DELINEATING CANCER GENOMICS

THROUGH DATA VISUALIZATION

Project report submitted

in partial fulfilment of the requirement for the degree of

Bachelor of Design

By

Linu George (111020516)

Rupam Das (11020529)

Under the supervision of

Dr. Prasad Bokil

DEPARTMENT OF DESIGN

INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI

(July 2014 - November 2014)

Approval Sheet

This project report entitled "Delineating cancer genomics through Data Visualization" by Linu

George (11020516) and Rupam Das (11020529) is approved for the degree of Bachelor of

Design.

Examiners

____________________

____________________

____________________

Supervisor(s)

____________________

____________________

____________________

Chairman

____________________

Date: 10th November, 2014

Place: IIT Guwahati

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Declaration

We declare that this written submission represents our ideas in our own words and where others'

ideas or words have been included, we have adequately cited and referenced the original. We

also declare that we have adhered to all principles of academic honesty and integrity and have

not misrepresented or fabricated or falsified any idea/data/fact/source in our submission. We

understand that any violation of the above will be cause for disciplinary action by the institute

and can also evoke penal action from the sources which have thus not been properly cited or

from whom proper permission has not been taken when needed.

______________________ __________________

Linu George Rupam Das

11020506 11020529

Date: 10th November, 2014

iii

Certificate

It is certified that the work contained in the project report titled " Delineating cancer genomics

through Data Visualization " by Linu George (11020506) and Rupam Das (11020529), has been

carried out under my supervision and that this work has not been submitted elsewhere for a

degree.

________________________________

Dr Prasad Bokil

Asst. Professor

Department of Design

Indian Institute of Technology

Novemeber, 2014

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Acknowledgement

First and foremost, we are extremely thankful to our project supervisor Dr Prasad Bokil for his

invaluable support and guidance which made our project work productive, stimulating and

enjoyable. We feel honoured to have worked with him, and owe a great debt of gratitude for

his patience and inspiration.

We are grateful to the Department of Design, IIT Guwahati, for providing us with a rich learning

and working environment, without which many of our ideas probably would have not come to

fruition.

Last, but never the least, we would like to thank our family members who have been a constant

source of motivation for everything good that we have attempted to do so far in life.

______________________ _____________________

Linu George Rupam Das

11020516 11020529

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ABSTRACT

In spite in advances in technologies for working with data, people spend undue amount of time

in understanding the data and manipulating it into holistic visualization. Data visualization

software for complex dataset such as in cancer genomics (which we have taken as case study)

are not able to provide effective visualization for the users. Identification and characterization

of cancer detection are important areas of research that are based on the integrated analysis of

multiple heterogeneous genomics datasets. In this report, we review the key issues and

challenges associated with cancer genomics through exploration of data visualization

techniques, interactions and methods, which will in-turn advance the state of the art.

1. INTRODUCTION

Data visualization is a concept that is used for the representation of raw and crude form of

information in visual form which helps the user to perceive as well as understand the given

scenario. We proceeded with a very basic of the topic and tried to explore each and every

possibilities of the domain of visualization. As we were going through the references and

sources of our project we found out there is lack of coherence between the data that being

presented and the data that is being showcased. The raw cancer data that has been provided to

us was in a much disorganized manner, which made it very tough to decipher meaningful

visualization out of that datasets.

The advent of high-throughput technologies have given rise to fruitful research on large-scale

genomic data analysis. Since genomic data is usually very large and complex, visualization

tools are always essential for data examination and interpretation [12]. As we know that the

amount of bio-medical data available on the Web grows exponentially with time. The resulting

large volume of data makes manual exploration very tedious. Moreover, the velocity at which

this data changes and the variety of formats in which bio-medical data is published and

documented in the Web makes it difficult to access them in an integrated form. Finally, the lack

of an integrated vocabulary makes querying more difficult [13].

The data is the key component of the visualization and it plays a large role in determining the

effectiveness of the visualization tool. This large set of unorganized clattered data lead to

accumulation of huge chunk of information which should be dealt with the elegant techniques

of visual graphics and visualization. This can help in comprehension of huge amounts of data

into a well-organized form by inducing the user to think about the data and encourage the eye

to compare different pieces of data.

To overcome this problem we went through many of the resources that are already there for

these researches and found out similarities and dissimilarities, which led to the documentation

of large set of trends and possibilities which has been documented in our later chapters.

