Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

AN EXTENDABLE VISUALIZATION AND USER INTERFACE DESIGN FOR TIME-VARYING MULTIVARIATE GEOSCIENCE DATA

Geoscience data has unique and complex data structures, and its visualization has been challenging due to a lack of effective data models and visual representations to tackle the heterogeneity of geoscience data. In today’s big data era, the needs of visualizing geoscience data become urgent, especially driven by its potential value to human societies, such as environmental disaster prediction, urban growth simulation, and so on. In this thesis, I created a novel geoscience data visualization framework and applied interface automata theory to geoscience data visualization tasks. The framework can support heterogeneous geoscience data and facilitate data operations. The interface automata can generate a series of interactions that can efficiently impress users, which also provides an intuitive method for visualizing and analysis geoscience data. Except clearly guided users to the specific visualization, interface automata can also enhance user experience by eliminating automation surprising, and the maintenance overhead is also reduced. The new framework was applied to INSIGHT, a scientific hydrology visualization and analysis system that was developed by the Nebraska Department of Natural Resources (NDNR). Compared to the existing INSIGHT solution, the new framework has brought many advantages that do not exist in the existing solution, which proved that the framework is efficient and extendable for visualizing geoscience data. Adviser: Hongfeng Y

Explora : interactive querying of multidimensional data in the context of smart cities

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving-on ingestion time-synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach

AN EXTENDABLE VISUALIZATION AND USER INTERFACE DESIGN FOR TIME-VARYING MULTIVARIATE GEOSCIENCE DATA

Geoscience data has unique and complex data structures, and its visualization has been challenging due to a lack of effective data models and visual representations to tackle the heterogeneity of geoscience data. In today’s big data era, the needs of visualizing geoscience data become urgent, especially driven by its potential value to human societies, such as environmental disaster prediction, urban growth simulation, and so on. In this thesis, I created a novel geoscience data visualization framework and applied interface automata theory to geoscience data visualization tasks. The framework can support heterogeneous geoscience data and facilitate data operations. The interface automata can generate a series of interactions that can efficiently impress users, which also provides an intuitive method for visualizing and analysis geoscience data. Except clearly guided users to the specific visualization, interface automata can also enhance user experience by eliminating automation surprising, and the maintenance overhead is also reduced. The new framework was applied to INSIGHT, a scientific hydrology visualization and analysis system that was developed by the Nebraska Department of Natural Resources (NDNR). Compared to the existing INSIGHT solution, the new framework has brought many advantages that do not exist in the existing solution, which proved that the framework is efficient and extendable for visualizing geoscience data. Adviser: Hongfeng Y