Integration of hydrological observations into a Spatial Data Infrastructure under a Sensor Web environment

Various sensors connected to the World Wide Web are used to obtain real-time hydrological observations.Thus,real-time management and utilization of such distributed in situ observations in the cyber-physical environment becomes possible.A Sensor Observation Service(SOS)chaining Web Feature Service(WFS)method is proposed to integrate geographical reference observation data collected by a hydrological Sensor Web into a virtual globe.This method hides the complexity of a series of information and service models in the Sensor Web realm to enable the integration of heterogeneous distributed hydrological data sources into a Spatial Data Infrastructure(SDI).The core components-a dynamic schema transformer and automatic information extractor-were designed and implemented.The SOS schema is matched to WFS schema that uses the schema transformer dynamically.The information extractor extracts and serves features automatically,conforming to standard SOS operations for observation retrieval and insertion.Feasibility experiments conducted on the Jinsha River tested this proposed method.Results show that the proposed approach allows the integration of SOS servers into legacy applications that have a higher degree of availability within many SDIs.However,this is accompanied with the drawback that only a limited part of the SOS functionality is available to clients.

An integrated heterogeneous web service retrieval via combination of instance-and metadata-based schema matching method

Schema matching is a critical step in the integration of heterogeneous web service,which contains various types of web services and multi-version services of the same type.Mapping loss or mismatch usually occurs due to schema differences in structure and content and the variety in concept definition and organization.Current instance schema matching methods are not mature enough for heterogeneous web service because they cannot deal with the instance data in web service domain and capture all the semantics,especially metadata semantics.The metadata-based and the instance-based matching methods,in the case of being employed individually,are not efficient to determine the concept relationships,which are crucial for finding high-quality matches between schema attributes.In this paper,we propose an improved schema matching method,based on the combination of instance and metadata(CIM)matcher.The main method of our approach is to utilize schema structure,element labels,and the corresponding instance data information.The matching process is divided into two phases.In the first phase,the metadata-based matchers are used to compute the element label similarity of multi-version open geospatial consortium web service schema,and the generated matching results are raw mappings,which will be reused in the next instance matching phase.In the second phase,the designed instance matching algorithms are employed to the instance data of the raw mappings and fine mappings are generated.Finally,the raw mappings and the fine mappings are combined,and the final mappings are obtained.Our experiments are executed on different versions of web coverage service and web feature service instance data deployed in Geoserver.The results indicate that,the CIM method can obtain more accurate matching results and is flexible enough to handle the web service instance data.

A comprehensive optimization strategy for real-time spatial feature sharing and visual analytics in cyberinfrastructure

For geospatial cyberinfrastructure-enabled web services,the ability of rapidly transmitting and sharing spatial data over the Internet plays a critical role to meet the demands of real-time change detection,response and decision-making.Especially for vector datasets which serve as irreplaceable and concrete material in data-driven geospatial applications,their rich geometry and property information facilitates the development of interactive,efficient and intelligent data analysis and visualization applications.However,the big-data issues of vector datasets have hindered their wide adoption in web services.In this research,we propose a comprehensive optimization strategy to enhance the performance of vector data transmitting and processing.This strategy combines:(1)pre-and on-the-fly generalization,which automatically determines proper simplification level through the introduction of appropriate distance tolerance speed up simplification efficiency;(2)a progressive attribute transmission method to reduce data size and,therefore,the service response time;(3)compressed data transmission and dynamic adoption of a compression method to maximize the service efficiency under different computing and network environments.A cyberinfrastructure web portal was developed for implementing the proposed technologies.After applying our optimization strategies,substantial performance enhancement is achieved.We expect this work to facilitate real-time spatial feature sharing,visual analytics and decision-making.