be improved to a level that different types of applications, especially near real time applications, can be satisfied. In this circumstance, we 1) review selected research on improving the performance of Network GIs; 2) provide insides on implementing the techniques; and 3) illustrate how to adopt the techniques in Network GIs. We expect the research and development reported here can be easily adopted by different users to accelerate the performance of various Network GIS software and applications, as well as to support the building of spatial data infrastructure to support the sharing of heterogeneous geospatial information.

Spatial Web Portal for Building Spatial Data Infrastructure

Abstract The past decades have witnessed the rapid growth of heterogeneous geospatial information systems. An important way to share these valuable assets is a spatial data infrastructure (SDI). Recent developments in Web Services and distributed geospatial information services(Yang et al., 2005) provide a practical approach, Web Portals, to building a SDI. This article describes research, development, and challenges related to Web Portals for SDI.

Chapter 10 – Polar CI Portal: A Cloud-Based Polar Resource Discovery Engine

Abstract The Polar Regions are going through rapid and dramatic changes. These changes have significant global impacts on both the environment and society. Nevertheless, the Polar Regions are still the largest observational data voids on the planet, and polar-related resources are usually distributed across different online systems. This chapter introduces the Polar Cyberinfrastructure (CI) Portal, a one-stop portal that makes it easy for users to discover, share, and access polar resources. The polar resource discovery engine integrates a set of capabilities: (1) semantic-based searching, (2) service quality evaluation, (3) polar-friendly and user-friendly visualization, and (4) scalability of cloud computing. We start by discussing the background and challenges of geospatial cyberinfrastructure for Polar Regions, and then explain the architecture and each building block of the Polar CI portal system. A detailed discussion of the status and functions of key components is included to demonstrate the advantages of the system and its integrated techniques.

An architecture for mitigating near earth object's impact to the earth

Near-Earth Objects (NEOs), like species extinction events, present a great threat to our home planet and human kind. The motivation of designing this architectural framework is the current lack of structured architecture for the process of detecting, characterizing and mitigating these NEO threats. Due to the recent establishment of the NASAs Planetary Defense Coordination Office (PDCO), it is critical to link the individual facilities conducting separate research with an objective of forming a clearly defined collaborative system based on data reporting and sharing. The architectural framework is designed for integrating the process of detecting, characterizing and mitigating NEO threats. The goal of designing the architecture is to organize current data and resources into useful information and correlate that information with the goals of the NEO mitigation study. The architectural framework will enable scientists, organizations, and decision makers to locate, identify and resolve semantic confusion, properties, facts, constraints and issues with potentially hazardous asteroids. Our major focus is to design the data and information flow that models the complete process from NEO detection, to designing the mitigation strategies. A secondary focus is to develop a system-of-systems architecture to describe the supporting infrastructure for the framework. The framework is also built with the opportunity to leverage future assets from the broader Planetary Defense (PD) community, and identify/speed up relevant PD research and response.i