基于云计算服务模式的数据挖掘应用平台的构建

本文将云计算的服务模式应用到数据挖掘应用平台的设计中,提出了3层4模式的云计算服务层次体系,并将此体系应用到数据挖掘平台的构建中,构建了基于云计算服务模式的数据挖掘应用平台框架,详述了平台各子系统的功能,阐述了该平台的特色.此平台集成了多种数据挖掘方法,具有良好的通用性和可扩展性,可以为从事数据挖掘应用和研究的企业或学者提供一个良好的交流平台.     

基于云计算的数据挖掘系统架构研究

分析当前数据挖掘遇到的问题以及云计算的优势,构建了基于云计算的数据挖掘系统架构,并对其中的模块进行了描述。该框架有利于解决当前数据挖掘遇到的数据复杂程度高,数据量大等问题,能够显著提高数据挖掘的性能,高可信的基于云计算的数据挖掘系统和服务是未来的一个参考方向。     

基于云计算物联网数据挖掘模式的构建方式研究

物联网作为一种具备高水准动态性和异构性的现实网络,其数据具备较强的空间性和时间性,想要对其进行数据挖掘是相对困难的.因此,基于云计算的数据挖掘模式被应用到了物联网数据挖掘中.利用云计算技术构建出面向物联网的分布式数据挖掘模型,进而有效完成数据挖掘任务.     

基于云计算的物联网数据挖掘模式的构建

科技的不断发展和互联网技术的不断进步使得物联网给人类的联通需求提供了更多的帮助,同时也对该项技术对数据挖掘的模式提出了更高要求。而传统的数据挖掘显然已经无法满足物联网的发展要求。基于此,利用云计算技术衍生出来的物联网数据挖掘受到了更广泛的关注。     

基于云计算环境下的数据挖掘服务模式分析

随着网络的不断发展,网络中充斥着各种庞大的数据信息是常见的问题。如何在庞大的数据信息中挖掘有价值的信息是很多领域需要解决的问题。文章主要研究的是云计算背景下的数据挖掘服务模式,对其服务模式的结构类型、建模流程和服务体系都进行分析,探讨出完整的云计算背景下的数据挖掘服务模式。     

基于云计算环境的数据挖掘算法研究

本文将介绍K-means算法的一般执行流程、云环境下K-means算法的实现、以及云环境下K-means算法的改进方法,还将从云计算环境下的数据挖掘算法、数据存储和管理技术、数据预处理技术、分类、聚类、关联规则挖掘和时序数据挖掘算法等方面进行探讨。     

浅谈云计算下物联网的数据挖掘模式分析

随着科学技术的不断发展,信息通信业获得了较好发展,以云计算为基础的物联网也开始受到很多人的关注.物联网中形成的大量信息和时间以及空间有着一定关联,其具有动态、异构以及分布的特点,这对物联网数据挖掘而言是相当艰难的.本文对建立在云计算之上的物联网数据挖掘模式进行研究,以期可以给其日后发展带来一些有用的参考信息.     

云计算平台下物联网的数据挖掘

随着我国信息技术产业日渐成熟,物联网这一新一代信息技术关键技术日渐受到学界重视,基于此,本文就物联网与云计算、物联网数据挖掘需要解决的关键性问题展开分析,并对基于云计算的物联网数据挖掘、实验验证进行了详细论述,希望由此能够为相关业内人士带来一定启发。     

基于云计算的数据挖掘技术分析

由于计算机的迅速发展,基于云计算的数据挖掘技术已经是一种十分高效可信的技术,它可以解决传统数据挖掘方式不适合海量数据的问题。本文介绍了数据挖掘、云计算的相关概念,分析了基于云计算的数据挖掘技术及其优点、问题。     

数据挖掘应用到云计算环境的有效性分析

云计算在当前的计算机技术发展中是热度很高的话题,对于它的具体定义描述,到目前还没有一个较为权威统一的标准。从它的结构构成来讲,它是一种新兴的共享基础架构和方法,它的开展基础是公开的标准和服务,围绕互联网为中心来提供相关服务,它在当前社会的大多数领域都有了广泛的应用和体现,涉及到诸多的领域,需要在具体应用时进行综合全面的把握,基于此,本文就在分析云计算开放环境和系统的关键研发基础上,对云计算模式下的数据挖掘平台原型建立进行了分析,阐述了平台在实际应用中的扩展性和通用性,可以给相关领域的研究学者提供参考作用。     

