面向交通轨迹的数据流可视化方法

为通过视觉图形交互式创建交通轨迹可视化,提出一种面向交通轨迹的数据流可视化方法.使用向导推荐辅助构建数据流图,完成交通轨迹聚类分析、相关性分析和异常检测.首先,开发数据流系统用于交互式构建交通轨迹可视化;其次,使用基于数据流异构网络的向导推荐模型辅助构建数据流图.该推荐模型将向导推荐作为图搜索问题,将用户关于向导推荐结果的评分反馈至数据流异构网络以更新节点关系.基于真实交通轨迹数据的案例评估和关于用户选择倾向的里克特量表评估结果表明,所提方法是有效的.     

面向签到日志的用户行为模式交互探索

签到日志记录了用户对于某类设施的使用情况,从中发现用户行为模式,在很多领域如精确广告投放、犯罪团伙发现等方面都具有非常广泛的应用价值.但是,发现过程却较为困难,主要因为：（1）日志数据体现为长时间序列且含有噪声,导致数据在高维空间分布较为稀疏,影响模式提取的准确性;（2）行为模式往往与不同的时间尺度相关;（3）多样的参数选择空间以及数据处理方式使得传统的机器学习方法很难获得可信且易于理解的行为分析结果.提出一种面向签到日志的用户行为模式交互探索的方法,该过程采用动态子空间策略,动态改变用于分析相似行为模式的时间片,从而减少人为设定参数对于分析结果的影响.方法集成了一个可视分析工具以支持该过程,利用该工具,分析人员可以实时了解方法每一步发现的模式,及时调整分析过程、直观理解和验证分析结论.包含了一个基于真实数据集的案例分析和一个来自不同领域专家的评审,其结果验证了方法的有效性.     

针对气味数据的交互式聚类可视分析框架

自然界中的气味通常是多种成分的混合物,相比图像和声音具有特殊的数据复杂性,且由于缺少有效的表示方法导致气味研究相对滞后.聚类分析是研究气味的一个重要方法,有助于增进对气味的理解、识别、合成和设计,然而大部分高效的聚类方法都是被动的,用户往往期望参与到计算过程中,利用领域知识和经验进行交互式聚类.气味数据存在多种可能的聚类方式,用户需要对多组聚类结果进行比较,以确定最优的聚类.提出一个对气味数据进行交互式聚类的可视分析框架.首先使用人工神经网络结合多分子气味的高维化学信息和感官描述信息,计算一种低维度的气味嵌套表示;然后基于高斯混合模型的交互式聚类算法让用户对气味嵌套进行符合预期的聚类和重新聚类;最后用户可通过聚类可视化视图比较多组聚类结果的异同,增进对气味数据的理解,从而找到最佳的聚类方式.通过实验和2位领域专家的测试与评估,验证了该可视分析框架的有效性.     

面向空气质量监测数据时空多维属性的可视分析方法

空气质量监测数据具有显著的时空多维属性.传统的平行坐标技术虽然能够有效地展示数据的多维属性信息,却在分析与解读空气质量监测数据各个属性的时空变化规律方面表现出一定的局限性,在平行坐标展示空气质量监测数据多维属性的基础上,提出一种支持用户交互式探索大气污染时空特征的可视分析方法.首先利用平行坐标展示空气质量监测数据,支持用户交互改变时空维度以及指定坐标轴排列顺序;然后引入角度面积正负相关性等方式度量数据在平行坐标系中的布局差异,并且通过矩阵图和交互式柱状图分别展示不同时空维度下数据的布局差异;再综合考虑各个属性之间的数据布局差异,构建相似性矩阵,利用多维标度法对当前时空维度的数据进行降维,获得初始数据在低维空间的表示;最后利用层次聚类方法对低维空间的数据表达做聚类分析,并且分别设计时钟隐喻图和地域抽象图描述各个类别的时空节点组成.集成上述可视化算法设计便捷的用户交互模式,开发面向空气质量监测数据时空多维属性的可视分析原型系统,为用户快速分析和解读大气污染的时空特征及潜在规律提供有效手段.通过大量的可视化效果及用户反馈结果,进一步验证了文中所提可视分析方法的有效性和实用性.     

