Direction relations and two-dimensional range queries: optimisation techniques

This paper defines direction relations (e.g., north, northeast) between two-dimensional objects and shows how they can be efficiently retrieved using B-, KDB- and R- tree-based data structures. Essentially, our work studies optimisation techniques for 2D range queries that arise during the processing of direction relations. We test the efficiency of alternative indexing methods through extensive experimentation and present analytical models that estimate their performance. The analytical estimates are shown to be very close to the actual results and can be used by spatial query optimizers in order to predict the retrieval cost. In addition, we implement modifications of the existing structures that yield better performance for certain queries. We conclude the paper by discussing the most suitable method depending on the type of the range and the properties of the data.     

Recognition of shapes by attributed skeletal graphs

In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classification. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of different objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and effective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and occlusions.     

SBH: Super byte-aligned hybrid bitmap compression

Bitmap indexes are commonly used in data warehousing applications such as on-line analytic processing (OLAP). Storing the bitmaps in compressed form has been shown to be effective not only for low cardinality attributes, as conventional wisdom would suggest, but also for high cardinality attributes. Compressed bitmap indexes, such as Byte-aligned Bitmap Compression (BBC), Word-Aligned Hybrid (WAH) and several of their variants have been shown to be efficient in terms of both time and space, compared to traditional database indexes. In this paper, we propose a new technique for compressed bitmap indexing, called Super Byte-aligned Hybrid (SBH) bitmap compression, which improves upon the current state-of-the-art compression schemes. In our empirical evaluation, the query processing time of SBH was about five times faster than that of WAH, while the size of its compressed bitmap indexes was retained nearly close to that of BBC.     

大型工件转动定位机构设计

介绍一种大型工件转动定位传动机构的设计,采用A轴伺服电机分度,通过双蜗轮蜗杆及齿轮传动对卡盘上的工件进行分度定位,解决了大型工件定位精度不高及转动扭矩过大等问题。     

高维索引机制中的降维方法综述

全面总结了现有的高维数据降维方法,深入分析比较了它们各自的优缺点,并从应用的角度指出其今后发展的趋势在于多种降维技术的合理组合,尤其是线性降维与非线性降维思想的有机结合。     

基于Pro/E弧面凸轮分度装置的3D参数化设计

以弧面凸轮分度装置的中心距和分度滚子数为已知条件,定义了与其他所有尺寸的参数关系,并且基于Pro/Engineer Wildfire 4.0以及其二次开发工具Program对弧面凸轮分度装置做了如下研究工作:以弧面凸轮分度装置为研究对象,系统阐述了其工作原理、设计方法、运动形式以及结构形式;以Pro/E作为三维建模工具...     

新型平面包络蜗杆式分度凸轮机构的原理与分析

针对弧面分度凸轮机构变速比、无侧隙、正交交错轴传动特点 ,本文在高副机构线啮合原理的基础上 ,借鉴平面包络蜗轮蜗杆的传动技术 ,首次提出了平面包络凸轮分度机构的基本构思 ,建立了平面包络蜗杆分度凸轮机构共轭的基本方程 ,讨论了机构瞬时接触线方程及其分布规律、啮合面方程、共轭界限条件、诱导曲率的计算、以及设计分析要点等 ,并以计算实例进行验证 ,为进一步的深入分析与制造研究 ,奠定了坚实的基础。     

Array-index: a plug&search K nearest neighbors method for high-dimensional data

Previous algorithms of data partitioning methods (DPMs) to find the exact K-nearest neighbors (KNN) at high dimensions are outperformed by a linear scan method [J.M. Kleinberg, Two algorithms for nearest neighbor search in high dimensions, 29th ACM Symposium on Theory of computing, 1997; R. Weber, H.-J. Schek, S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. in: Proc. of the 24th VLDB, USA, 1998]. In this paper, we present a “plug&search” method to greatly speed up the exact KNN search of existing DPMs. The idea is to linearize the data partitions produced by a DPM, rather than the points themselves, into a one-dimensional array-index, that is simple, compact and fast. Unlike most DPMs that support KNN search, which require storage space linear, or exponential [J.M. Kleinberg, Two algorithms for nearest neighbor search in high dimensions, 29th ACM Symposium on Theory of computing, 1997; M. Hagedoom, Nearest neighbors can be found efficiently if the dimension is small relative to the input size, ICDT 2003], in dimensions, the array-index requires a storage space that is linear in the number of mapped partitions.     

Searchable words on the Web

In designing data structures for text databases, it is valuable to know how many different words are likely to be encountered in a particular collection. For example, vocabulary accumulation is central to index construction for text database systems; it is useful to be able to estimate the space requirements and performance characteristics of the main-memory data structures used for this task. However, it is not clear how many distinct words will be found in a text collection or whether new words will continue to appear after inspecting large volumes of data. We propose practical definitions of a word and investigate new word occurrences under these models in a large text collection. We inspected around two billion word occurrences in 45 GB of World Wide Web documents and found just over 9.74 million different words in 5.5 million documents; overall, 1 word in 200 was new. We observe that new words continue to occur, even in very large datasets, and that choosing stricter definitions of what constitutes a word has only limited impact on the number of new words found.     

Multimedia indexing and retrieval: ever great challenges

In this introduction, we present a brief state of the art of multimedia indexing and retrieval as well as highlight some notions explored in the special issue. We hope that the contributions of this special issue will present ingredients for further investigations on this ever challenging domain. The special issue is actually situated between old problems and new challenges, and contribute to understand the next multimedia indexing and retrieval generation. The contributions explore wide range of fields such as signal processing, data mining and information retrieval.