视觉分析引领信息时代：问题解决之道

需积分: 8 81 浏览量更新于2024-07-18 收藏 24.03MB PDF 举报

《信息时代的主宰：视觉分析解决问题》一书由丹尼尔·基姆、乔恩·科霍尔默、杰弗里·埃利斯和弗洛里安·曼斯曼共同编撰，是 Eurographics协会出版的一部作品。该文档的核心主题集中在视觉分析（Visual Analytics）这一领域，强调了在数据爆炸的时代，如何通过直观、有效的数据分析方法来解决日益复杂的问题。书中详细探讨了数据管理与数据挖掘的重要性，尤其是在政策制定、公共服务等各个层面，海量数据的收集和存储已经成为了常态。随着大数据的兴起，如何有效地利用这些数据成为关键挑战。视觉分析作为一种强大的工具，它能够将复杂的数据转化为易理解和决策的可视化图表，帮助决策者快速洞察趋势、模式和关联性。内容涵盖了以下几个主要知识点： 1. **视觉分析基础**：介绍了视觉分析的基本原理，包括数据可视化技术、图表设计原则、用户界面设计等，这些都是提高数据分析效率的关键要素。 2. **数据管理策略**：讨论了如何有效地管理和组织大规模数据，包括数据清洗、预处理、存储和索引优化等技术，确保数据的质量和可用性。 3. **数据挖掘实践**：书中深入浅出地讲解了数据挖掘的方法，如分类、聚类、关联规则挖掘等，以及如何将这些技术应用于实际问题中。 4. **案例研究与应用示例**：提供了多个实际场景下的视觉分析应用案例，展示了视觉分析在政府决策、商业智能、科学研究等多个领域的实际效果。 5. **伦理与社会影响**：讨论了在利用大数据进行分析时可能涉及的隐私保护、公平性和决策透明度等问题，提醒读者关注数据使用的道德责任。 6. **未来发展趋势**：书中还对未来视觉分析技术的发展趋势进行了预测，包括人工智能、云计算和实时分析等技术如何推动视觉分析的进步。 7. **纪念 Jim Thomas**：作为视觉分析领域的先驱，Jim Thomas的逝世为本书增添了感人的哀思，他的贡献被铭记，对视觉分析的推动也体现在书中的每一处。《信息时代的主宰：视觉分析解决问题》是一本实用且具有深度的指南，为读者提供了理解和掌握视觉分析技术，以应对信息时代挑战的全面教程。无论是专业分析师还是跨学科的研究人员，都能从中受益匪浅。

4 Introduction

monitored and guided the algorithm when appropriate. The Cockpit interface

supported this activity by showing, dynamically, hierarchically related and

meaningful indications of chip performance and sensitivity information, as well

as on-the-ﬂy advice by an artiﬁcial intelligence system, all of which information

could be managed to interactively.

1.3 Overview

This book is the result of a community eﬀort of the partners of the VisMaster

Coordinated Action funded by the European Union. The overarching aim of

this project was to create a research roadmap that outlines the current state of

visual analytics across many disciplines, and to describe the next steps to take

in order to form a strong visual analytics community, enabling the development

of advanced visual analytic applications. The ﬁrst two chapters introduce the

problem space and deﬁne visual analytics. Chapters 3 to 8 present the work of

the specialised working groups within the VisMaster consortium. Each of these

chapters follow a similar structure – the motivation section gives an outline

of the problem and relevant background information; the next section presents

an overview of the state of the art in the particular domain, with reference to

visual analytics; challenges and opportunities are then identiﬁed; and ﬁnally

in the next steps section, suggestions, pertinent to the subject of the chapter,

are put forward for discussion. Higher level recommendations for the direction

for future research in visual analytics, put forward by the chapter authors are

collectively summarised in the ﬁnal chapter. We now outline the chapters in

more detail.

Chapter 2 describes some application areas for visual analytics and putsDaniel A. Keim

Jörn Kohlhammer

Florian Mansmann

Thorsten May

Franz Wanner

the size of the problem into context, and elaborates on the deﬁnition of

visual analytics. The interdisciplinary nature of this area is demonstrated

by considering the scientiﬁc ﬁelds that are an integral part of visual analyt-

ics.

