Spatial data management on a very large cadastral database

An overview is presented of spatial data management in the Netherlands Cadastre. As a result of more than three years research a major improvement in the accessibility and maintenance of 50 Gb available geometric data has been introduced. Quality assurance of topological relationships, maintenance of historic spatial data and flexible data access by a query tool is supported. The ‘Spatial Location Code’ used for spatial clustering and spatial indexing guarantees excellent performance of the relational database. This opens the possibility to develop new products, which can be delivered efficiently via networks.     

通用印刷企业管理信息系统的设计与开发

根据某印刷企业的信息化建设需求,结合我国印刷企业的管理特征及共性,采用.NET平台,开发了面向中小型印刷企业的通用印刷企业管理信息系统(PHMIS).PHMIS以印刷企业典型业务流程为主线,有订印单、工艺、库存、生产、质量、财务管理六个主要功能模块,实现了对印刷企业全部业务流程的管理.PHMIS系统已经在某印刷企业上线,经过四个月的试运行,系统功能符合企业需求,大幅度提高了企业管理水平.     

A parallel mixture of SVMs for very large scale problems

Support vector machines (SVMs) are the state-of-the-art models for many classification problems, but they suffer from the complexity of their training algorithm, which is at least quadratic with respect to the number of examples. Hence, it is hopeless to try to solve real-life problems having more than a few hundred thousand examples with SVMs. This article proposes a new mixture of SVMs that can be easily implemented in parallel and where each SVM is trained on a small subset of the whole data set. Experiments on a large benchmark data set (Forest) yielded significant time improvement (time complexity appears empirically to locally grow linearly with the number of examples). In addition, and surprisingly, a significant improvement in generalization was observed.     

From vacuum tubes to very large scale integration: a personalmemoir

The article traces one man's journey through the various different forms of electronics (from 1935 to 1986) and how they were developed and used at IBM. It also illuminates some of the reasons why particular decisions about various technologies were taken and the longer term results of those actions     

Painting-91: a large scale database for computational painting categorization

Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.     

Eco-Physic: Eco-Physical design initiative for very large databases

In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In data-centric applications, Database Management Systems are one of the major energy consumers when executing complex queries involving very large databases. Several initiatives have been proposed to deal with this issue, covering both the hardware and software dimensions. They can be classified in two main approaches assuming that either (a) the database is already deployed on a given platform, or (b) it is not yet deployed. In this study, we focus on the first set of initiatives with a particular interest in physical design, where optimization structures (e.g., indexes, materialized views) are selected to satisfy a given set of non-functional requirements such as query performance for a given workload. In this paper, we first propose an initiative, called Eco-Physic, which integrates the energy dimension into the physical design when selecting materialized views, one of the redundant optimization structures. Secondly, we provide a multi-objective formalization of the materialized view selection problem, considering two non-functional requirements: query performance and energy consumption while executing a given workload. Thirdly, an evolutionary algorithm is developed to solve the problem. This algorithm differs from the existing ones by being interactive, so that database administrators can adjust some energy sensitive parameters at the final stage of the algorithm execution according to their specifications. Finally, intensive experiments are conducted using our mathematical cost model and a real device for energy measurements. Results underscore the value of our approach as an effective way to save energy while optimizing queries through materialized views structures.     

大规模化合物子结构检索的并行实现与优化

化合物的子结构检索在计算机辅助药物设计、波普学、化学数据库等领域是不可或缺的工具.然而由于子结构检索是一个NP完备性的问题,获得用户可接受的平均检索时间一直是研究人员十分关注的问题,其方法主要有改进算法和提升硬件条件2个方面.当化学结构数据库的规模达到百万乃至千万级别时,尽管改进算法的方式能够获得一定的检索效率提升,但其提升的空间有限,因而,集群并行方式是大规模化合物子结构检索应用的必然选择.本文以Chem D B Portal的化学子结构检索系统为基础,实现了基于集群并行的化学子结构检索系统,并进行了任务均分、多线程并行等优化.在包含800万个化合物结构的化学结构数据库中,利用5个节点的小型集群,选取10个较为典型的提问结构进行子结构检索测试.测试结果为基于集群的化学子结构检索的平均检索时间由初始单节点时的34.1 min降低为2.75 min,检索效率平均提高12.4倍,表明在大规模乃至超大规模的数据条件下,集群并行化方式能够显著地提高子结构检索系统的执行效率.     

在MIS系统中构建联邦数据库的解决方案

作为信息集成的方法之一,联邦数据库同时满足现代企业MIS系统对数据库自治性和信息共享的需求。在介绍了联邦数据库系统和复制机制后,提出了一种在MIS系统中构建联邦数据库的解决方案,该方案利用DBMS提供的复制机制实现数据库间的数据传输与共享,并以对等的方式实现联邦数据库,消除了数据交换中的性能瓶颈和安全隐患。     

A novel framework for concept detection on large scale video database and feature pool

Large-scale semantic concept detection from large video database suffers from large variations among different semantic concepts as well as their corresponding effective low-level features. In this paper, we propose a novel framework to deal with this obstacle. The proposed framework consists of four major components: feature pool construction, pre-filtering, modeling, and classification. First, a large low-level feature pool is constructed, from which a specific set of features are selected for the latter steps automatically or semi-automatically. Then, to deal with the unbalance problem in training set, a pre-filtering classifier is generated, which the aim of achieving a high recall rate and a certain precision rate nearly 50% for a certain concept. Thereafter, from the pre-filtered training samples, a SVM classifier is built based on the selected features in the feature pool. After that, the SVM classifier is applied to classification of semantic concept. This framework is flexible and extensible in terms of adding new features into the feature pool, introducing human interactions in selecting features, building models for new concepts and adopting active learning.     

