基于Web的流动地震监测数据库查询系统的设计与实现

介绍了基于Web的流动地震监测数据库查询系统的开发背景和目的,描述了数据库查询系统的设计和实现过程。该系统借助Internet/Intranet网络,以B/S(浏览器/服务器)体系结构作为基础架构,采用WAMP集成化软件开发,具有系统加密、数据查询、数据维护、数据导出和资料下载等功能。     

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACT

Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.     

多维度动态农村土地承包经营权业务权限的设计与实现

为满足浙江省各级农经管理部门在农村土地承包经营权管理系统中复杂的权限管理需要,基于RBAC模型实现了以Apache Shiro安全框架为支撑的多维度动态业务权限管理。该模型从角色维度、部门维度、空间维度、时间维度上分别对用户的资源菜单、工作流、数据操作空间、数据历史回溯4个方面的权限进行细粒度控制。在浙江省农村土地承包管理信息系统中实际运行表明,设计的业务权限系统可满足省市县各级农经管理部门在管理系统的权限要求。     

Parallel and scalable block system generation

Generating a realistic representation of a fractured rock mass is a first step in many different analyses. Field observations need to be translated into a 3-D model that will serve as the input for these analyses. The block systems can contain hundreds of thousands to millions of blocks of varying sizes and shapes; generating these large models is very computationally expensive and requires significant computing resources.By taking advantage of the advances made in big data analytics and Cloud Computing, we have a developed an open-source program—SparkRocks—that generates block systems in parallel. The application runs on Apache Spark which enables it to run locally, on a compute cluster or the Cloud. The block generation is based on a subdivision and linear programming optimization as introduced by Boon et al. (2015). SparkRocks automatically maintains load balance among parallel processes and can be scaled up on the Cloud without having to make any changes to the underlying implementation, enabling it to generate real-world scale block systems containing millions of blocks in minutes.     

在Vista操作系统下利用VertrigoServ搭建局域网Web服务器

利用VertrigoServ中集成的Apache服务器功能,在最新的Vista操作系统上,快速搭建起能够对外提供服务的Web服务器。使用这样的Web服务器,单位或者个人可以不通过专业的网站制作人员,而自主、方便、快捷的对外展示自己的页面、将一些资料以网页的形式对外共享。     

Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification

Advances in the development of Earth observation data acquisition systems have led to the continuously growing production of remote sensing datasets, for which timely analysis has become a major challenge. In this context, distributed computing technology can provide support for efficiently handling large amounts of data. Moreover, the use of distributed computing techniques, once restricted by the availability of physical computer clusters, is currently widespread due to the increasing offer of cloud computing infrastructure services. In this work, we introduce a cloud computing approach for object-based image analysis and classification of arbitrarily large remote sensing datasets. The approach is an original combination of different distributed methods which enables exploiting machine learning methods in the creation of classification models, through the use of a web-based notebook system. A prototype of the proposed approach was implemented with the methods available in the InterCloud system integrated with the Apache Zeppelin notebook system, for collaborative data analysis and visualization. In this implementation, the Apache Zeppelin system provided the means for using the scikit-learn Python machine learning library in the design of a classification model. In this work we also evaluated the approach with an object-based image land-cover classification of a GeoEye-1 scene, using resources from a commercial cloud computing infrastructure service provided. The obtained results showed the effectiveness of the approach in efficiently handling a large data volume in a scalable way, in terms of the number of allocated computing resources.     

基于Hilbert空间分区和Geohash索引的并行Ripley's K函数

作为二阶点模式分析方法,Ripley’s K函数(简称K函数)以距离为自变量探测不同尺度下点事件的分布模式及演变规律,在生态学、经济学、地理学等诸多领域得到广泛应用。然而,随着点规模的增加,估计与模拟阶段点对距离遍历计算时间开销激增,严重制约了K函数的应用,算法流程优化与并行加速成为应对海量点数据下K函数性能瓶颈及可计算性问题的关键技术手段。针对默认数据分区未考虑点事件空间邻近性导致跨节点通讯成本高昂且K函数距离阈值较大时索引优化失效的现象,本文提出一种基于空间填充曲线的K函数优化加速方法。该方法采用Hilbert曲线构建空间分区,在顾及数据空间邻近性的前提下减少分区间数据倾斜和通讯开销;在分区基础上,利用Geohash编码改进各分区内本地空间索引策略加速点对距离计算。本文以湖北省工商企业注册数据为例,通过对比实验分析了默认分区无索引、KDB分区组合R树索引、本文Hilbert分区组合Geohash索引算法在不同数据规模、距离阈值、集群规模下的计算耗时。结果表明,300 000点数据规模下本文方法的时间开销约为默认分区无索引方法的1/4,9台节点下加速比超过3.6倍。因此,该方法能有效...     

空间大数据分析引擎的设计与实现

针对空间大数据分析需求日益增加但缺乏关键技术支撑的问题,融合GIS与大数据技术构建空间大数据分析引擎成为有效的解决方法.本文基于SuperMap iObjects for Java和Apache Spark计算框架设计并实现了空间大数据分析引擎,并将其用于计算航路拥挤情况的要素连接分析实验.研究结果表明,空间大数据分析引擎处理性能高,具备保障空间大数据分析的能力.     

CHIRICAHUA APACHE HOMELAND IN THE BORDERLAND SOUTHWEST*

The creation of the present United States‐Mexico boundary in the mid‐nineteenth century interrupted and disregarded the traditional territorial space of the Chiricahua Apache, whose ancestral homeland transcended this new line. As a result of their land claims, the United States created a reservation for the Chiricahua Apache, but it was later withdrawn. Today members of this group officially reside among Mescalero Apache in New Mexico and Fort Sill Apache in Oklahoma. This essay assesses the historic and contemporary impact of geographical borderland changes for the Chiricahua Apache and discusses the legacy of a transformed homeland.