基于多用户垃圾短信数据的伪基站活动轨迹可视分析方法

由于传统短信服务（SMS）存在严重的安全漏洞,不法分子可通过伪基站发送大量垃圾短信和诈骗信息,严重干扰人们的日常生活,危及人们的财产安全,影响社会稳定。然而,现有的关于伪基站的分析方法不能很好地感知某个大区域内的伪基站分布态势,难以探索伪基站的活动规律,把握伪基站的运动轨迹。针对这一问题,提出基于多用户垃圾短信数据进行可视分析的方法。通过用户上报垃圾短信的时间、相对位置、内容等信息,来追溯伪基站的近似活动轨迹;并通过设计多种可视化视图,实现一个多视图组合的交互式可视分析系统。最后,采用ChinaVis2017挑战赛I的数据集进行实验和案例分析,验证了该方法的可行性和有效性。     

Interactive Depth-of-Field Rendering with Secondary Rays

This paper presents an efficient method to trace secondary rays in depth-of-field (DOF) rendering, which significantly enhances realism. Till now, the effects by secondary rays have been little addressed in real-time/interactive DOF rendering, because secondary rays have less coherence than primary rays, making them very difficult to handle. We propose novel measures to cluster secondary rays, and take a virtual viewpoint to construct a layered image-based representation for the objects that would be intersected by a cluster of secondary rays respectively. Therefore, we can exploit coherence of secondary rays in the clusters to speed up tracing secondary rays in DOF rendering. Results show that we can interactively achieve DOF rendering effects with reflections or refractions on a commodity graphics card.     

学习分析技术在大数据环境中的应用

学习分析是大数据在教育应用中的焦点,本文对学习分析的核心环节进行技术剖析,梳理主要的学习分析工具,以实证研究的方式,从课程建设者、教学管理者和辅导教师这3种不同用户视角展示学习分析技术的应用过程。研究以某课程平台的学习行为数据作为研究样本,应用统计、可视化、聚类、关联规则等方法,采用Excel,SPSS,Weka等工具,分析课程模块访问频次,了解不同教学组对学生登录周数的影响,刻画学生的分类特征,发现隐含的内在规律。研究表明,学习分析技术充分发挥了教育大数据的价值,使数据成为教学干预、实施决策的重要依据。     

基于混合现实的可视分析综述

混合现实具有新颖的人机交互方式,拓展了可视化与交互界面的设计空间,拥有广阔的应用前景.近几年,混合现实技术快速发展,许多研究者将其用于可视分析领域.首先,介绍了混合现实的概念与发展现状,并从显示与交互2个方面讨论混合现实环境与传统交互环境的差异,分析混合现实可视化的优势与局限性;其次,简介基于混合现实的可视分析的3个应用场景,即增强可视化、实景分析和协作分析;最后,给出基于混合现实的可视分析面临的机遇与挑战.     

面向科学文献同名消歧的可视化分析方法

直观地展示科研团队中的合作关系,能够辅助用户在科学文献管理工作中更好地进行文献同名消歧.由于科学文献作者合作关系复杂,难以通过可视化进行直观展示,因此设计并实现了面向科学文献的同名消歧可视化分析方法.首先,根据文献合著者存在的合作网络生成合作关系图,揭示科研团队中作者的合作关系;其次,为了展示不同作者研究方向之间的相关性,设计了合作关系图和发文期刊图之间的可视化联动;最后,通过结合深度学习模型分别对文献和作者进行分类,实现了从作者和团队任意主体出发的交叉分析与连贯推理.基于北京工业大学4 000篇论文构成的真实数据进行案例分析,并邀请了科研管理人员和学生通过实验完成度和李克特量表进行评价,验证了所提方法的有效性.     

Enhancing the Effectiveness of Interactive Case-Based Reasoning with Clustering and Decision Forests

In interactive case-based reasoning, it is important to present a small number of important cases and problem features to the user at one time. This goal is difficult to achieve when large case bases are commonplace in industrial practice. In this paper we present our solution to the problem by highlighting the interactive user- interface component of the CaseAdvisor system. In CaseAdvisor, decision forests are created in real time to help compress a large case base into several small ones. This is done by merging similar cases together through a clustering algorithm. An important side effect of this operation is that it allows up-to-date maintenance operations to be performed for case base management. During the retrieval process, an information-guided subsystem can then generate decision forests based on users' current answers obtained through an interactive process. Possible questions to the user are carefully analyzed through information theory. An important feature of the system is that case-base maintenance and reasoning are integrated in a seamless whole. In this article we present the system architecture, algorithms as well as empirical evaluations.     

