The Concept and Technologies of Quality of Geographic Information Service: Improving User Experience of GIServices in a Distributed Computing Environment

Abstract

With the wide use of web technologies, service-oriented architecture (SOA), and cloud computing, more and more geographical information systems are served as GIServices. Under such circumstance, quality of geographic information services (QoGIS) has emerged as an important research topic of geoinformatics. However, it is not easy to understand the field since QoGIS has no formal standards, which is not only in regard to server-side performance and capabilities, but is also related with the quality of experience (QoE) during user interaction with GIServices. In this paper, we compare quality of service (QoS) and QoGIS research to understand the uniqueness of QoGIS. A conceptual framework is proposed to organize and interpret QoGIS research from the perspective of quality modeling, acquisition, and application, and we discuss the status, limitations, and future directions of this area. Overall, our analysis shows that new quality metrics will evolve from existing metrics to match the needs in concrete QoGIS applications, and user preferences need to be considered in quality modeling for GIServices. We discuss three approaches for the provision of QoGIS information and find that user feedback mining is an important supplementary source of quality information. Gaps between QoS and QoGIS research suggest that the GIService performance enhancement must not only consider the unique features of spatial data models and algorithms, but also system architecture, deployment, and user spatiotemporal access behaviors. Advanced service selection algorithms must be introduced to tackle the quality optimization problems of geoprocessing workflow planning. Moreover, a QoGIS-aware GIServices framework must be established to facilitate and ensure GISerivce discovery and interaction. We believe this bibliographic review provides a helpful guide for GIS researchers.

Optimizing and accelerating space–time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis

“…The expected time cost of Ripley's K function would be even higher when extended from spatial dimension to spatiotemporal dimension. There have been desktop-based software packages that provide Ripley's K function and its extensions (e.g., Spatstat [17], Splancs [18], Stpp [19] in R), but the time efficiency is far from satisfying for large data volume, which affects the user experience of geoprocessing significantly [20] and impedes its further application. Hence, the optimization and acceleration of space-time Ripley's K function is urgent to enable efficient spatiotemporal point pattern analysis for big point datasets.…”

Beyond First Impressions: Estimating Quality of Experience for Interactive Web Applications

“…familiarity with the UI and functions) and user's objectives (e.g. pinpointing a place or navigating from one location to another) are understood to impact the user's experience [43]. Table 3 summaries the pre-test questionnaire undertaken to capture the cohort's previous experience with web applications as well as demographic information.…”

Geospatial Information Processing Technologies

“…As global providers deliver more GIServices with similar functions but diverse quality, it has become challenging to select appropriate service instances from similar service candidates. To enable quality-aware service chain instantiation, quality evaluation methods and mathematical planning methods must be developed (Hu et al 2019b). Quality evaluation assesses the fitness of individual participating services or aggregated services according to user quality requirements, and mathematical planning assists the service instance selection for each individual participating service by considering the overall quality of the service chain.…”