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1.
    
Execution trace logs are used to analyze system run‐time behaviour and detect problems. Trace analysis tools usually read the input logs and gather either a detailed or brief summary of them to later process and inspect in the analysis steps. However, continuous and lengthy trace streams contained in the live tracing mode make it difficult to indefinitely record all events or even a detailed summary of the whole stream. This situation is further complicated when the system aims to compare different parts of the trace and provide a multilevel and multidimensional analysis. This paper presents an architecture with corresponding data structures and algorithms to process stream events, generate an adequate summary—detailed enough for recent data and succinct enough for old data—and organize them to enable an efficient multilevel and multidimensional analysis, similar to online analytical processing analyses in the database applications. The proposed solution arranges data in a compact manner using interval forms and enables the range queries for any arbitrary time durations. Because this feature makes it possible to compare of different system parameters in different time areas, it significantly influences the system's ability to provide a comprehensive trace analysis. Although the Linux operating system trace logs are used to evaluate the solution, we propose a generic architecture that can be used to summarize various types of stream data. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

2.
OD数据是一类提供起点和终点位置、稀疏地描述对象移动轨迹的数据.OD数据可视分析能够发现群体移动模式,挖掘空间异常和隐藏关系,深入探索和分析多种统计属性,是数据可视化、地理空间分析等领域的研究热点.从OD数据的地理空间点对结构出发,阐述经典的OD数据可视化方法和原理,如OD矩阵、OD嵌套地图、OD流图等;然后从空间聚类维度、空间语义维度和时空联合维度对OD数据进行可视分析,探寻其在特征的表达、感知、提取、呈现等方面的应用场景和增强意义;再针对大规模OD数据可视化的视觉混淆问题,梳理OD数据可视分析研究方法中过滤、采样及聚合等视觉简化方法;最后通过OD数据可视分析技术在不同行业领域的应用及具体案例,总结和深入讨论OD数据可视分析中存在的挑战性问题,并展望该领域的发展趋势.  相似文献   

3.
    
While JavaScript programs have become pervasive in web applications, they remain hard to reason about. In this context, most static analyses for JavaScript programs require precise call graph information, since the presence of large numbers of spurious callees significantly deteriorates precision. One of the most challenging JavaScript features that complicate the inference of precise static call graph information is read/write accesses to object fields, the names of which are computed at runtime. JavaScript framework libraries often exploit this facility to build objects from other objects, as a way to simulate sophisticated high-level programming constructions. Such code patterns are difficult to analyze precisely, due to weak updates and limitations of unrolling techniques. In this paper, we observe that precise field origination relations can be inferred by locally reasoning about object copies, both regarding to the object and to the program structure, and we propose an abstraction that allows to separately reason about field read/write access patterns working on different fields and to carefully handle the sets of JavaScript object fields. We formalize and implement an analysis based on this technique. We evaluate the performance and precision of the analysis on the computation of call graph information for examples from jQuery tutorials.  相似文献   

4.
We describe a new conceptual methodology and related computational architecture called Knowledge‐based Navigation of Abstractions for Visualization and Explanation (KNAVE). KNAVE is a domain‐independent framework specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration, in a context‐sensitive manner, of time‐oriented raw data and the multiple levels of higher level, interval‐based concepts that can be abstracted from these data. The KNAVE domain‐independent exploration operators are based on the relations defined in the knowledge‐based temporal‐abstraction problem‐solving method, which is used to abstract the data, and thus can directly use the domain‐specific knowledge base on which that method relies. Thus, the domain‐specific semantics are driving the domain‐independent visualization and exploration processes, and the data are viewed through a filter of domain‐specific knowledge. By accessing the domain‐specific temporal‐abstraction knowledge base and the domain‐specific time‐oriented database, the KNAVE modules enable users to query for domain‐specific temporal abstractions and to change the focus of the visualization, thus reusing for a different task (visualization and exploration) the same domain model acquired for abstraction purposes. We focus here on the methodology, but also describe a preliminary evaluation of the KNAVE prototype in a medical domain. Our experiment incorporated seven users, a large medical patient record, and three complex temporal queries, typical of guideline‐based care, that the users were required to answer and/or explore. The results of the preliminary experiment have been encouraging. The new methodology has potentially broad implications for planning, monitoring, explaining, and interactive data mining of time‐oriented data.  相似文献   