1.1 Objectives of the project

1. To find out the recent trends in the domain of data visualization.

2. Use these knowledge to brainstorm various possibilities in the domain of cancer.

3. Explore new visualization mediums to depict the same information.

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4. Generate concept low fidelity prototypes for that cancer data set.

5. Explain the scope of work that can be done in later stages.

1.2 Need of data visualization

Visualization is the graphical presentation of information, with the goal of providing the viewer

with a qualitative understanding of the information contents. It is also the process of

transforming objects, concepts, and numbers into a form that is visible to the human eyes. When

we say “information”, we may refer to data, processes, relations, or concepts.

It’s also about understanding ratios and relationships among numbers. Not about understanding

individual numbers, but about understanding the patterns, trends, and relationships that exist in

groups of numbers.

To see and understand pictures is one of the natural instincts of human, and to understand

numerical data it takes years’ worth of training from schools, and even so, a lot of people are

still not good with numerical data. Data visualization shifts the load from numerical reasoning

to visual reasoning. Getting information from pictures is far more time-saving than looking

through text and numbers – that’s why many decision makers would rather have information

presented to them in graphical form, as opposed to a written or textual form [1].

As we go through various forms of visualization we also discovered that data visualization is

not scientific visualization. Scientific visualization uses animation, simulation, and

sophisticated computer graphics to create visual models of structures and processed that cannot

otherwise be seen, or seen in sufficient detail [1].

While data visualization is a way of communicating and presenting the display presentation of

the given set of data is a way that helps to minimize the cognitive load of that human who is

trying to understand data. The cognitive mind of the humans always tries to create pattern and

way out to appropriately recognize, interpret, select and associate the right kind data in a short

period of time from the given dataset.

2. LITERATURE REVIEW

2.1 Data visualisation: An overview

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• In spite of advances in technologies for working with data, analysts still spend an

inordinate amount of time

• The diagnosing data quality issues and manipulating data into a usable form.

• This process of ‘data wrangling’ often constitutes the most tedious and time-consuming

aspect of analysis.

• Though data cleaning and integration are longstanding issues in the database

community, relatively little research has explored how interactive visualization can

advance the state of the art.

• Data visualization is a quite new and promising field in computer science and uses

computer graphic effects to reveal the patterns, trends, relationships out of datasets.

2.2 Background

The history of visualization was shaped to some extent by available technology and by the

pressing needs of the time, they include: primitive paintings on clays, maps on walls,

photographs, table of numbers (with rows and columns concepts), these are all some kind of

data visualization – although we may not call them under this name at that time. These

eventually led to new opportunities for the analysis and communication of data using

visualization. The current scenario is very encouraging which helps us to use and practice

various possibilities of desktop screens, mouse, and keyboard-based systems that are making

them increasingly attractive [2]. Talking about graphics, we should remind what is called

graphical entities and attributes. They are the following variables which decides what data

should be categorize to which part [1]:

Entity: point, line(curve), polyline, glyph, surface, solid, image, text

Attribute: color/intensity, location, style, size, relative position/motion

Numeric, symbolic (or mix): 123, or @

Scalar, vector, or complex structure:

Various units: meters, inch.

Discrete or continuous: 1, 2, 3, or p

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Spatial, quantity, category, temporal, relational, structural

Accurate or approximate

Dense or space

Ordered or non-ordered

Disjoint or overlapping

Binary, enumerated, multilevel

Independent or dependent

Multidimensional, etc.

We consider the data is properly visualized, if the visualization is [6]:

Effective: viewers can interpret it easily.

Accurate: sufficient for correct quantitative evaluation.

Aesthetics: must not offend viewer's senses

Adaptable: can adjust to serve multiple needs

2.3 Data visualization: A Case Study

Ranjit Kumar is a Senior Biologist in SAP Labs with a PhD degree in Bioinformatics

Engineering. He is 32 years old and Married with two children. His is focused, goal-oriented

person with very good concept understanding skills.

One of his concerns is analysis and collection of legible data from the information provided to

them in the software. He spends time by performing experimental tests for proper functioning

of data visualizing platform. He also helps in reviewing and testing of the built software, along

with doing literature review and compiling of prerequisite of the software. While doing the full

length reviews of the software that he uses he often faces the problem of data inconsistencies

which makes it rather inconvenient and tedious for him to manipulate and integrate all the data

into one common data model. As we all know that cancer data is very huge and can be of

enormous size, one can’t expect a data scientist to wrangle all the data on his own and make

amends on the data. During the use of the existing software there can be time where he can have

an error in judgment which may result to resetting all the previous data and come again from

the start.

