科学计算可视化与数据可视化的比较研究

可视化技术是在20世纪80年代发展起来的一个新的技术领域。本文将着重介绍科学计算可视化的提出背景及其特点,数据可视化的概念意义及应用,以及它们的基本思想、比较它们的不同点及应用领域。     

从科学计算可视化到信息可视化

信息可视化技术是情报学研究的新领域。它是从科学计算可视化发展来的,是可视化众多分支的一个。分析了从科学计算可视化到信息可视化的若干阶段;对比了它们之间各自的特点、相同点和区别;最后从数据来源、处理技术和处理过程上进行了总结。     

大数据时代背景下的web数据可视化探析

数据可视化技术可借助人脑的视觉思维能力,帮助人们理解大量的数据信息,发现数据中隐含的规律,从而提高数据的使用效率。面对大数据深奥的面貌,如何才能让大型数据集变得亲切和易于理解,可视化无疑是最有效的途径。对大数据背景下的数据可视化应用展开研究,将有助于我们发展和创新数据可视化技术。     

大数据的“豆形”可视化及其在资本市场中的应用

"大数据"时代催发了可视化技术的再创新。"豆形图"以其处理海量数据的超强能力可以直观地展现大数据的结构特征,并为大数据的可视化分析奠定基础。本文首先引入"豆形图"及其可视化大数据的特征,并将其应用于资本市场中典型的金融大数据——超高频数据,构造用于可视化分析的豆形时间序列,分析数据的日内模式和动态特征,为大数据的可视化技术开辟新的方向。     

以用户为中心的关联数据可视化方案设计

分析现有关联数据可视化技术的缺陷,提出一种以用户为中心的关联数据可视化方案,用户不需要掌握专业的语义网技术、程序开发技能和可视化知识,即可对任意关联数据进行可视化展示。基于Mazumdar视图框架提出通用可视化概念框架,基于用户交互分析设计可视化工作流,基于可视化工作流设计软件架构,并提出软件平台的技术实现方案。     

关系敏感型数据可视化方法研究

数据展现是人们利用数据的终端环节,对数据进行采集、加工和分析,最终得到有用的数据。数据可视化作为一门学科,它的主要作用是将数据以用户需要的方式进行展示。对表达事物间及事物的不同属性间关系的数据特征进行了分析,提出了适用于该类数据的若干可视化方法。     

数据可视化技术实现流程探讨

数据可视化技术广泛应用于各个领域,备受人们关注。阐述了数据可视化的概念,结合对数据可视化应用的实例,详细论述数据可视化技术展现数据的工作流程。     

时间敏感型数据可视化方法研究

数据可视化是关于数据视觉表达形式的研究,它关注如何以适当的形式向用户展现数据。数据按其特点和用户需求,可划分为时间敏感型、比例敏感型、关系敏感型和空间敏感型等类别。重点分析了时间敏感型数据的特征,提出了有关数据可视化的方法,在一定程度上能够帮助用户加强对数据的分析和使用,并挖掘数据背后隐含的可用信息。     

基于复杂网络的图书管理信息数据可视化研究

目前针对图书管理信息数据的研究方法与手段众多,文章利用最新的复杂网络研究手段及方法对选取的图书借阅典型数据进行了全面的可视化实证分析研究,具体包括借阅数据结构可视化、社团规划可视化和数据链路预测可视化,深入讨论了各种可视化实现的具体工具及代表算法,尝试阐明复杂网络与图书管理数据相结合的研究机制,试图为搭建高效合理的图书借阅环境提供一种比较实用的新方法.     

数据可视化信息误导现状及发展趋势研究

[研究目的]数据可视化在当今社会被频繁的使用,但设计不当会产生误导,引发信息安全和伦理问题。研究数据可视化信息误导国内外研究现状和发展方向,能为该领域未来研究及可视化批判性素养提供参考和启示。[研究方法]搜集、编译国内外相关文献,分别从数据可视化信息误导概念界定、当前研究进展、主要研究方向3个视角进行综述。[研究结论]国内对于数据可视化信息误导研究处于新兴阶段,理论体系尚未构成。借鉴中西方已有的研究提出数据可视化信息误导相关概念,整理当前数据可视化信息误导具体方式探究、用户体验研究以及防御性技术研发3个具体研究方向,并从概念、认知和应用角度对未来研究进行展望。     

地球科学数据导航系统数据资源状况分析

地球科学数据导航系统是我们近两年在自建信息平台基础之上建立起来的。它搜集网络上已经公布的地球科学数据资源的元数据信息，然后根据地球科学数据导航系统的检索和导航需求对其加以广泛地整理，最终根据元数据信息的描述，有效地实现对全部4000多条地球科学数据信息的系统检索揭示和指引。本文针对地球科学数据导航系统的全部数据资源从几个主要方面进行了分析和评价。     