Chapter 3 reviews the ﬁeld of data management with respect to visual analyticsGiuseppe Santucci

Helwig Hauser

and reviews current database technology. It then summarises the problems

that can arise when dealing with large, complex and heterogeneous datasets

or data streams. A scenario is given, which illustrates tight integration of

data management and visual analytics. The state of the art section also

considers techniques for the integration of data and issues relating to data

reduction, including visual data reduction techniques and the related topic

of visual quality metrics. The challenges section identiﬁes important issues,

such as dealing with uncertainties in the data and the integrity of the results,

the management of semantics (i.e., data which adds meaning to the data

values), the emerging area of data streaming, interactive visualisation of large

databases and database issues concerning distributed and collaborative visual

analytics.

1.3 Overview 5

Chapter 4 considers data mining, which is seen as fundamental to the automated Kai Puolamäki

Alessio Bertone

Roberto Therón

Otto Huisman

Jimmy Johansson

Silvia Miksch

Panagiotis Papapetrou

Salvo Rinzivillo

analysis components of visual analytics. Since today’s datasets are often

extremely large and complex, the combination of human and automatic analysis

is key to solving many information gathering tasks. Some case studies are

presented which illustrate the use of knowledge discovery and data mining

(KDD) in bioinformatics and climate change. The authors then pose the

question of whether industry is ready for visual analytics, citing examples of

the pharmaceutical, software and marketing industries. The state of the art

section gives a comprehensive review of data mining/analysis tools such as

statistical and mathematical tools, visual data mining tools, Web tools and

packages. Some current data mining/visual analytics approaches are then

described with examples from the bioinformatics and graph visualisation ﬁelds.

Technical challenges speciﬁc to data mining are described such as achieving

data cleaning, integration, data fusion etc. in real-time and providing the

necessary infrastructure to support data mining. The challenge of integrating

the human into the data process to go towards a visual analytics approach is

discussed together with issues regarding its evaluation. Several opportunities

are then identiﬁed, such as the need for generic tools and methods, visualisation

of models and collaboration between the KDD and visualisation communi-

ties.

Chapter 5 describes the requirements of visual analytics for spatio-temporal Gennady Andrienko

Natalia Andrienko

Heidrun Schumann

Christian Tominski

Urska Demsar

Doris Dransch

Jason Dykes

Sara Fabrikan

Mikael Jern

Menno-Jan Kraak

applications. Space (as in for example maps) and time (values change over

time) are essential components of many data analysis problems; hence there is

a strong need for visual analytics tools speciﬁcally designed to deal with the par-

ticular characteristics of these dimensions. Using a sizeable ﬁctitious scenario,

the authors guide the reader towards the speciﬁcs of time and space, illustrating

the involvement of various people and agencies, and the many dependencies

and problems associated with scale and uncertainties in the data. The current

state of the art is described with a review of maps, geographic information

systems, the representation of time, interactive and collaborative issues, and the

implication of dealing with massive datasets. Challenges are then identiﬁed,

such as dealing with diverse data at multiple scales, and supporting a varied set

of users, including non-experts.

Chapter 6 highlights the fact that currently most visual analytics application Jean-Daniel Fekete

are custom-built stand-alone applications, using for instance, in-memory data

storage rather than database management systems. In addition, many other

common components of visual analytics applications can be identiﬁed and po-

tentially built into a unifying framework to support a range of applications. The

author of this chapter reviews architectural models of visualisation, data man-

agement, analysis, dissemination and communication components and outlines

the inherent challenges. Opportunities and next steps for current research are

subsequently identiﬁed which encourage a collaborative multidisciplinary eﬀort

to provide a much needed ﬂexible infrastructure.

Chapter 7 discusses visual perception and cognitive issues - human aspects Alan Dix

Margit Pohl

Geoﬀrey Ellis

of visual analytics. Following a review of the psychology of perception

and cognition, distributed cognition, problem solving, particular interaction

issues, the authors suggest that we can learn much from early application

2 Visual Analytics

Visual analytics is not easy to deﬁne, due to its multi-disciplinary nature

involving multiple processes and the wide variety of application areas. An

early deﬁnition was "the science of analytical reasoning facilitated by inter-

active human-machine interfaces"

[125]

. However, based on current practice,

a more speciﬁc deﬁnition would be: "Visual analytics combines automated Visual analytics combines

automated analysis with

interactive visualisations

analysis techniques with interactive visualisations for an eﬀective understand-

ing, reasoning and decision making on the basis of very large and complex

datasets".

