Findability and Usability
Lessons Learnt from Text Analytics
Anna Divoli
@annadivoli
Pingar Research
CS4HS 2013 @ Unitec
twitter, July 2006Google, September 1998
http://www.laurencecaro.co.uk/other/what-the-worlds-biggest-websites-looked-like-...
http://www.laurencecaro.co.uk/other/what-the-worlds-biggest-websites-looked-like-at-launch/
Yahoo!, March 1995
Facebook, F...
http://www.whoisandrewwee.com/facebook/the-new-facebook-hot-or-not/
Often there is resistance!
Anna Divoli CS4HS 2013 @ Unitec
http://imgur.com/gallery/U8C1r
Users adapt & evolve as well!
Anna Divoli CS4HS 2013 @ Unitec
15
• Familiar Interfaces
• User Centered Design & Usability
• Who am I & who is Pingar
• Text analytics in a nutshell
• Our u...
• Familiar Interfaces
• User Centered Design & Usability
• Who am I & who is Pingar
• Text analytics in a nutshell
• Our u...
http://blog.lib.umn.edu/torre107/si/pics/superficialintelligence14.jpg
Usability testing is important! Needs to be done ri...
User Centered Design
Design
Prototype
User
Feedback
Analysis
Anna Divoli CS4HS 2013 @ Unitec
http://headrush.typepad.com/photos/uncategorized/focusonusersnotcompetitors.jpg
http://headrush.typepad.com/creating_passi...
• Eye tracking
• Mouse clicks
• Tasks (goal & time)
• Questionnaires
• Observation
• Talk aloud
• Brain scanners
• ….
Usab...
http://gazehawk.com/blog/
Eye tracking
Anna Divoli CS4HS 2013 @ Unitec
• Familiar Interfaces
• User Centered Design & Usability
• Who am I & who is Pingar
• Text analytics in a nutshell
• Our u...
Who am I?
Anna Divoli CS4HS 2013 @ Unitec
UK
BSc (1999) Biomedical Sciences
.
MSc (2001) Biosystems & Informatics
.
PhD (2...
Who is Pingar?
Anna Divoli CS4HS 2013 @ Unitec
A small global company that uses state
of the art text analytics to offer
s...
• Familiar Interfaces
• User Centered Design & Usability
• Who am I & who is Pingar
• Text analytics in a nutshell
• Our u...
research
A brief Text Analytics Overview – just Entity Recognition basics…
Anna Divoli CS4HS 2013 @ Unitec
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went to Was...
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went to Was...
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went to Was...
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went to Was...
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went to Was...
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went to Was...
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
Sam Arlington initiate...
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
Sam Arlington initiate...
What?
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went ...
What?
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices last month.
John Smith went ...
How? Why? How do we feel about it?
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices...
How? Why? How do we feel about it?
S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd
offices...
S. Arlington did not initiate partnership discussions during his visit to Eureka’s
Ltd offices last month.
John Smith went...
Eye drops off shelf.
Include your children when
baking cookies.
Turn right here.
John saw the man on the
mountain with a t...
Looking for: interactions between SAF and viral LTR elements
(SAF is a transcription factor, LTR stands for ‘long terminal...
Knowledge management - Extracting metadata
Search engines
Question Answering
Text mining / knowledge discovery
Analytics &...
• Familiar Interfaces
• User Centered Design & Usability
• Who am I & who is Pingar
• Text analytics in a nutshell
• Our u...
Specialized domain study:
1. Search interface features: Biomedical Scientists (HCIR)
Anna Divoli and Alyona Medelyan
Searc...
1. Search interface features: Biomedical Scientists
Anna Divoli CS4HS 2013 @ Unitec
Anna Divoli and Alyona Medelyan, Search interface feature evaluation in biosciences,
HCIR 2011, Google, Mountain View, CA,...
animal models huntington disease
Facets (in search systems)
Anna Divoli CS4HS 2013 @ Unitec
Most liked Least liked
animal models huntington disease
Bio-Facets
Anna Divoli CS4HS 2013 @ Unitec
• Faceted search is the most important stand alone feature in a search
interface for bioscientists.
• Few, query-oriented ...
Search expansions★
Facetted refinement
Related searches
Results preview★
- hard to read
Semedico
- hard to read
NextBio
- ...
• Familiar Interfaces
• User Centered Design & Usability
• Who am I & who is Pingar
• Text analytics in a nutshell
• Our u...
Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD
Anna Divoli CS4HS 2013 @ Unitec
Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD
Anna Divoli CS4HS 2013 @ Unitec
Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD
Anna Divoli CS4HS 2013 @ Unitec
Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD
Anna Divoli CS4HS 2013 @ Unitec
Boston Oct 2012
A
A B C D E F
B
C
D
E
F
A B C D E F A B C D E F A B C D E F A B C D E F
Exploring UI features - Systems Te...
• Menu highlighting
• Hierarchical folder layout
• Expand hierarchy with “+” and “–”
• Dual view (tree on left, results on...
• Familiar Interfaces
• User Centered Design & Usability
• Who am I & who is Pingar
• Text analytics in a nutshell
• Our u...
60.0%26-40
12.7%41-60
0%61 or older
27.3%25 or younger
Age:
52.7%College/University
43.6%Graduate School
3.6%High School
H...
44.2%popularity (A)
42.3%alphabetically (B)
13.5%no preference
Concept sorting
Anna Divoli CS4HS 2013 @ Unitec
42.3%A
51.9%B
5.8%no preference
Displaying Counts
Anna Divoli CS4HS 2013 @ Unitec
72.5%in frames (A)
23.5%with labels (B)
3.9%no preference
Using Labels
Anna Divoli CS4HS 2013 @ Unitec
47.1%A
37.3%B
15.7%no preference
Plus/minus signs or arrows
Anna Divoli CS4HS 2013 @ Unitec
11.8%B
70.6%C
3.9%no preference
13.7%A
Search Results Display
Anna Divoli CS4HS 2013 @ Unitec
74.5%partial
64.7%hidden
2.0%no preference
Search Functionality
Anna Divoli CS4HS 2013 @ Unitec
Outcome: User-friendly Taxonomy Editor
Anna Divoli CS4HS 2013 @ Unitec
Outcome: User-friendly Taxonomy Editor
Anna Divoli CS4HS 2013 @ Unitec
Lessons Learnt
Anna Divoli CS4HS 2013 @ Unitec
Perfection is achieved, not when there is nothing more to add,
but when the...
Ignas Kukenys
PhD (2010) in Computer Vision,
University of Otago
Previously: NextWindow
Expert in: computer vision, machin...
http://farm5.static.flickr.com/4087/5019944289_5e2f0637c9.jpg
Take away message: Involve the user!
Anna Divoli CS4HS 2013 ...
Upcoming SlideShare
Loading in …5
×

