Visualization for Data Analysis: A New Way to Look at Content

Marjorie M.K. Hlava.
President
Access Innovations, Inc.
mhlava@accessinn.com
505-998-0800

MARCH 28TH
VIRTUAL LUNCH WEBINAR:
VISUALIZATION FOR DATA
ANALYSIS: A NEW WAY TO LOOK
AT CONTENT

1

A picture is worth…
thousand words
As librarians we normally look at data in lists, citations, and other text
based presentations. Increasingly however this data can be analyzed,
manipulated and presented as visual displays. Maps of science, places and
spaces, increased amounts storage and computing power have made
working with digital assets possible. Presenting the data in new and visual
ways allow us to see trends, changes in research directions, coverage,
demographic trends, data overlap and the white spaces where data does
not exist on a topic – knowledge gaps are exposed. This talk will cover
how the data is prepared and options for visual display content…
100 words x10 = thousand words

2

Why take a visual look?
• As librarians we normally look at data in lists, citations, and other
text based presentations.
• Increasingly however this data can be analyzed, manipulated and
presented as visual displays.
• Maps of science, places and spaces, increased amounts storage
and computing power have made working with digital assets
possible.
• Presenting the data in new and visual ways allow us to see trends,
changes in research directions, coverage, demographic trends, data
overlap and the white spaces where data does not exist on a topic
– knowledge gaps are exposed.
3

Visualization of data
• Needs • Is richer with
− Measurement − Linking
− Metrics − Semantic enrichment
− Numbers − Classification
• Shows
− Adjacency • Supports
− Relationships − Forecasting
− Trends − Trend analysis
− Co – occurrence − Segmentation
− Conceptual distance
− Distribution

4

Man’s attention to
visual display to convey
knowledge is ancient

Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 5

The art in maps
is a
longstanding
tradition


Super imposing data is now common
A mash up example
Traffic Injury Map
UK Data Archive
US National Highway
Safety Administration
Google Maps Base
Accident categories include
children
automobile
bicycle
etc.
Data
time
place
type

Source:
JISC TechWatch: Data Mash-ups September 2010

The most popular APIs for mashups

 a) July 2009 b) October 2009

Source: JISC TechWatch: Data Mash-ups September 2010
Programmable web data

Radio4 website
Data source
MapTube
Credit Crunch Mood Map

User Website questionnaire

Crowdsourced visualization and mapping

Early responses Final Credit Crunch Mood Map

Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
11

Mash up of bird flight migrations and
weather patterns
http://www.youtube.com/watch?v=uPff1t4pXiI&feature=youtu.be


http://www.youtube.com/watch?v=nokQBjk1s_8&feature=player_embedded


Noise Tube Application uses geo-locations of SMS
like Twitter with GPS sensing on mobile devices


Changes in our life time!

Its only the beginning

Fine, So there are nice visual maps,

What about information from databases and
libraries??


Start with data – like this XML file


Index or tag using subject terms from
thesaurus or taxonomy
 date, category, taxonomy term, frequency


Many views of one set of data


Load to a visualization program
Like Prefuse


Or Pajek


National Information center
for Educational Media
Albuquerque’s own
» Sandia developed VxInsight
» Access Innovations NICEM
Same data - three views
Primary and Secondary Education in US
Shows the US Valley of Science
Little Science taught in elementary years


Requirements for Visualization
 From a society / publisher perspective
» Which topical areas form our core? periphery?
» Where is the coverage dense? thin?
» Which topical areas are most active? least active?
» Which topical areas seem to be emerging?
declining?
» Which topical areas are interrelated? isolated?
» What are the overlaps between journals / segments?
» Where are the potential expansion points?
 From a thesaurus perspective
» What terms are too broadly defined?
» How do actual topical relationships differ from the
thesaurus structure?


Using visualization to show
 From a society / publisher perspective
» Identify Core, Boundary and Cross Border
» Provides Indicators
 Activity
 Growth
 Relatedness
 Centrality
» Locates Journal domains

 From a thesaurus perspective
» Identifies terms that are too broadly defined
» Potential Improvements in thesaurus structure using topic
structures


Case Study:
Mapping IEEE thesaurus space
 We are interested in an expanded map that
includes adjacencies to the IEEE data
» Expanded term set shows adjacent white space;
opportunities for expansion
 Overlaps and edges of the science
» We need comparison data
 Learn the directions in the field
» Low occurrence rate in IEEE documents?
» Linkage to terms in IEEE documents?
 Where do we find these terms? How can we
add them?