So, in terms of the goal of visual analytics, we can elaborate on this deﬁnition

to state that visual analytics is the creation of tools and techniques to enable

people to:

- Synthesise information and derive insight from massive, dynamic, ambigu-

ous, and often conﬂicting data.

- Detect the expected and discover the unexpected.

- Provide timely, defensible, and understandable assessments.

- Communicate these assessment eﬀectively for action.

In Section 2.2 we will look at how visual analytics strives to achieves these

goals in terms of the high-level processes required to generate knowledge from

data, and then in Section 2.3 in terms of the many scientiﬁc disciplines that

contribute to visual analytics. But ﬁrstly, in order to give a sense of the social

and economic importance of visual analytics, as well as the scale of the data

being dealt with, we will look at some typical uses.

2.1 Application of Visual Analytics

Visual analytics is essential in application areas where large information spaces

have to be processed and analysed. Major application ﬁelds are physics

and astronomy. For example, the discipline of astrophysics oﬀers many

opportunities for visual analytics techniques: massive volumes of unstructured

data, originating from diﬀerent directions of space and covering the whole

frequency spectrum, from continuous streams of terabytes of data that can be

recorded and analysed. With common data analysis techniques, astronomers

can separate relevant data from noise, analyse similarities or complex patterns,

and gain useful knowledge about the universe, but the visual analytics approach

can signiﬁcantly support the process of identifying unexpected phenomena Monitoring the climate

involves huge amounts of

data from many diﬀerent

sources

inside the massive and dynamic data streams that would otherwise not be found

by standard algorithmic means. Monitoring climate and weather is also a

domain which involves huge amounts of data collected by sensors throughout

the world and from satellites, in short time intervals. A visual approach can help

8 Visual Analytics

to interpret these massive amounts of data and to gain insight into the dependen-

cies of climate factors and climate change scenarios that would otherwise not

be easily identiﬁed. Besides weather forecasts, existing applications visualise

global warming, melting of the poles, the stratospheric ozone depletion, as well

as hurricane and tsunami warnings.

Figure 2.1: Visual analytics in action: Visual support for the simulation of

climate models provided by CGV

[113]

(Coordinated Graph Visual-

ization), a highly interactive graph visualisation system. To support

diﬀerent visualisation tasks, view ensembles can be created dynam-

ically with the help of a ﬂexible docking framework. CGV includes

enhanced dynamic ﬁltering, graph lenses, edge-based navigation, in

addition to augmented navigation with inﬁnite grid and radar view.

Data source: Potsdam Institute for Climate Impact Research

In the domain of emergency management, visual analytics can help determineMore than 210 billion

emails, 4 billion SMS and

50 million tweets per day

the on-going progress of an emergency and identify the next countermeasures

(e.g., construction of physical countermeasures or evacuation of the population)

that must be taken to limit the damage. Such scenarios can include natural

or meteorological catastrophes like ﬂood or waves, volcanoes, storm, ﬁre

or epidemic growth of diseases (e.g. N1H1 virus), but also human-made

technological catastrophes like industrial accidents, transport accidents or pol-

lution. Visual analytics for security and geo-graphics is an important research

topic. The application ﬁeld in this sector is wide, ranging from terrorism

informatics, border protection, path detection to network security. Visual

analytics supports investigation and detection of similarities and anomalies in

very large datasets. For example, on a worldwide scale, per day there are

upwards of 210 billion emails, 4 billion SMS messages, 90 million tweets

and the number of IP data packets exceeds 9000 billion. As an example

剩余181页未读，继续阅读

cuidiwhere

粉丝: 131
资源: 4

视觉分析引领信息时代：问题解决之道

Mastering Visual Studio 2017 epub

Mastering Visual Studio 2017

兴顺物流管理系统 JAVA毕业设计 源码+数据库+论文+启动教程（SpringBoot+Vue.JS）.zip

技术资料分享SN75176B电子技术资料.zip

基于Android Studio开发的选课系统源代码

arcgis符号库整理

西南交通大学-数据结构实验报告-关键路径

电路基础-nidmsrv软件

小程序基于ssm 购物系统设计与实现

技术资料分享PDIUSBD12-cn电子技术资料.zip

最新资源

兴顺物流管理系统 JAVA毕业设计源码+数据库+论文+启动教程（SpringBoot+Vue.JS）.zip