基于Rough集潜在语义索引的Web文档分类

Rough集(粗糙集)埋论是一种处理不确定或模糊知识的数学工具。提出了一种基于Rough集理论的潜在语义索引的Web文档分类方法。首先应用向量空间模型表示Web文档信息，然后通过矩阵的奇异值分解来进行信息过滤和潜在语义索引；运用属性约简算法生成分类规则，最后利用多知识库进行文档分类。通过试验比较，该方法具有较好的分类效果。     

Normal vector compression of 3D mesh model based on clustering and relative indexing

As the transmission of 3D shape models through Internet becomes more important, the compression issue of shape models gets more critical. While the compressions of topology and geometry have been explored significantly, the same issue for normal vectors has not yet been studied as much as it deserves.

Presented in this paper is an approach to compress the normal vectors of a 3D mesh model using the concept of clustering and relative indexing. The model is assumed to be manifold triangular mesh model with normal vectors associated with vertices. The proposed scheme clusters the normal vectors of given model and the representative normal vector of each cluster is referred to via a mixed use of relative as well as absolute indexing concepts. It turns out that the proposed approach achieves a significant compression ratio (less than 10% of the original VRML model files) without a serious sacrifice of the visual quality.     

A Peer-to-Peer Approach to Web Service Discovery

Web Services are emerging as a dominant paradigm for constructing and composing distributed business applications and enabling enterprise-wide interoperability. A critical factor to the overall utility of Web Services is a scalable, flexible and robust discovery mechanism. This paper presents a Peer-to-Peer (P2P) indexing system and associated P2P storage that supports large-scale, decentralized, real-time search capabilities. The presented system supports complex queries containing partial keywords and wildcards. Furthermore, it guarantees that all existing data elements matching a query will be found with bounded costs in terms of number of messages and number of nodes involved. The key innovation is a dimension reducing indexing scheme that effectively maps the multidimensional information space to physical peers. The design and an experimental evaluation of the system are presented.     

基于UCL的网页自动标引技术

UCL(UniformContentLocator)是作者、编者和读者进行语义沟通的工具,是进行信息快速选择、智能代理和信息主动服务的基础。该文针对网络信息检索中的自动标引问题,提出了一种基于UCL的网页自动标引技术。研究了从HTML编写的网页映射到XML文档的过程,并从中提取符合用户兴趣模型的UCL字段,从而达到网页自动标引的目的。实验验证了理论方案的正确性和有效性。     

圆柱分度凸轮单侧面加工及误差计算方法研究

应用空间啮合原理和旋转变换矩阵理论，推导了圆锥滚子从动件圆柱分度凸轮的理论工作廓面方程及单侧面加工凸轮实际工作廓面方程；廓面采取单侧面加工时，给出了刀具误差对凸轮廓面法向误差影响的精确计算方法和近似计算方法，解决了空间凸轮廓面误差计算的难题，进一步完善了圆柱分度凸轮机构的设计和制造理论。     

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.     

k-Nearest neighbor searching in hybrid spaces

Little work has been reported in the literature to support k-nearest neighbor (k-NN) searches/queries in hybrid data spaces (HDS). An HDS is composed of a combination of continuous and non-ordered discrete dimensions. This combination presents new challenges in data organization and search ordering. In this paper, we present an algorithm for k-NN searches using a multidimensional index structure in hybrid data spaces. We examine the concept of search stages and use the properties of an HDS to derive a new search heuristic that greatly reduces the number of disk accesses in the initial stage of searching. Further, we present a performance model for our algorithm that estimates the cost of performing such searches. Our experimental results demonstrate the effectiveness of our algorithm and the accuracy of our performance estimation model.     

Optimizing the parallel adaptive indexing algorithm on multi-core CPUs

An improved parallel adaptive indexing algorithm on multi-core CPUs is proposed to solve the problems that the parallel adaptive indexing algorithms cannot take full advantage of the CMP's parallel execution resource, and properly process the sequential query pattern. Based on the optimization of the Refined Partition Merge algorithm, our improved parallel adaptive indexing algorithm combines the Parallel Database Cracking method with the Refined Partition Merge algorithm. In our algorithm, when fewer data chunks are in the index, we use the optimized Refined Partition Merge algorithm so as to reduce the probability of conflict between threads, decrease the waiting time, and increase the utilization of the threads, and when more data chunks are in the index, we use the Parallel Database Cracking method so as to take full advantage of the CMP's parallel execution resources. Besides, we propose an optimization for the robustness, which makes our algorithm suitable for two common query patterns. Experiments show that our method can reduce the query time by 25.7％～33.2％, and suit with common query patterns.