A scalable data structure for real-time estimation of resource availability in build-to-order environments

This paper defines a highly scalable interval index structure called the Temporal Bin tree (TB-tree) that can be embedded in any resource planning application whose algorithms require efficiently estimating either the time that a resource will be available to process a specific task of known length or the net availability of a resource during a specified period of time. It is specifically engineered to meet the real-time response and space efficiency requirements of large-scale resource planning applications that are required for mass customization. Basically, the TB-tree is a binary tree structure that represents availability of a resource across a planning horizon. Representing intervals of availability hierarchically using a tree structure increases the efficiency of search for resource availability when the discretization of time is fine-grained or the planning horizon is long. The tree forms a backbone structure that does not require disruptive rebalancing during update operations, which would mitigate the ability of the tree to respond to queries in real time. Its specific implementation allows for random access at any level of the tree to further improve scalability. An application of planning to real-time promising of order due dates for custom built products provides the context for empirical evaluation. Results of analytical evaluations and simulation experiments clearly demonstrate the scalability of the TB-tree relative to existing index structures in terms of both time and space.     

Semplore: A scalable IR approach to search the Web of Data

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.     

支持向量回归机在物价预测上的应用

把时间序列SVM预测模型应用于物价指数的预测,采用逐步递归的方法进行,同时注意尽量减少训练样本的浪费和充分挖掘SVM模型适合短期预测的潜力.分析结果表明,无论是拟合情况,还是预测值的检验和物价指数的实际规律来看,都有很高的精度,可以作为物价指数预测的一种行之有效的方法.     

Efficient skycube computation using point and domain-based filtering

Skyline queries have been increasingly used in multi-criteria decision making and data mining applications. They retrieve a set of interesting points from a potentially large set of data points. A point is said to be interesting if it is not dominated by any other point. Skyline cube (skycube) consists of skylines of all possible non-empty subsets of a given set of dimensions. In this paper, we propose two algorithms for computing skycube using bitmaps that are derivable from indexes. The Point-based skycube algorithm is an improvement over the existing Bitmap algorithm, extended to compute skycube. The Point-based algorithm processes one point at a time to check for skylines in all subspaces. The Domain-based skycube algorithm views points as value combinations and probes entire search space for potential skyline points. It significantly reduces bitmap access for low cardinality dimensions. Our experimental study shows that the two algorithms strictly dominate, or at least comparable to, the current skycube algorithm in most of the cases, suggesting that such an approach could be a useful addition to the set of skyline query processing techniques.     

Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north-south climatic gradient

The aim of this study was to evaluate the use of ground-based canopy reflectance measurements to detect changes in physiology and structure of vegetation in response to experimental warming and drought treatment at six European shrublands located along a North-South climatic gradient. We measured canopy reflectance, effective green leaf area index (green LAIe) and chlorophyll fluorescence of dominant species. The treatment effects on green LAIe varied among sites. We calculated three reflectance indices: photochemical reflectance index PRI [531 nm; 570 nm], normalized difference vegetation index NDVI₆₈₀ [780 nm; 680 nm] using red spectral region, and NDVI₅₇₀ [780 nm; 570 nm] using the same green spectral region as PRI. All three reflectance indices were significantly related to green LAIe and were able to detect changes in shrubland vegetation among treatments. In general warming treatment increased PRI and drought treatment reduced NDVI values. The significant treatment effect on photochemical efficiency of plants detected with PRI could not be detected by fluorescence measurements. However, we found canopy level measured PRI to be very sensitive to soil reflectance properties especially in vegetation areas with low green LAIe. As both soil reflectance and LAI varied between northern and southern sites it is problematic to draw universal conclusions of climate-derived changes in all vegetation types based merely on PRI measurements. We propose that canopy level PRI measurements can be more useful in areas of dense vegetation and dark soils.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

基于ETM＋的北京西部山区乔木群落结构研究

利用遥感技术结合地面调查的方法对2008年春季北京西部山区乔木群落的多样性进行了研究。提取归一化植被指数（NDVI）,并计算NDVI随时间的变化率（△NDVI）;利用地面调查数据计算研究区的健康指数、Mar-galef丰富度指数、Shannon-Wiener多样性指数和Simpson多样性指数,并分析△NDVI、健康指数和多样性指数之间的关系。结果表明：（1）健康指数和多样性指数之间存在正相关关系,群落多样性指数越高,群落的整体健康状况越好。（2）△NDVI与健康指数之间呈正相关关系,健康指数越高的群落,单位时间内NDVI的增加值越大,植被群落的生长变化越明显。（3）△NDVI的高低可以代表区域植被的健康程度,反映区域植物群落的丰富度、多样性。△NDVI的值越高,植被群落的健康程度越好,植被群落的丰富度、多样性指数越高。本文的研究较好的将遥感技术和地面调查相结合,对△NDVI与群落植被健康程度、多样性指数进行了研究和验证,为以后研究植被群落多样性提供了一定的借鉴。     

基于倒排索引的密文数据库检索方法研究

针对实际密文数据库的应用,在全文检索倒排索引技术的基础上,设计了一种通过密文倒排索引文件对其进行快速检索的方法。密文索引文件中主要包含有索引项、相对应的记录主键等信息。检索时,通过用检索词匹配索引文件中的索引项,找到对应的记录主键集合,再根据记录主键集合查询密文数据库,获取相应的密文数据,进行解密即可获取明文数据信息。整个检索过程中不对数据库进行解密,从而实现了在不解密的情况下对密文数据库的快速检索。     

P2P网络关键技术与安全性探讨

详细介绍了现有的各种P2P网络安全模型,阐述了典型的P2P模型的技术特点,并对未来进行了展望。     

A revised method for ranking fuzzy numbers using maximizing set and minimizing set

A large number of methods have been proposed for ranking fuzzy numbers in the last few decades. Nevertheless, none of these methods can always guarantee a consistent result for every situation. Some of them are even non-intuitive and not discriminating. Chen proposed a ranking method in 1985 to overcome these limitations and simplify the computational procedure based on the criteria of total utility through maximizing set and minimizing set. However, there were some shortcomings associated with Chen’s ranking method. Therefore, we propose a revised ranking method that can overcome these shortcomings. Instead of considering just a single left and a single right utility in the total utility, the proposed method considers two left and two right utilities. In addition, the proposed method also takes into account the decision maker’s optimistic attitude of fuzzy numbers. Several comparative examples and an application demonstrating the usage, advantages, and applicability of the revised ranking method are presented. It can be concluded that the revised ranking method can effectively resolve the issues with Chen’s ranking method. Moreover, the revised ranking method can be used to differentiate different types of fuzzy numbers.