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Fig: Sample cancer data

His workplace is connected with round the clock Wi-Fi and is equipped with a workstation. He

works in the laboratory and performs various experiment in cancer research. He works 12 hrs.

a day and keeps tab of each and every activity and task that is happening on the software. He

has project where he has to compare a set of DNA with other set that is coded in the software.

He feeds the input and sets all the parameters carefully to get the required data. He closely

observes and takes note of each and every step of the process. The data taken from respective

step will help him to prove his stated hypothesis. Further he can use the large data set for the

softwares benefit. He can make use of the information for improvement and innovating in

software.

The required solution for the data scientist can be a software which can be interactive and fast

so that it doesn’t kill his time while he is inputting the data to the system. Also the cognitive

load of the interaction in the software should be less so there would be less possibility of error.

They should also have the capability of sensing the data format and make the data editable as

well as per the common data formats. All in all the robustness of that particular software will

be key to the amount of output that can come up from that biologist.

This above depicts the challenges and their way of solving of the people who is going to use

the whole tool that would be later made by us. It helps in distinguish among the needs,

motivation and various other use cues of the person who will be using our platform.

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2.4 Brief history of data visualization

Statistical graphics and data visualization is a relatively modern developments in statistics with

graphic portrayal of quantitative information has been going on from the beginning. Map-

making and visual depiction were very much done during the early 17th centuries when the first

roots of data visualization was sowed. With that thematic cartography, statistics and statistical

graphics, with applications and innovations in many fields of medicine and science that are

often intertwined with each other came up along the way in this brief history of visualized data.

Nowadays variety of advancements contributed to the widespread use of data visualization

which resulted in technologies for drawing and reproducing images, advances in mathematics

and statistics, and new developments in data collection, empirical observation and recording.

Pre-17th Century: Early maps and diagrams

The earliest seeds of visualization arose in geometric diagrams, in tables of the positions of

stars and other celestial bodies, and in the making of maps to aid in navigation and

exploration. The idea of coordinates was used by ancient Egyptian surveyors in laying out

towns, earthly and heavenly positions were located by something akin to latitude and

longitude at least by 200 BC, and the map projection of a spherical earth into latitude and

longitude by Claudius Ptolemy [c. 85–c. 165] in Alexandria would serve as reference

standards until the 14th century [3].

1600-1699: Measurement and theory

During the 17th century the problems were concerned with physical measurement— of time,

distance, and space— for astronomy, surveying, map making, navigation and territorial

Fig: Circos data visualization. Circos is a

software package for visualizing data and

information. It visualizes data in a

circular layout — this makes Circos ideal

for exploring relationships between

objects or positions. Data acquisition,

analysis and reporting pipelines can be

done on it. [4]

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expansion. In this century we also saw great new growth in theory and growth of practical

application— the rise of analytic geometry and coordinate systems and various other system

and theories of errors of measurement and estimation. It also saw the birth of probability theory

and the beginning of demographic statistics which includes the study of population, land, taxes,

value of goods, etc. for the purpose of understanding the wealth of the state.

1700-1799: New graphic forms

With the increased interest and importance of data, and the idea of graphic representation

established, the 18th century witnessed the expansion of these aspects to new domains and new

graphic forms. In cartography, map-makers began to try to show more than just geographical

position on a map. As a result, new data representations were invented, and thematic mapping

of physical quantities took root. Towards the end of this century, the first attempts at the

thematic mapping of geologic, economic, and medical data were being made [3]. During that

time only there were widespread use of abstract graphs, and graphs of functions with the help

of statistical theory (measurement error) and systematic collection of empirical data.

1800-1850: Beginnings of modern graphics

With the advent of previous innovation on design and techniques of statistical graphics and

thematic mapping was becoming more and more popular. In statistical graphics, all of the

modern forms of data display were invented: bar and pie charts, histograms, line graphs and

time-series plots, contour plots, scatterplots came up during that time. In the same way the

thematic cartography led to mapping progressed from single maps to comprehensive atlases,

depicting data on a wide variety of topics and also introduced a wide range of novel forms of

symbolism. During this period graphical analysis of natural and physical phenomena (lines of

magnetism, weather, tides, etc.) began to appear regularly in scientific publications as well [3].

1850–1900: The Golden Age of statistical graphics

By the mid1800s the whole of Europe woke up on the call of rapid growth of visualization and

data graphics. This led to establishment of state statistical offices were established throughout

the region in recognition of the growing importance of numerical information for social

planning, industrialization, commerce, and transportation.

