多核处理器环境下内存数据库索引性能分析

从20世纪80年代内存数据库出现时的T树到21世纪初出现的缓存感知的CSS、CSB+树等,都适应了当时的硬件发展趋势,具有一定的性能优势.随着计算机硬件技术的进一步发展,尤其是多核技术的应用与推广,新的多核处理器在提高索引性能的同时又给内存索引结构提出了新的挑战.文中对B+树、T树、CSS树、CSB+树等几个经典的内存索引结构在多核处理器环境下的性能进行了全面的实验测试,对其在多核处理器环境下不同数据输入、不同节点大小等多种情况下的性能构成与差异进行了比较和分析,总结了在多核处理器中影响索引性能的关键因素,为内存索引结构的进一步改进奠定了坚实的基础.     

CQRtree空间数据库索引结构及实现算法

空间数据库的索引是提高空间数据库存储效率、空间检索性能的关键技术.本文在R树索引的基础上提出了一种新的空间数据库索引结构CQRtree,给出了CQRtree的数据结构、插入、删除、查询实现算法以及性能分析与比较,最后指出了进一步的研究方向.     

空间索引技术研究

空间索引可以提高空间数据库的操作效率,目前人们的研究工作更多地集中在空间数据的多维索引的研究上.文中全面地总结了当前空间数据库领域中空间索引以及时空索引的研究进展,描述了R树系列索引的构建思想,节点插入与分裂操作的不同.通过实验深入分析了R树以及R树变体的磁盘访问率,插入,删除,更新的CPU时间,验证了在数据激增的情况下,R树系列索引的复杂性带来的重叠问题会指数递增.由于R树当前应用的深度和广度,研究基于 R树的高效时空高维索引技术是解决索引应用问题一个有效方法.提出了索引性能改进的方向在于多种索引技术的结合,尤其是树形结构索引和网状结构索引的结合.     

HT树：缓存敏感的内存数据库索引

在内存数据库系统中,针对处理器缓存对提高内存数据库的性能有重要影响的情况,在B＋树的基础上提出一种新的缓存敏感的索引——HT索引。将Hash方法和树结构相结合,构造一种适用于内存数据库的索引。结果证明,该索引结构能提高处理器缓存的利用率,其整体操作性能优于传统的缓存敏感索引。     

R*-树空间索引的优化研究

针对大型空间数据库应用的需求及己有空间索引技术的不足，在论述R-树及R*-树索引技术的相关概念、数据结构、算法描述及性能分析的基础上，提出了一种改进的R*-树空间索引结构。研究结果表明：改进后的R*-树与原始的R*-树相比具有更高的性能。     

空间索引的混合树结构研究

针对大型的空间数据库和多媒体数据库的应用,R-树索引结构是一种有效的高维数据索引结构,但R树也有其缺点,文章提出了一种基于四叉树和R-树的混合树空间索引结构,研究结果表明:在存储开销上,混合树比R-树略高,但插入、删除、特别是查找的性能得到了显著的提高,且索引量越大,混合树的查找性能越明显优于R-树。     

DPslR+:一种基于动态空间槽的分布式并行空间索引树

空间索引是空间数据库的关键组成部分,其性能的优劣直接决定着空间数据操作的效率.为此,在分析了现有各种空间索引的基础上,将分布并行处理技术与空间索引相融合,提出了一种DPsIR+树.DPslR+树借助繁衍和返祖,动态分割空间槽,并将它们映射到多个节点机上.每个节点机再将其对应空间槽中的空间对象组织成R树,并将R树分裂成多个残枝,将残枝并行存入本地MultiDisk中;在内存中则按R-link组织空间对象,按R+处理节点溢出.实验结果表明DPslR+树具有良好的查询特性.     

基于空间数据不同索引方法的比较

空间索引是空间数据库的关键技术，其性能的高低决定着整个数据库的效率。本文分别对R树及其变形树、四叉树、网格文件作了介绍，并基于空间数据对这几种索引结构的性能作了比较，其结果为今后进一步研究提供了参考依据。     

基于Realms的主存R树索引的实现

为了充分发挥主存数据库技术的优越性，提高系统性能，需要使用空间索引，并将索引也放在主存中。R树类是目前空间数据索引的研究热点，具有动态性及构造和维护的简单性，在基本R树索引的基础上便于作各种算法改进，文中开发的基于Realms的空间分析数据库管理系统SADBS中实现了主存R树索引的创建及插入、删除、更新、查询等操作。     

基于聚类的空间数据多比例尺索引树

针对现有空间对象多尺度索引结构聚簇性不高的问题,在R树索引的基础上提出一种基于聚类的空间数据多比例尺索引结构。利用树的层次结构反映空间数据的多比例尺特性,用k-means算法对相同等级的空间对象进行聚类分组,减少空间区域覆盖和重叠。实验结果表明,该方法与基于四叉树的多比例尺索引相比,能有效提高空间数据多比例尺显示的性能。     

The design and implementation of seeded trees: an efficient methodfor spatial joins

