提出一种RESTful Web API服务架构的技术方案,用以解决海量遥感影像的检索问题。首先研究了RESTful架构相对于传统Web架构的优势,它的使用可充分实现前后端分离的思想;然后选取Spring相关框架作为RESTful Web API服务架构的实现方式,并...提出一种RESTful Web API服务架构的技术方案,用以解决海量遥感影像的检索问题。首先研究了RESTful架构相对于传统Web架构的优势,它的使用可充分实现前后端分离的思想;然后选取Spring相关框架作为RESTful Web API服务架构的实现方式,并使用经典的MVC分层结构来设计;最后引用数据库拓展Postgis处理空间数据的能力,完善服务的空间检索功能,提出空间查询的交互方式和服务的应用场景。结果表明,该架构应用在遥感影像检索系统中,可以使其更加简洁和高效。展开更多
Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pa...Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing（such as Hadoop） and stream processing（such as Spark） of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.展开更多
文摘提出一种RESTful Web API服务架构的技术方案,用以解决海量遥感影像的检索问题。首先研究了RESTful架构相对于传统Web架构的优势,它的使用可充分实现前后端分离的思想;然后选取Spring相关框架作为RESTful Web API服务架构的实现方式,并使用经典的MVC分层结构来设计;最后引用数据库拓展Postgis处理空间数据的能力,完善服务的空间检索功能,提出空间查询的交互方式和服务的应用场景。结果表明,该架构应用在遥感影像检索系统中,可以使其更加简洁和高效。
基金supported by the Discovery grant No.RGPIN 2014-05254 from Natural Science&Engineering Research Council(NSERC),Canada
文摘Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing（such as Hadoop） and stream processing（such as Spark） of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.