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2.5 State of the Art

There is various software in the internet from where you can download and make use of that to

create your visualization by just inputting the required data. They can be listed as follows:

Visual.ly

It has combined gallery and infographic generation tool. It offers a simple toolset for building

data representations, as well as a platform to share your creations. This goes beyond pure data

visualisation, but if you want to create something that stands on its own.

Fig 17: A Visual.ly chart

jQuery Visualize

It is developed by team of JQuery's ThemeRoller and jQuery UI websites, jQuery Visualize

Plugin is an open source charting plugin for jQuery that uses HTML Canvas to draw a number

of different chart types.

Fig 17: A jQuery Visualize chart

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Dygraphs

Dygraphs is a fast, flexible open source JavaScript charting library that allows users to explore

and interpret dense data sets. It's highly customizable, works in all major browsers, and you can

even pinch to zoom on mobile and tablet devices.

Fig 18: A Dygraphs chart

Timeline

Timeline is a fantastic widget which renders a beautiful interactive timeline that responds to the

user's mouse, making it easy to create advanced timelines that convey a lot of information in a

compressed space. Each element can be clicked to reveal more in-depth information, making

this a great way to give a big-picture view while still providing full detail.

Fig 19: A Timeline chart

Exhibit

Developed by MIT, and fully open-source, Exhibit makes it easy to create interactive maps,

and other data-based visualizations that are orientated towards teaching or static/historical

based data sets, such as flags pinned to countries, or birth-places of famous people.

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Fig 17: An Exhibit chart

Modest Maps

Modest Maps is a lightweight, simple mapping tool for web designers that makes it easy to

integrate and develop interactive maps within your site, using them as a data visualization

tool.

Fig 18: A Modest map

Leaflet

Leaflet makes it easy to use OpenStreetMap data and integrate fully interactive data

visualisation in an HTML5/CSS3 wrapper. The core library itself is very small, but there are a

wide range of plugins available that extend the functionality with specialist functionality such

as animated markers, masks and heatmaps.

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Fig 19: A Leaflet chart

WolframAlpha

WolframAlpha is really good at intelligently displaying charts in response to data queries

without the need for any configuration. It uses publically available data, this offers a simple

widget builder to make it really simple to get visualizations on your site.

Fig 20: A WolframAlpha chart

Dipity

Dipity allows you to create rich interactive timelines and embed them on your website. It offers

a free version and a premium product, with the usual restrictions and limitations present. The

timelines it outputs are beautiful and fully customisable, and are very easy to embed directly

into your page.

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Fig 21: A Dipity chart

Many Eyes

Developed by IBM, Many Eyes allows you to quickly build visualizations from publically

available or uploaded data sets, and features a wide range of analysis types including the

ability to scan text for keyword density and saturation

Fig 22: A Many Eyes chart

2. OBSERVATIONS AND RESULTS

Data visualization and information visualization are useful for referring to any visual

representation of data. This data that we are talking about can be algorithmically drawn (with

custom touches but is largely rendered with the help of computerized methods) and can be easy

to regenerate with different data (the same form may be repurposed to represent different

datasets with similar dimensions or characteristics).In addition these are often aesthetically

barren (data is not decorated) and are relatively data-rich (large volumes of data are welcome

and viable, in contrast to infographics).

Data can be categorized into three types; Nominal; Ordinal and quantitative. Nominal data are

the one with no order and value they are just distinct labels in set, for example gender (male

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and female). Ordinal are data which may or mayn’t have value but they occur in a definite

order/sequence or pattern, for example days of a week (mon-sun). And lastly quantitative data

are the one with no order or patterns but have only its numerical value with them, for example

physical measurements like temperature.

As we go on further into the depth of data visualization, according to B. Shneiderman data can

be divided into various information taxonomy like as follows:

1D linear data: Sequences of some other data element (characters, numbers, documents,

words, etc.)

2D map data: Data with a genuine sense of “area,” such as maps, floor plans, and layouts

3D world data: Data that has “volume,” typically real world objects and structures; the

third dimension adds new challenges regarding navigation and rendering.

Multidimensional data: Data with more than 3 dimensions, such as relational or

statistical databases where attributes can be thought of as axes in an n-dimensional

space; typically uses multiple “layers” of 2D or 3D views like Fisher’s Iris data set [14].

Temporal data: A special case of 1D linear data, where the sequence of elements is

based on time; having start and end times influences the visualization approach

Tree data: Data with a hierarchical, recursive structure, with elements (except the root)

having exactly one parent; visualization approaches include outlines, 3D views, and

space-filling treemaps like Linnean taxonomies [15], phylogenies[16].