国内外引文分析研究进展综述

对发表于2000-2008年间代表引文分析研究进展的大量情报学文献进行了评述,一方面勾勒出了引文分析发展的现状,另一方面也跟踪了引文分析里新出现的理论、方法,期望能为国内外同行了解引文分析最新进展、制定学科发展战略提供借鉴。     

基于数据挖掘的专利数据预处理系统的设计与实现

本文针对当前专利数据预处理中存在的处理效率低、耗费资源量大、处理准确度不高的问题,结合数据挖掘中预处理技术,以欧洲专利局文献管理数据库(DOCDB)专利数据为例,设计并实现了DOCDB专利数据的预处理系统。该系统能够对DOCDB专利数据文件的结构进行解析,提取相关的专利信息,并将处理后的数据存入数据库中。实验结果表明,该系统能够高效处理专利数据,有力的提高了专利预处理的自动化水平。     

A survey on evaluation of summarization methods

The increasing volume of textual information on any topic requires its compression to allow humans to digest it. This implies detecting the most important information and condensing it. These challenges have led to new developments in the area of Natural Language Processing (NLP) and Information Retrieval (IR) such as narrative summarization and evaluation methodologies for narrative extraction. Despite some progress over recent years with several solutions for information extraction and text summarization, the problems of generating consistent narrative summaries and evaluating them are still unresolved. With regard to evaluation, manual assessment is expensive, subjective and not applicable in real time or to large collections. Moreover, it does not provide re-usable benchmarks. Nevertheless, commonly used metrics for summary evaluation still imply substantial human effort since they require a comparison of candidate summaries with a set of reference summaries. The contributions of this paper are three-fold. First, we provide a comprehensive overview of existing metrics for summary evaluation. We discuss several limitations of existing frameworks for summary evaluation. Second, we introduce an automatic framework for the evaluation of metrics that does not require any human annotation. Finally, we evaluate the existing assessment metrics on a Wikipedia data set and a collection of scientific articles using this framework. Our findings show that the majority of existing metrics based on vocabulary overlap are not suitable for assessment based on comparison with a full text and we discuss this outcome.     

Stewardship of personal data on social networking sites

The extant Information Management literature highlights the asymmetric distribution of power between users and online platforms, while the issues related to the stewardship of personal data on such platforms remain problematic and largely unresolved. To address that lacuna, we propose a conceptual design that can help to overcome many of the challenges related to storage, analysis, and integrity associated with the stewardship of personal data on online platforms. We adopt a systemic perspective and propose a shift from the current user-platform relationship to one in which users control the level of access to their data, organisations are relieved from the burden of maintaining personal data, and the data are not decoupled from information about their provenance and context of origin. We apply our conceptual design to the context of social networking sites, where we specifically address issues related to privacy, and identity and pave the path to a broader set of possible applications. We discuss the significance and timeliness of our proposed conceptual design for the stewardship of personal data, and the importance of our findings for future research, as well as for the design of online platforms.     

Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems

Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user–item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.     

煤矿数据集成技术研究与应用

本文分析了当前煤炭企业信息化水平低,信息孤岛现象严重,应用系统间相互隔离无法协同工作以及更上层次的集成应用更加困难的问题,提出了数据集成的目标,设计了该系统的体系结构,从而解决了不同平台间异构系统互操作以及数据多级综合利用的问题。并结合本系统的体系结构,详述了系统设计流程、实现过程和模式集成示例。     

The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age

The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer science, statistics and mathematics.     

基于PCA的SOM网络在基因数据聚类分析中的应用

把主分量分析（PCA）方法和自组织特征映射网络（SOM）相结合,应用到基因数据聚类分析中。首先对基因数据集进行PCA分析,提取出少量的特征主分量,再对数据集进行降维。这些主分量基本上可以反映原数据集的综合信息,然后应用SOM网络对得到的特征分量进行聚类分析,把相似的基因划分到一个区域。实验结果表明,与单一地选用SOM网络进行聚类分析相比,该方法有较高的分类正确率及较为清晰的分类边界,是一种非常有效的聚类分析方法。     

城乡居民信息消费差距对城乡收入差距的影响

信息消费的过程中能产生知识资源、创造财富,因而不仅是一种消费行为,更是一种投资行为.从信息消费的投资功能这一角度来研究其对城乡收入差距的影响,利用1997-2012年的面板数据,将全国31个省市分为东、中、西3个区域分别构建模型分析城乡居民信息消费差距对城乡收入差距的影响.结果表明：东部地区的城乡居民信息消费差距拉大了城乡收入差距,中部地区没有显著的影响,西部地区则没有形成稳定的关系.