{"title":"集成基于web的时空传感器数据与MapReduce功能的编程框架","authors":"James L. Horey","doi":"10.1145/1878500.1878511","DOIUrl":null,"url":null,"abstract":"Web-based sensor data, provided by organizations such as the National Oceanographic and Atmospheric Administration, provide a valuable service to the public and scientific communities. However, much of this data is locked in a variety of presentation formats and is computationally inaccessible. In addition, although these data have a spatiotemporal context, both the spatial and temporal data are usually only implicitly defined. Although storing this data in a consistent database can partially resolve this problem, a data-driven programming model coupled with MapReduce capabilities is a more flexible and extensible solution. Our implementation of this programming model allows users to parse a wide array of sensor data and express complex computation in a simple, scalable manner. In addition, our framework uses a simple key-value storage mechanism and provides convenient geospatial output mechanisms. In this paper, we discuss some early results of our programming model within the context of our current Java-oriented implementation, and demonstrate how the system can be used to create many different applications. We also discuss and evaluate our system with respect to memory usage and scalability.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A programming framework for integrating web-based spatiotemporal sensor data with MapReduce capabilities\",\"authors\":\"James L. Horey\",\"doi\":\"10.1145/1878500.1878511\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Web-based sensor data, provided by organizations such as the National Oceanographic and Atmospheric Administration, provide a valuable service to the public and scientific communities. However, much of this data is locked in a variety of presentation formats and is computationally inaccessible. In addition, although these data have a spatiotemporal context, both the spatial and temporal data are usually only implicitly defined. Although storing this data in a consistent database can partially resolve this problem, a data-driven programming model coupled with MapReduce capabilities is a more flexible and extensible solution. Our implementation of this programming model allows users to parse a wide array of sensor data and express complex computation in a simple, scalable manner. In addition, our framework uses a simple key-value storage mechanism and provides convenient geospatial output mechanisms. In this paper, we discuss some early results of our programming model within the context of our current Java-oriented implementation, and demonstrate how the system can be used to create many different applications. We also discuss and evaluate our system with respect to memory usage and scalability.\",\"PeriodicalId\":190366,\"journal\":{\"name\":\"International Workshop on GeoStreaming\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on GeoStreaming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1878500.1878511\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on GeoStreaming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1878500.1878511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A programming framework for integrating web-based spatiotemporal sensor data with MapReduce capabilities
Web-based sensor data, provided by organizations such as the National Oceanographic and Atmospheric Administration, provide a valuable service to the public and scientific communities. However, much of this data is locked in a variety of presentation formats and is computationally inaccessible. In addition, although these data have a spatiotemporal context, both the spatial and temporal data are usually only implicitly defined. Although storing this data in a consistent database can partially resolve this problem, a data-driven programming model coupled with MapReduce capabilities is a more flexible and extensible solution. Our implementation of this programming model allows users to parse a wide array of sensor data and express complex computation in a simple, scalable manner. In addition, our framework uses a simple key-value storage mechanism and provides convenient geospatial output mechanisms. In this paper, we discuss some early results of our programming model within the context of our current Java-oriented implementation, and demonstrate how the system can be used to create many different applications. We also discuss and evaluate our system with respect to memory usage and scalability.