Network data: Any graph-structured data, presenting challenges in terms of layout and

traversal; going domain specific may help refine visualization choices like metabolic

pathways.

Data Visualization Techniques

Choropleth map

It is a thematic map in which areas are shaded or patterned in proportion to the measurement of

the statistical variable being displayed on the map, such as population density or per-capita

income. The choropleth map provides an easy way to visualize how a measurement varies

across a geographic area or it shows the level of variability within a region. When mapping

quantitative data, a specific colour progression is used to depict the data. Generally, single-hue

progressions fade from a dark shade of the chosen colour to a very light or white shade of

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relatively the same hue. Where the darkest hue represents the greatest number in the data set

and the lightest shade representing the least number.[5]

. Fig 3: A choropleth map

Cartogram map

A cartogram is a map in which some thematic mapping variable – such as travel time,

population is substituted for land area or distance. A cartogram illustrates the relative sizes of

the populations of the countries of the world by scaling the area of each country in proportion

to its population; the shape and relative location of each country is retained to as large an extent

as possible. Here especially the geometry or space of the map is distorted in order to convey

the information of this alternate variable.

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Fig 4: Area cartogram of the United States

Dot distribution map

It is as a map type that uses a dot symbol to show the presence of a feature or phenomenon

where they rely on the visual scatter to show the spatial pattern. In a one-to-one dot map, each

dot represents one single recording of a phenomenon where care must be taken to ensure that

the dot is represented in its correct spatial location. In a one-to-many, each dot on the map

represents more than one of the phenomena being mapped where the number of data represented

by each dot may be determined by the product of data availability. In this form of graph the

dots represent aggregate data and are often arbitrarily placed on a map.

Fig 5: Dot map showing clusters of cholera cases in the London epidemic of 1854

Timeline

A timeline is a way of displaying a list of events in chronological order, they can take use any

time scale, depending on the subject and data. This time scale is dependent on the events in the

timeline. While most timelines use a linear timescale, for very large or small timespans,

logarithmic timelines use a logarithmic scale to depict time [6]. There are different types of

timelines

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Text timelines, labeled as text

Number timelines, the labels are numbers, commonly line graphs

Interactive, clickable, zoomable timelines

Fig 5: A New Chart of History, 1765 by Joseph Priestley

Polar area diagram

The polar area diagram is similar to a usual pie chart, except sectors are equal angles and differ

rather in how far each sector extends from the centre of the circle. The polar area diagram is

used to plot cyclic phenomena (e.g., count of deaths by month).

Fig 6: Diagram of the causes of mortality in the army in the East by Florence Nightingale.

Pie Chart

A pie chart (or a circle chart) is a circular statistical graphic, which is divided into sectors to

illustrate numerical proportion where each of the arcs length is equal to the proportion to the

quantity it represent. Pie charts cannot show more than a few values without separating the

slices from the data they represent. When slices become too small, pie charts have to rely on

colours, textures or arrows which makes the reader difficult to understand them.

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Fig 7: Pie chart of populations of English native speakers

Histogram

A histogram is a graphical representation of the distribution of data where we take the range of

values -- that is, divide the entire range of values into a series of small intervals -- and then

count how many values fall into each interval.

Fig 8: A histogram

Alluvial diagrams

They are a type of flow diagram to represent changes in network structure over time that can

be used to visualize any type of change in group composition between states or over time and

include statistical information to reveal significant change. They make highlight important

structural changes that can be further emphasized by colour, and make identification of major

transitions easy.

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Fig 9: Diagram illustrates how the scientific study on neuroscience.

Tag cloud

They are visual representation for text data, typically used to depict keyword metadata (tags)

on websites, or to visualize free form text. Typically they are usually single words, and the

importance of each tag is shown with font size or colour.

Fig 20: Population of each of the world's countries

Treemapping

Treemapping is a method for displaying hierarchical data by using nested rectangles. Each

branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing

sub-branches. A leaf node's rectangle has an area proportional to a specified dimension on the

data.

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Fig 11: Drink preference in a small group of people

Line Charts

A line chart or line graph is a type of chart which displays information as a series of data

points called 'points/markers' connected by straight line segments. Line Charts show how a

particular data changes at equal intervals of time. A line chart is often used to visualize a trend

in data over intervals of time – a time series – thus the line is often drawn chronologically.

Fig 12: Data over intervals with connected points

Scatter Plot

A scatter plot, scatterplot, or scattergraph to display values for two variables for a set of data.

