Available online xxxxKeyword: Geospatial cyberinfrastructure (GCI) Rapid response Disaster management GeoJSON GML WMS WFS Service-oriented architecture (SOA) a b s t r a c t High population growth, urbanization, and global climate change drive up the frequency of disasters, affecting the safety of people's lives and property worldwide. Because of the inherent big-data nature of this disaster-related information, the processes of data exchange and transfer among physically distributed locations are increasingly challenging. This paper presents our proposed efficient network transmission model for interoperating heterogeneous geospatial data in a cyberinfrastructure environment. This transmission model supports multiple data encoding methods, such as GML (Geography Markup Language) and GeoJSON, as well as data compression/decompression techniques, including LZMA and DEFLATE. Our goal is to tackle fundamental performance issues that impact efficient retrieval of remote data. Systematic experiments were conducted to demonstrate the superiority of the proposed transmission model over the traditional OGC Web Feature Service (WFS) transmission model. The experiments also identified the optimized configuration for data encoding and compression techniques in different network environments. To represent a real-world user request scenario, the Amazon EC2 cloud platform was utilized to deploy multiple client nodes for the experiments. A web portal was developed to integrate the real-time geospatial web services reporting with real-time earthquake related information for spatial policy analysis and collaborative decision-making.
The proliferation of geospatial data from diverse sources, such as Earth observation satellites, social media, and unmanned aerial vehicles (UAVs), has created a pressing demand for cross-platform data integration, interoperation, and intelligent data analysis. To address this big data challenge, this paper reports our research in developing a rule-based, semantic-enabled service chain model to support intelligent question answering for leveraging the abundant data and processing resources available online. Four key techniques were developed to achieve this goal: (1) A spatial and temporal reasoner resolves the spatial and temporal information in a given scientific question and enables place-name disambiguation based on support from a gazetteer; (2) a spatial operation ontology categorizes important spatial analysis operations, data types, and data themes, which will be used in automated chain generation; (3) a language-independent chaining rule defines the template for input, spatial operation, and output as well as rules for embedding multiple spatial operations for solving a complex problem; and (4) a recursive algorithm facilitates the generation of executive workflow metadata according to the chaining rules. We implement this service chain model in a cyberinfrastructure for online and reproducible spatial analysis and question answering. Moving the problem-solving environment from a desktop-based environment onto a geospatial cyberinfrastructure (GeoCI) offers better support to collaborative spatial decision-making and ensures science replicability. We expect this work to contribute significantly to the advancement of a reproducible spatial data science and to building the next-generation open knowledge network.
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