基于SQL Server7．0大型数据库应用系统性能优化的研究

该文论述了基于SQL Server7.0大型数据库应用系统的性能优化问题,并详细分析了影响数据库应用系统性能的一些因素和能有效提高系统性能的一些方法。     

An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.     

Using computational intelligence for large scale air route networks design

Due to the rapid development of air transportation, Air Route Networks (ARNs) need to be carefully designed to improve both efficiency and safety of air traffic service. The Crossing Waypoints Location Problem (CWLP) plays a crucial role in the design of an ARN. This paper investigates this problem in the context of designing the national ARN of China. Instead of adopting the single-objective formulation established in previous research, we propose to formulate CWLP as a bi-objective optimization problem. An algorithm named Memetic Algorithm with Pull–Push operator (MAPP) is proposed to tackle it. MAPP employs the Pull–Push operator, which is specifically designed for CWLP, for local search and the Comprehensive Learning Particle Swarm Optimizer for global search. Empirical studies using real data of the current national ARN of China showed that MAPP outperformed an existing approach to CWLP as well as three well-known Multi-Objective Evolutionary Algorithms (MOEAs). Moreover, MAPP not only managed to reduce the cost of the current ARN, but also improved the airspace safety. Hence, it has been implemented as a module in the software that is currently used for ARN planning in China. The data used in our experimental studies have been made available online and can be used as a benchmark problem for research on both ARN design and evolutionary multi-objective optimization.     

Comments on "a parallel mixture of SVMs for very large scale problems"

Collobert, Bengio, and Bengio (2002) recently introduced a novel approach to using a neural network to provide a class prediction from an ensemble of support vector machines (SVMs). This approach has the advantage that the required computation scales well to very large data sets. Experiments on the Forest Cover data set show that this parallel mixture is more accurate than a single SVM, with 90.72% accuracy reported on an independent test set. Although this accuracy is impressive, their article does not consider alternative types of classifiers. We show that a simple ensemble of decision trees results in a higher accuracy, 94.75%, and is computationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers.     

大规模数字电路系统可测性设计技术研究

为满足大规模数字电路系统测试、故障诊断的需要,可测性(DFT)设计已成为大规模数字电路系统设计中不可或缺的重要组成部分。结合边界扫描测试原理和大规模数字电路系统的主要特点,研究DFT实现的技术途径,并将其用于某大规模数字电路系统的设计中。实现了该大规模数字电路系统的一键式互连故障诊断及可扫描网络准确定位,有效简化了测试复杂度。     

Heuristic approaches for the optimal wiring in large scale robotic skin design

This paper faces the problem of optimizing the wiring and the connections in a tactile skin for robots. The robotic skin is a device composed of a network of tactile sensors, whose wiring can be very complex: the control of this complexity is a key problem. In the considered robotic skin, skin elements are grouped into skin patches, which output tactile data that have to be read by a micro-controller. The logical connections between the sensors must be defined in order to route signals through the network. A finite set of micro-controllers is given and a set of constraints is imposed on the given assignment and routing. The considered problem has a combinatorial nature and it can be formulated as a Minimum Constrained Spanning Forest problem with costs on arcs that cannot be a priori defined as they are solution-dependent. The problem is NP-hard. The paper introduces a mathematical formulation and then proposes a Multi-Start Heuristic algorithm and an Ant Colony Optimization approach whose effectiveness is evaluated through experimental tests performed on both real and synthetically generated instances.     

大力发展自主大规模工程力学应用软件

针对大规模工程力学应用软件开发在高水平CAE能力建设中的基础性地位,通过分析其国内外现状,指出当前正是发展自主软件的良好时机.分析开发大规模工程力学应用软件所面临的挑战,探讨基础框架的搭建及其关键技术,并介绍初步实践的结果.实践表明,充分利用开源资源能给软件开发经验不足的人员和团队提供较好的借鉴,有利于迅速接近先进技术水平.     

基于数据库设计的中英文双语网站开发模式

传统的中英文网站开发采用两套代码两个数据库或者一套代码两套资源文件的开发模式,在软硬件开销、代码与信息分离和系统的维护性方面均存在不同程度的缺陷。针对这一问题．把页面设计与数据库设计结合起来,利用编程技术,用一套代码一个数据库实现中英文双语网站的开发,以提高开发及维护效率,降低软件成本。     

大型网络MIS系统中基于角色的权限管理

该文分析了大型网络ＭＩＳ系统的特征,将基于角色的访问控制模型引入系统权限管理,提出了一种符合企业管理结构的基于应用层的系统访问控制管理方式。     

Active learning in very large databases

Query-by-example and query-by-keyword both suffer from the problem of “aliasing,” meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.