Data is displayed as collection of points, each having the value of one variable determining the

position on the horizontal axis and the value of the other variable determining the position on

the vertical axis.

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Fig 13: A Scatter Chart

Bubble Chart

A bubble chart is a type of chart that displays three dimensions of data. Each entity with its

triplet (v1, v2, v3) of associated data is plotted as a disk that expresses two of the vi values

through the disk's xy location and the third through its size.

Fig 14: Bubble chart show the relationship between three variables

Area chart

An area chart or area graph displays graphically quantitive data. An area chart or area graph

displays graphically quantitive data where more than one quantities are compared with each

other. The area chart is like the plot chart except that the area below the plotted line is filled in

with colour to indicate volume.

Fig 14: An Area plot

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Heat map

A heat map is a graphical representation of data where the individual values contained in a

matrix are represented as colors. It’s a bit similar to the chloropleth map where the intensity of

color gives the quantity of that data distribution, but are confided in a Cartesian coordinate

system.

Fig 15: A Heat map

Radar Chart

A radar chart is a graphical method of displaying multivariate data in the form of a two-

dimensional chart of three or more quantitative variables represented on axes starting from the

same point. The relative position and angle of the axes is typically uninformative.

Fig 16: Star plot from NASA

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Mapping data

Fig: Encoding Schemes [17]

Fig: Mapping data types to encoding [17]

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Basic Task Involved

Overview: This task extract the overall picture of the dataset.

Zoom: This task looks “closer” at some data subset, typically after the overview task

has determined specific areas of interest.

Filter: This task reduces the current amount of visible data, typically based on a query

or predicate.

Details-on-demand: Individual data items may have additional (extensive) properties or

content; the user/ viewer will want easy access to this upon request.

Relate: A task that truly leverages visual displays, relating items or groups of items have

a broad range of variations and choices, including proximity, containment, connectors,

colors, and highlighting.

History: An overall information visualization “session” typically involves a sequence

of task instances; users may want to review this sequence, allowing backtracking or a

review of what has been done so far.

Extract: Once a user has found a suitable subset (and presentation), he or she may wish

to share or send it, in a separate, distinct form or package

Interactions based on the basic tasks

Re-orderable matrices - sorting

Brushing - browsing

Linked views – comparison, correlation, different perspectives

Linking

Overview and detail -

Eccentric labelling

Zooming – dealing with complexity/amount of data

Focus & context - dealing with complexity/amount of data

Fisheye….

Hyperbolic

Animated transitions - keeping context

Dynamic queries – exploring

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Challenges

Information visualization remains a wide-open field with a number of existing challenges:

Importing data: Inputting the data into the given software is very tedious process of

match making and accessibility.

Visual vs. textual — Visual representations don’t obviate the need for labels; the idea

is to bring them together in a way that plays to their respective strengths

Related data — Users/viewers may want to jump across data sets, if those sets are

available in a compatible form.

Large volumes — Data are now available on the order of millions or billions, presenting

challenges of scale in terms of technology and approach; note that, given the right

presentation, human perception can still gain a decent understanding of such large data

sets.

Data mining — Automatically finding patterns or trends was once viewed as a distinct

area, but is now seen as complementary to information visualization, since the right

display can highlight such patterns and trends.

Collaboration — There is frequently need to perceive data sets in groups or teams, so

that element needs to factor in.

Universal usability — Visualization approaches might lock out certain users, such as

the visually impaired or the colorblind, or carry cultural or economic assumptions

A Probable Proposal

Visualization of multi-dimensional data is a challenging task. The goal is not the display of

multiple data dimensions, but user comprehension of the multi-dimensional data. A glyph-

based system allows the visualization of both regular and irregular grids of volumetric data. A

glyph's location, 3D size, color, and opacity encode up to 8 attributes of scalar data per glyph.

Glyph, or iconic, visualization is an attempt to encode more information in a comprehensible

format, allowing multiple values to be encoded in the parameters of the glyphs [18]. The shape,

color, transparency, orientation, etc., of the glyph can be used to visualize data values. Glyph

rendering is an extension to the use of glyphs and icons in numerous fields, including

cartography, logic, and pictorial information systems [19].

We are thinking of incorporating three dimensional shape attributes (e.g 3D n sided polygon)

for various stages of our visualization component which can be controlled through customizable

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modularities by the user to make it simple, flexible and elegant. This n-dimensional polygon

might visualize a futuristic perspective for a universal visualization system where key issues

and challenges could address the sustainable development of data visualization techniques.

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