The process
 Built a rule base to auto index IEEE content
» “90 % accuracy out of the box on journal data”*
» “80% out of the box on proceedings data”*
 The overlapping data sets
» Auto indexed 1.2 million Xplore records
» 10 years of US Patent data
» 10 years of Medline
 Term sets used
» IEEE thesaurus terms rule base
» Medical Subject Headings (MeSH) (and simple rule base)
» Defense Technical Information Center (DTIC) Thesaurus (
and simple rule base)
» Similar level of detail to current IEEE thesaurus terms


Defining expanded term space
1. The data - Select related corpus

14k DTIC

2k terms

IEEE
475k patents PubMed
1.2M documents
525k docs

24k MeSH


2. Identify related terms
Use the IEEE Thesaurus to index the three collections

2k terms

IEEE
1.2M documents


2. Identify related terms
Use MESH and DTIC to also index the three collections

2k terms

IEEE
1.2M documents


3. Resulting term set
The co-indexed items from the three collections

2k terms

IEEE
1.2M documents


4. Term:Term Matrix
Where do the articles and their indexing intersect?


Visualization Strategies

Visualization
Matrix Software


All data up-posted to the top level


Many map options
Previous Experience IEEE Experience


IEEE Portfolio
Electromag
Compat Soc Prof
Reliability Commun
Society Society Education
Sensors Ultrason, Robot Society
Oceanic Council Ferro … Autom Soc
Engng Soc
Instr
Measur Soc
CouncilDielectr El Nucl Plasma
SupercondInsul Soc Sys Man
Sci Soc Computer
Cyber
Prod Saf Society Photonics
Compon, Systems Society
Engng Soc Magnetics Council Soc
Packag …
Soc

Nanotech Social
Council Impl Techn
Computer
Intelligence
Society Eng Med
Biol Sci

Council Electr
Design Auto

Industr
Industry
Geosci Rem Electr Soc
Appl Soc
Sens Soc

Antennas
Propag Soc
Power
Power &
Electron Soc Microwave
Energy Soc
Theory Soc

Circuits &
Signal Consumer
Systems
Electron Proc Soc Electr Soc
Dev Soc

Broadcast
Intell Transp Techn Soc
Sys Soc
Solid St
Circuits Soc

Aerosp
Electr Vehicular
Sys Soc Techn Soc

Commun
Soc

Info Theory
Soc


Radial Visualization


Subsidiary radials
Journal of Instrumentation

Compon, Dielectr El Ultrason, Electromag
Packag … Instr Ferro … Compat Soc
Insul Soc
Measur Soc

Prod Saf Council Magnetics Sensors Antennas
Engng Soc Supercond Soc Council Propag Soc

Nanotech Oceanic Geosci Rem Nucl Plasma
Council Engng Soc Sens Soc Sci Soc


The research team
 Access Innovations / Data Harmony
» Founded in 1978
» Data enrichment and normalization
» Suite of Semantic Enrichment tools

 SciTechStrategies
» Understanding data through visualization

 IEEE Indexing & Abstracting Group


Use a Thesaurus to Label Maps
Construction Packaging Consumer
Products
Vehicles,
Parts Welding
Gearing

Automotive + Flow
Defense Boats Appliances Food
Brakes Hygiene
Aircraft
Dynamics Sprayers Cleaning
IC Engines
Turbines Industrial
Pumps
ValvesProducts Exhaust

Leisure Fitness Outerwear Footwear
Control Medical
Pipes Devices
Toys Health Care
Clocks Games Blasting Radiology
Cooling
Measurement

Energy Med Instruments Agriculture
Cables Heating Plants,
Micro-orgs
Conveyers
Oilfield
Services
Pharma
Lamps Components
Printing
Telecom Computer Motors
Acyclic Comp
HW/SW Semiconductors Lubricants Metals
Optics
Lasers Rubber
Molding Paper
Displays Electronics Catalysis
Magn/Elect Conductors Layers
Circuits Textiles
Electrochem
Magnets Macromolecules
Disk
Amplifiers Photochem Chemicals Coatings


Questions Answered
 Is there a way, using our own information, to forecast our
direction?
 Where is the industry headed? What about by technology
sector?
 Does our coverage match our mission and vision?
 Can we become smarter about our data and potential
markets using our collection in new ways?
Are the societies publishing and talking about what their
charter indicates they cover?
 What are the trends – are topics emerging/cooling?
 Can we use technology and our own data to explore these
questions while enhancing our data?

Well Formed Data • Semantic Enrichment • Taxonomies •46
Access Innovations • Data Harmony

Conference Strategy


Publication Strategy

JASIST reference


We looked at
Visualization of data
 Finding the Metrics  How to enrich with
» Measurement » Linking
» Numbers » Semantic enrichment
» Terms as indicators » Classification
 Ways to show  Maps supporting
» Adjacency
» Relationships » Forecasting
» Trends » Trend analysis
» Co – occurrence » Segmentation
» Conceptual distance » Distribution

49

Effective maps require

 Contextual data
 Detailed data
 Classification methods
 At least two directions in the matrix
 A little art for fun


Changing the way we
interact with reality

Acrossair’s Augmented reality application – just point your phone at it

It just takes a little imagination

Thank you

Marjorie M.K. Hlava
President, Access Innovations
505-998-0800
mhlava@accessinn.com
52

Visualization for Data Analysis: A New Way to Look at Content

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