Geographic applications of remotely sensed data

The application of remote sensing to the study of geography as spatially distributed phenomena began in the late 1800s when German foresters used photography taken from a balloon to produce forest maps. Since this early beginning researchers and professionals in government, industry, and educational institutions have become increasingly aware of the ability of remote sensing to provide geographic information. This is information on the spatial distribution and temporal dynamics of objects and processes in the world around us. Geographic applications of remotely sensed data typically take one of four explanatory forms and are aimed at one or more of three practical objectives. Explanatory forms include: 1) morphometric analysis, 2) cause and effect analysis, 3) temporal analysis, 4) functional and ecological systems analysis. Objectives include: 1) mapping, 2) monitoring, 3) modeling. As we have moved from using manual analysis techniques on black-and-white aerial photography for mapping distributions to machine-assisted analysis of multispectral satellite data in digital form for analyzing complex environmental processes, the techniques and procedures involved in the use of remotely sensed data have become more complex. Remote sensing does offer a tremendous potential to the community involved in geographic applications. To realize this potential will continue to require a balanced program of both basic and applied research.     

Minimum-volume transforms for remotely sensed data

Scatter diagrams for multispectral remote sensing data tend to be triangular, in the two-band case, pyramidal for three bands, and so on. They radiate away from the so-called darkpoint, which represents the scanner's response to an un-illuminated target. A minimum-volume transform may be described (provisionally) as a nonorthogonal linear transformation of the multivariate data to new axes passing through the dark point, with directions chosen such that they (for two bands), or the new coordinate planes (for three bands, etc.) embrace the data cloud as tightly as possible. The reason for the observed shapes of scatter diagrams is to be found in the theory of linear mixing at the subfootprint scale. Thus, suitably defined, minimum-volume transforms can often be used to unmix images into new spatial variables showing the proportions of the different cover types present, a type of enhancement that is not only intense, but physically meaningful. The present paper furnishes details for constructing computer programs to effect this operation. It will serve as a convenient technical source that may be referenced in subsequent, more profusely illustrated publications that address the intended application, the mapping of surface mineralogy     

面向云计算环境下Web数据挖掘技术

在云计算环境下,Web数据挖掘技术得到了快速发展.由于云计算的应用,Web数据挖掘体系已体现出新的特点.分析云计算环境下Web数据挖掘技术的特点,可以明确应用要点,可以实现云计算在数据存储中的突破,实现存储的能力与安全性的提高.从海量数据中高效挖掘有价值的资源,属于信息技术要解决的关键问题.云计算技术支持下的数据挖掘实...     

Preserving radiometric resolution in remotely sensed data withlossy compression

Recent advances in imaging technology make it possible to obtain remotely sensed imagery data of the Earth at high spatial, spectral, and radiometric resolutions. The rate at which the data is collected from these satellites can far exceed the channel capacity of the data downlink. Reducing the data rate to within the channel capacity can often require painful trade-offs in which certain scientific returns are sacrificed for the sake of others. The authors focus on the case where radiometric resolution is sacrificed by dropping a specified number of lower order bits (LOB) from each data pixel. To limit the number LOBs dropped, they also compress the remaining bits using lossless compression. They call this approach “truncation followed by lossless compression” or TLLC. They then demonstrate the suboptimality of this TLLC approach by comparing it with the direct application of a more effective lossy compression technique based on the JPEG algorithm. This comparison demonstrates that, for a given channel rate, the method based on JPEG lossy compression better preserves radiometric resolution than does TLLC     

云计算环境下的海量数据特定特征挖掘技术

针对传统云端计算过程中的数据特定信息提取不精确的问题,提出一种云计算环境下的海量数据特定特征挖掘方法。采用矩阵节点差分模型进行数据的有序排列,避免传统方法中的数据混乱造成提取数据不精确,庞大的云端数据量致使数据的定位不精准,为了避免此类问题的产生,使用多维数据定位计算,能够有效地解决定位不准的问题,最终可以成功的对数据信息进行有效提取。为了验证设计云计算环境下的海量数据特定特征挖掘方法的有效性,设计了对比仿真实验,实验结果充分证明了该方法能够有效地解决数据提取不精确的问题。     