5.
如何从海量数据中快速有效地挖掘出有价值的信息以更好地指导决策,是大数据分析的重要目标.可视分析是一种重要的大数据分析方法,它利用人类视觉感知特性,使用可视化图表直观呈现复杂数据中蕴含的规律,并支持以人为本的交互式数据分析.然而,可视分析仍然面临着许多挑战,例如数据准备代价高、交互响应高延迟、可视分析高门槛和交互模式效率低.为应对这些挑战,研究者从数据管理、人工智能等视角出发,提出一系列方法以优化可视分析系统的人机协作模式和提高系统的智能化程度.系统性地梳理、分析和总结这些方法,提出智能数据可视分析的基本概念和关键技术框架.然后,在该框架下,综述和分析国内外面向可视分析的数据准备、智能数据可视化、高效可视分析和智能可视分析接口的研究进展.最后,展望智能数据可视分析的未来发展趋势.  相似文献   

6.
7.
智能数据分析在医学领域的应用综述   总被引:3,自引:0,他引:3  
回顾了智能数据分析方法在医学领域中的应用情况,目的是缩小医学领域数据采集和数据理解之间的鸿沟。介绍了智能数据分析技术的发展情况,提出了智能数据分析的分类方法,选择介绍了一些重要的智能数据分析方法及其在医学领域中的应用。  相似文献   

8.
Information Visualization Within a Digital Video Library   总被引:1,自引:0,他引:1  
The Informedia Digital Video Library contains over a thousand hours of video, consuming over a terabyte of disk space. This paper summarizes the multimedia abstractions used to represent this video in prior systems and introduces the visualization techniques employed to browse and navigate multiple video documents at once.  相似文献   

9.
地理空间数据可视分析综述   总被引:1,自引:0,他引:1  
地理空间数据通常是指用于描述自然现象和社会事件的发生及演变的空间位置、分布、关系、变化规律等方面的信息资料.随着获取渠道的多样化、采集过程的规范化以及采样粒度的精细化,地理空间数据普遍呈现属性描述多样化、特征分布时空化、结构关系层次化等特点,经典的统计分析软件和地理信息系统难以有效地发掘地理空间数据中隐含的复杂关系模式及结构特征.可视分析则是在有效地融合可视设计和数据挖掘模型的基础上,借助交互技术引导用户全面而细致地分析和探索地理空间数据中潜在的对象、过程、事件,以及所呈现的多维、时空、动态、关联等特征.因此,文中对面向地理空间数据可视分析的相关研究进行综述,首先从视觉元素映射的视角出发,介绍点、线、面、体等视觉元素在地理空间数据可视化过程中的设计与应用;其次对于地理空间数据的组织形式,分别概述具有显著多维、时空、层次等特点的地理空间数据的可视分析前沿技术和方法;进一步简述地理空间数据可视分析技术在自然环境、城市交通、人文经济等领域的拓展应用.在此基础上,对地理空间数据可视分析的未来发展趋势进行了展望.  相似文献   

10.
教育大数据可视化分析对于复杂教育规律的理解与挖掘具有重要作用,已成为当前教育信息科学研究领域的重要课题。首先归纳了教育大数据的典型特征,从促进学生元认知发展、辅助教师监督学习过程及提升管理者科学决策水平三个角度介绍了教育大数据应用的最新研究成果,并简述了利用教育大数据实施可视化分析的基本流程。然后重点对文本数据可视化、多维数据可视化、网络数据可视化、时间序列数据可视化以及地理空间数据可视化等五种主流的教育大数据可视化呈现方法进行特征描述,并给出具体的应用场景。随后介绍了动态查询与过滤技术、可缩放/变形界面技术和多视图联动技术三个实施教育大数据可视化的关键交互技术方法。最后依据最新研究动态,从多模态教育数据融合、人机交互、人机协同范式以及教育数据可视化设计的标准规范和评价体系四方面对教育大数据可视化未来研究方向进行了展望。  相似文献   

11.
The NYU Tvoc project applies the method of translation validation to verify that optimized code is semantically equivalent to the unoptimized code, by establishing, for each run of the optimizing compiler, a set of verification conditions (VCs) whose validity implies the correctness of the optimized run. The core of Tvoc is Tvoc-sp, that handles structure preserving optimizations, i.e., optimizations that do not alter the inner loop structures. The underlying proof rule, Val, on whose soundness Tvoc-sp is based, requires, among other things, to generating invariants at each “cutpoint” of the control graph of both source and target codes. The current implementation of Tvoc-sp employs somewhat naïve fix-point computations to obtain the invariants. In this paper, we propose an alternative method to compute invartiants which is based on simple data-flow analysis techniques.  相似文献   

12.
3S技术已成为地学数据获取、管理、可视化与分析的重要工具。川藏公路灾害信息系统综合了遥感技术、地理信息系统技术和数据库技术,在系统设计开发过程中妥善处理数据组织与维护、数据显示与分类、多专题信息提取、文档信息与地图信息紧密结合等重要环节,这些环节不只是川藏公路灾害信息系统中要解决的问题,也是诸多GIS应用软件的共同特征。川藏公路灾害信息系统的开发,实现了对川藏公路灾害信息管理与维护方便快捷、信息共享以及辅助研究人员对川藏公路沿线灾害进行研究与治理,并且可以将相关资料发布到网上供公众浏览。  相似文献   

13.
    
Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.  相似文献   

14.
In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multi-variate data. We discuss how different techniques take effect at specific stages of the visualization pipeline and how they apply to multi-variate data sets being composed of scalars, vectors and tensors. We also provide a categorization of these techniques with the aim for a better overview of related approaches. Based on this classification we highlight combinable and hybrid approaches and focus on techniques that potentially lead towards new directions in visualization research. In the second part of this paper we take a look at recent techniques that are useful for the visualization of complex data sets either because they are general purpose or because they can be adapted to specific problems.  相似文献   

15.
随着网络时代的迅速发展以及我国对数据信息可视化的深入研究,大数据的资源提供对于各个行业也变得越来越重要.尤其是对于股票数据的分析,如何更好地进行各股之间的相关性分析已经成为当今股票分析的重中只重.希望通过此次分析,可以为股票数据信息可视化技术的发展以及对于股票预测的训练提供一定的帮助.  相似文献   

16.
多维可视化技术综述   总被引:12,自引:1,他引:12  
当前,面对科学、工程和商业领域中海量的多维信息,用户迫切需要使用有效的可视化工具在知识发现、信息认知及信息决策过程中对其进行理解。首先对多维和多变元的概念进行了界定,给出了二者的适用领域,然后基于多维可视化技术的分类体系,详细阐述了各类技术代表方法的基本原理、适用性及功能作用,最后分析比较了各类技术方法的优缺点,展望了多维可视化技术未来的研究方向及其面临的挑战。  相似文献   

17.
数据维度相关性分析一直是数据分析领域的研究重点。传统的可视化方法可通过图形描述直观判断几个数据维度存在何种相关关系,但是难以解决维数灾难问题。一些数据挖掘方法虽然可行,但是难以把过程具象化,并且在一些应用场景下仍然需要可视化方法提供参数指导。提出了ASExplorer:一个探索高维数据维度相关性为目的的可视分析系统。该系统首先基于联合熵的维度重要性评价算法,帮助用户选择分析路径和过滤数据,然后基于以采样尺度为中心的交互探索方法,令用户可以同时探索多个数据维度在采样尺度变化时的关联关系。该系统适用于缺乏先验知识的数据集的早期分析过程,案例分析和用户研究验证了该系统的有效性。  相似文献   

18.
基于不确定性的相关关系和原始数据的相关关系作为2种相关关系具有一定的联系与区别.为了找出不确定性相关关系并有效地对比这2种关系的异同,发现可能产生额外不确定性的数据特征,提出了一套分析框架和交互系统.首先进行聚类分析,并根据聚类结果得到分组;然后根据不确定性定量方法计算得到的每组不确定性数据来构建不确定性数据集,并进行相关分析和对比分析;最后建立可视分析系统,帮助用户利用不确定性相关关系来筛选出可能产生额外不确定性的数据.使用2个差异较大的真实数据集,验证了文中框架和系统的可用性和有效性.  相似文献   

19.
数据可视化的研究与发展   总被引:23,自引:0,他引:23  
针对数据可视化是可视化技术在大型数据库的应用中提出的新的数据分析和处理技术,该文介绍了数据可视化的概念和发展状况,然后针对大型数据集介绍了几种数据可视化技术以及它们的代表方法,并对数据可视化和科学计算可视化进行了分析和比较,最后探讨了数据可视化技术的研究发展方向。  相似文献   

20.
理解数据整理脚本的语义是数据工作者的常见需求.然而,数据整理操作的类型及其代码的实现方式复杂多样,使得数据工作者在理解脚本语义时费时费力.通过收集数据工作者在理解数据整理脚本语义上的具体需求,设计并实现了一个基于概览和细节模式的交互式可视分析系统ChangeVis,帮助数据工作者理解表格在数据整理过程中的变化.ChangeVis包含概览视图,语义视图,统计视图和数据视图4个视图,分别可视化代码块中的表结构变化、行列信息变化、单元格数据变化以及执行的数据转换操作的语义.通过案例分析和用户实验,验证了ChangeVis系统在帮助数据工作者理解数据整理脚本语义的可用性和有效性.  相似文献   

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