Existing methods for spatial joins require pre-existing spatial indices or other precomputation, but such approaches are inefficient and limited in generality. Operand data sets of spatial joins may not all have precomputed indices, particularly when they are dynamically generated by other selection or join operations. Also, existing spatial indices are mostly designed for spatial selections, and are not always efficient for joins. This paper explores the design and implementation of seeded trees, which are effective for spatial joins and efficient to construct at join time. Seeded trees are R-tree-like structures, but divided into seed levels and grown levels. This structure facilitates using information regarding the join to accelerate the join process, and allows efficient buffer management. In addition to the basic structure and behavior of seeded trees we present techniques for efficient seeded tree construction, a new buffer management strategy to lower I/O costs, and theoretical analysis for choosing algorithmic parameters. We also present methods for reducing space requirements and improving the stability of seeded tree performance with no additional I/O costs. Our performance studies show that the seeded tree method outperforms other tree-based methods by far both in terms of the number disk pages accessed and weighted I/O costs. Further, its performance gain is stable across different input data, and its incurred CPU penalties are also lower     

面向非易失内存的异构索引

非易失内存(non-volatile memory,NVM)为数据存储与管理带来新的机遇,但同时也要求已有的索引结构针对NVM的特性进行重新设计.围绕NVM的存取特性,重点研究了树形索引在NVM上的访问、持久化、范围查询等操作的性能优化,并提出了一种上下两层结构的异构索引HART.该索引结合了B+树与Radix树的特点...     

Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops

An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R₇₅₀/R₇₁₀ and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies.     

A tangible user interface for assessing cognitive mapping ability

Wayfinding, the ability to recall the environment and navigate through it, is an essential cognitive skill relied upon almost every day in a person's life. A crucial component of wayfinding is the construction of cognitive maps, mental representations of the environments through which a person travels. Age, disease or injury can severely affect cognitive mapping, making assessment of this basic survival skill particularly important to clinicians and therapists. Cognitive mapping has also been the focus of decades of basic research by cognitive psychologists. Both communities have evolved a number of techniques for assessing cognitive mapping ability. We present the Cognitive Map Probe (CMP), a new computerized tool for assessment of cognitive mapping ability that increases consistency and promises improvements in flexibility, accessibility, sensitivity and control. The CMP uses a tangible user interface that affords spatial manipulation. We describe the design of the CMP, and find that it is sensitive to factors known to affect cognitive mapping performance in extensive experimental testing.     

Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events

South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country’s drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. The VegDRI-SKorea was developed for South Korea, modifying the original VegDRI methodology (developed for the USA) by tailoring it to the available local data resources. The VegDRI-SKorea utilizes a classification and regression tree (CART) modelling approach that collectively analyses remote-sensing data (e.g. normalized difference vegetation index (NDVI)), climate-based drought indices (e.g. self-calibrated Palmer drought severity index (PDSI) and standardized precipitation index (SPI)), and biophysical variables (e.g. elevation and land cover) that influence the drought-related vegetation stress. This study evaluates the performance of the recently developed VegDRI-SKorea for severe and extreme drought events that occurred in South Korea in 2001, 2008, and 2012. The results demonstrated that the hybrid drought index improved the more spatially detailed drought patterns compared to the station-based drought indices and resulted in a better understanding of drought impacts on the vegetation conditions. The VegDRI-SKorea model is expected to contribute to the monitoring of drought conditions nationally. In addition, it will provide the necessary information on the spatial variations of those conditions to evaluate local and regional drought risk assessment across South Korea and assist local decision-makers in drought risk management.     

基于广义后缀树结合过滤因子的正则表达式匹配算法

为了提高正则表达式在文本集合上的匹配效率,提出一种基于广义后缀树与过滤因子相结合的正则表达式匹配技术。根据给定的文本集合构建广义后缀树,通过在广义后缀树上定位过滤因子得到有效的候选匹配集合,利用过滤因子的序列信息进一步过滤候选集合,进而对候选集合中的字符串进行验证,得到匹配结果。通过在真实的数据集上进行实验,证明了该算法能够有效地提高正则表达式的匹配性能。     

Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote-sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images and different classification algorithms, maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA) and object-based classification (OBC), were explored. The results indicate that a combination of vegetation indices as extra bands into Landsat TM multi-spectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multi-spectral bands improved the overall classification accuracy (OCA) by 5.6% and the overall kappa coefficient (OKC) by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes that have complex stand structures and large patch sizes.     

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.     

Comprehensive drought index as an indicator for use in drought monitoring integrating multi-source remote sensing data: a case study covering the Sichuan-Chongqing region

Numerous meteorological drought-monitoring indices and remote-sensing-based spatial drought monitoring indices have been developed and applied to monitor drought in different ways. However, individual indices have obvious deficiencies in terms of their responses to drought, and they do not comprehensively reflect the available information on drought. To overcome issues with the data themselves and improve drought monitoring techniques, we use a comprehensive drought index (CDI) derived from the vegetation condition index, the temperature condition index, and the precipitation condition index to monitor meteorological or agricultural drought for the Sichuan-Chongqing region. To assess CDI performance, monthly CDI values for Sichuan-Chongqing region were used to analyse the spatial and temporal variations of the 2006 drought. The results indicated that all aspects of the drought were monitored, and the results were in agreement with related research. Meanwhile, an extreme drought was accurately explored using the CDI in the Sichuan-Chongqing region from 2000 to 2011. Finally, a validation was performed, and the results show that the CDI is closely related with the standardized precipitation index calculated using a 3-month time scale (SPI3), as well as variations in crop yield and drought-affected crop area. These results provide further evidence that the CDI is an indicator that can be used in integrated drought monitoring and that it can simultaneously reflect meteorological and agricultural drought information.