Measuring trace gases in plumes from hyperspectral remotely sensed data

A method [joint reflectance and gas estimator (JRGE)] is developed to estimate a set of atmospheric gas concentrations in an unknown surface reflectance context from hyperspectral images. It is applicable for clear atmospheres without any aerosol in a spectral range between approximately 800 and 2500 nm. Standard gas by gas methods yield a 6% rms error in H/sub 2/O retrieval from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, reaching several tens percent for a set of widespread ground materials and resulting from an simplifying assumption of linear variations of the reflectance model within gas absorption bands and partial accounting of the gas induced signal. JRGE offers a theoretical framework consisting in a two steps algorithm that accounts for sensor characteristics, assumptions on gas concentrations and reflectance variations. It estimates variations in gas concentrations relatively to a standard atmosphere model. An adaptive cubic smoothing spline like estimation of the reflectance is first performed. Concentrations of several gaseous species are then simultaneously retrieved using a nonlinear procedure based on radiative transfer calculations. Applied to AVIRIS spectra simulated from reflectance databases and sensor characteristics, JRGE reduces the errors in H/sub 2/O retrieval to 2.87%. For an AVIRIS image acquired over the Quinault prescribed fire, far field CO/sub 2/ estimate (348 ppm, about 6% to 7% rms) is in agreement with in situ measurement (345-350 ppm) and aerosols yield an underestimation of total atmospheric CO/sub 2/ content equal to 5.35% about 2 km downwind the fire. JRGE smoothes and interpolates the reflectance for gas estimation but also provides nonsmoothed reflectance spectra. JRGE is shown to preserve various mineral absorption features included in the AVIRIS image of Cuprite Mining District test site.     

云计算环境下多服务器多分区数据的高效挖掘方法设计

云计算环境下的多服务器多分区系统中存在海量数据,传统串行数据挖掘方法对这些数据进行挖掘的过程中,无法对海量数据进行并行处理,挖掘效率低。针对该问题,设计云计算环境下多服务器多分区数据挖掘系统,其包括基础设施即服务层、平台即服务层、软件即服务层,可实现大规模数据的高效挖掘。系统通过平台即服务层中的多服务器多分区数据处理模型,实现海量数据的分布式运算,并基于MapReduce机制实现K均值聚类数据挖掘算法的并行化,通过Map和Reduce函数实现多服务器多分区数据的并行挖掘。实验结果表明,所设计系统大幅度降低了云计算环境下多服务器多分区数据的挖掘时间,提高了数据的挖掘效率和稳定性。     

A method for tracking individual planetary waves in remotely sensed data

We describe a methodology for tracking individual planetary waves in longitude-time plots of satellite data, based on fitting an elementary wave shape model to subsets of the data by maximum likelihood, then reconstructing the trajectory and evolution of every single wave (where for "single wave" we mean an individual positive or negative westward propagating anomaly) by joining the elementary waves according to their similarity. We then illustrate the potential of the methodology with an example at 34/spl deg/N in the Atlantic Ocean and its adaptability to different cases with a second example on eastward-propagating Kelvin waves in the equatorial Pacific. Although the examples given use sea surface height anomaly data, the technique lends itself to be applied to any space-time plot of any dataset displaying propagation and, in particular, to sea surface temperature data.     

云服务器中的不稳定数据挖掘系统的研究与设计

针对当前的云计算服务器缺少对不稳定数据的识别与检测,设计并实现一种云服务器中不稳定数据挖掘系统。介绍系统的总体结构,利用数据采样预处理模块实现从源数据到挖掘数据的映射,完成离散化、数据过滤等处理过程。依据2.0 mm ERmet Hard Metric连接器,采用Rapid IO协议,通过接口模块完成数据间的传输,以达到信号传输效率与稳定性的要求。通过数据挖掘模块对云服务器中不稳定数据的确认与挖掘,将挖掘结果传输至控制模块进行处理。软件设计过程中,对云服务器中不稳定数据挖掘系统进行了详细地分析,并给出不稳定数据挖掘的实现过程以及系统部分程序代码。实验结果表明,所设计的系统具有很高的实用性和可靠性。     

Analysis of remotely sensed data: the formative decades and the future

Developments in the field of image understanding in remote sensing over the past four decades are reviewed, with an emphasis, initially, on the contributions of David Landgrebe and his colleagues at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, culminating with a commentary on methods commonly adopted for multisource image analysis. The treatment concludes by examining the requirements of an operational multisource thematic mapping process, in which it is suggested that the most practical approach is to analyze each data type separately, by techniques optimized to that data's characteristics, and then to fuse at the label level.