"Findability and usability lessons learnt from text analytics" By: Anna Divoli At: CS4HS 2013 Unitec

1,019 views

Published on

CS4HS@Unitec workshop
http://hyperdisc.unitec.ac.nz/cs4hs/index.php

Anna Divoli
4 Oct 2013

Findability and usability - lessons learnt from text analytics

Text analysis technologies are used widely in a number of applications. Many of these applications aim to end users. The best example is search. In this talk, we will go over the basic concepts of text analytics and present results from a number of usability studies on user search interfaces.

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,019
On SlideShare
0
From Embeds
0
Number of Embeds
22
Actions
Shares
0
Downloads
11
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

"Findability and usability lessons learnt from text analytics" By: Anna Divoli At: CS4HS 2013 Unitec

  1. 1. Findability and Usability Lessons Learnt from Text Analytics Anna Divoli @annadivoli Pingar Research CS4HS 2013 @ Unitec
  2. 2. twitter, July 2006Google, September 1998 http://www.laurencecaro.co.uk/other/what-the-worlds-biggest-websites-looked-like-at-launch/
  3. 3. http://www.laurencecaro.co.uk/other/what-the-worlds-biggest-websites-looked-like-at-launch/ Yahoo!, March 1995 Facebook, February 2004
  4. 4. http://www.whoisandrewwee.com/facebook/the-new-facebook-hot-or-not/
  5. 5. Often there is resistance! Anna Divoli CS4HS 2013 @ Unitec
  6. 6. http://imgur.com/gallery/U8C1r Users adapt & evolve as well! Anna Divoli CS4HS 2013 @ Unitec 15
  7. 7. • Familiar Interfaces • User Centered Design & Usability • Who am I & who is Pingar • Text analytics in a nutshell • Our usability studies & findings • 1. Search interface features: Biomedical Scientists • 2. Study existing popular systems • 3. Mock ups of specific features • Take away message Outline Anna Divoli CS4HS 2013 @ Unitec
  8. 8. • Familiar Interfaces • User Centered Design & Usability • Who am I & who is Pingar • Text analytics in a nutshell • Our usability studies & findings • 1. Search interface features: Biomedical Scientists • 2. Study existing popular systems • 3. Mock ups of specific features • Take away message Outline Anna Divoli CS4HS 2013 @ Unitec
  9. 9. http://blog.lib.umn.edu/torre107/si/pics/superficialintelligence14.jpg Usability testing is important! Needs to be done right! Anna Divoli CS4HS 2013 @ Unitec
  10. 10. User Centered Design Design Prototype User Feedback Analysis Anna Divoli CS4HS 2013 @ Unitec
  11. 11. http://headrush.typepad.com/photos/uncategorized/focusonusersnotcompetitors.jpg http://headrush.typepad.com/creating_passionate_users/2006/07/ User Centered Design – What should companies focus on! Anna Divoli CS4HS 2013 @ Unitec FOCUS Users Competitors
  12. 12. • Eye tracking • Mouse clicks • Tasks (goal & time) • Questionnaires • Observation • Talk aloud • Brain scanners • …. Usability testing tools Measurements & Preferences on: • Aesthetics • Craftmanship • Technical performance • Ergonomics • Usability Anna Divoli CS4HS 2013 @ Unitec
  13. 13. http://gazehawk.com/blog/ Eye tracking Anna Divoli CS4HS 2013 @ Unitec
  14. 14. • Familiar Interfaces • User Centered Design & Usability • Who am I & who is Pingar • Text analytics in a nutshell • Our usability studies & findings • 1. Search interface features: Biomedical Scientists • 2. Study existing popular systems • 3. Mock ups of specific features • Take away message Outline Anna Divoli CS4HS 2013 @ Unitec
  15. 15. Who am I? Anna Divoli CS4HS 2013 @ Unitec UK BSc (1999) Biomedical Sciences . MSc (2001) Biosystems & Informatics . PhD (2006) Biomedical Text Mining: - Sentence extraction - Automatic protein family database annotation - Document clustering and visualization USA (2006-2011) Postdoctoral Academic Research: - Text analytics - Information retrieval - User search interfaces - Usability research - Knowledge acquisition - Expert opinions analysis NZ (2011- ) Pingar Research: - Entity Extraction - Automatic Taxonomy Generation - Taxonomy Editor (UI) - Friendly UIs - Usability & UX