Human–computer interaction in evolutionary visual software analytics

Software evolution is made up of changes carried out during software maintenance. Such accumulation of changes produces substantial modifications in software projects and therefore vast amounts of relevant facts that are useful for the understanding and comprehension of the software project for making additional changes. In this scenario, evolutionary visual software analytics is aimed to support software maintenance, with the active participation of users, through the understanding and comprehension of software evolution by means of visual analytics and human computer interaction. It is a complex process that takes into account the mining of evolutionary data, the subsequent analysis of the mining process results for producing evolution facts, the use of visualizations supported by interaction techniques and the active participation of users. Hence, this paper explains the evolutionary visual software analytics process, describes a framework proposal and validates such proposal through the definition and implementation of an architecture.     

电力设备运行中监测数据库信息分析方法

针对电力设备运行过程中监测数据库管理分析能力落后、监测数据管理欠妥的问题,本文设计了一种新型的监测数据库管理分析系统,并设计出基于ARM Cortex-M0微处理器的硬件监测电路,通过压电传感器和电流振荡传感器实现系统对电力设备运行状态自动信息采集,构建出t-SNE算法模型。实验结果表明,本研究系统的准确度高达98%,运行耗能相对较低(在5%左右),可视化效果较好,动态监测预警能力较强。     

基于E-t-SNE的混合属性数据降维可视化方法

针对传统的t分布随机近邻嵌入（t-SNE）算法只能处理单一属型数据,不能很好地处理混合属性数据的问题,提出一种扩展的t-SNE降维可视化算法E-t-SNE,用于处理混合属性数据。该方法引入信息熵概念来构建分类属性数据的距离矩阵,采用分类属性数据距离与数值属性数据欧式距离相结合的方式构建混合属性数据距离矩阵,将新的距离矩阵输入t-SNE算法对数据进行降维并在二维空间可视化展示。此外,为验证算法有效性,采用[k]近邻[(kNN)]算法对混合数据降维后的效果进行评价。通过在UCI数据集上的实验表明,该方法在处理混合属性数据方面,不仅具有较好的可视化能力,而且能有效地对不同类别的数据进行降维分簇,提升后续分类器的分类准确率。     

Mapping the study of learning analytics in higher education

ABSTRACT

In recent years, the application of technological innovation in higher education has become more and more widely spread, and technological innovation has been improving the level of education. In the research of higher education with innovation technology, one of the main focuses is on the dynamic data which can lay a foundation for the analysis of educational activities by learning analytics. The dynamic data created by technological innovation will become the key basis for analytical research and development in higher education. The methods and analysis results of learning analytics will directly affect decision-making and strategy about higher education. In this paper, we use bibliometric and visualisation methods to review the literature, in order to highlight the development of learning analytics in higher education. Using bibliometric analysis, our study depicts the development process of the main methods used in learning analytics, and summarises the current situation in this field, which increases the level of understanding provided by those studies. Finally, we summarise the research hotspots and study trends, which will be useful for future study in this field.     

Compiling CSPs into Tree-Driven Automata for Interactive Solving

Constraint programming techniques are widely used to model and solve decision problems and many algorithms have been developed to solve automatically and efficiently families of CSPs; nevertheless, they do not help solve interactive decision support problems, like product configuration. In such problems, the user chooses the values of the variables, and the role of the system is not to solve the CSP, but to help the user in this task. Dynamic global consistency maintaining is one of the most useful functionalities that should be offered by such a CSP platform. Unfortunately, this task is intractable in the worst case. Since interactivity requires short response times, intractability must be circumvented some way. To this end, compilation methods have been proposed that transform the original problem into a data structure allowing a short response time. In this paper, we extend the work of Amilhastre et al. [1] and Vempaty [15] by the use of a new structure, tree-driven automata, that takes advantage of the structural characteristics of configuration problems (decomposition of the components into independent subcomponents). Tree-driven automata can be far more compact than classical automata while keeping their good properties, especially a tractable complexity for the maintenance of global consistency.