  16. 16. Who is Pingar? Anna Divoli CS4HS 2013 @ Unitec A small global company that uses state of the art text analytics to offer solutions! We analyse unstructured text! Entity Extraction People, Organizations, Locations Dates, Money amounts, Email addresses… + Custom Entities Keyword Extraction Taxonomy Term Extraction Domain specific terminology (can plug in any taxonomy/ontology) Summarization Select length and focus Redaction & Sanitization Ensure anonymization of sensitive information. In English, German, French, Spanish Dutch, Chinese, Japanese
  17. 17. • Familiar Interfaces • User Centered Design & Usability • Who am I & who is Pingar • Text analytics in a nutshell • Our usability studies & findings • 1. Search interface features: Biomedical Scientists • 2. Study existing popular systems • 3. Mock ups of specific features • Take away message Outline Anna Divoli CS4HS 2013 @ Unitec
  18. 18. research A brief Text Analytics Overview – just Entity Recognition basics… Anna Divoli CS4HS 2013 @ Unitec
  19. 19. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Find and classify names… Information Extraction – Named Entity Recognition Anna Divoli CS4HS 2013 @ Unitec
  20. 20. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Find and classify names… Information Extraction – Named Entity Recognition Anna Divoli CS4HS 2013 @ Unitec
  21. 21. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Find and classify names… People Locations Organizations Methods: lexicon-based (gazeteers) grammar-based (rule-based) statistical models (machine learning) hybrids Information Extraction – Named Entity Recognition Anna Divoli CS4HS 2013 @ Unitec
  22. 22. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Find and classify names… People Locations Organizations Methods: lexicon-based (gazeteers) grammar-based (rule-based) ✓ statistical models (machine learning) ✓ hybrids Information Extraction – Named Entity Recognition Anna Divoli CS4HS 2013 @ Unitec
  23. 23. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Find and classify names… People Locations Organizations Who? Where? When? Dates Information Extraction – Named Entity Recognition Anna Divoli CS4HS 2013 @ Unitec
  24. 24. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Word Sense Disambiguation: identifying which sense/meaning of a word is used in a sentence, when the word has multiple meanings. Text normalization: transforming text into a single canonical form that it might not have had before. Disambiguation & Normalization: Word Sense Disambiguation & Text Normalization Anna Divoli CS4HS 2013 @ Unitec
  25. 25. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. Sam Arlington initiated partnership discussions during his visit to Eureka offices in September. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. J. Smith went to Washington DC to see the Smithsonian Institute and also met up with Virginia Peterson for a coffee. Word Sense Disambiguation & Text Normalization Anna Divoli CS4HS 2013 @ Unitec
  26. 26. S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. Sam Arlington initiated partnership discussions during his visit to Eureka offices in September. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. J. Smith went to Washington DC to see the Smithsonian Institute and also met up with Virginia Peterson for a coffee. Word Sense Disambiguation & Text Normalization Anna Divoli CS4HS 2013 @ Unitec
  27. 27. What? S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Fact & Relationship extraction Anna Divoli CS4HS 2013 @ Unitec
  28. 28. What? S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Fact & Relationship extraction Anna Divoli CS4HS 2013 @ Unitec
  29. 29. How? Why? How do we feel about it? S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. Deeper knowledge & Sentiment Anna Divoli CS4HS 2013 @ Unitec
  30. 30. How? Why? How do we feel about it? S. Arlington initiated partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and also met up with Virginia for a coffee. S. Arlington visited the Eureka’s Ltd offices last month to initiate partnership discussions. John Smith was delighted to go to Washington to see the Smithsonian and also met up with Virginia for a coffee. Deeper knowledge & Sentiment Anna Divoli CS4HS 2013 @ Unitec
  31. 31. S. Arlington did not initiate partnership discussions during his visit to Eureka’s Ltd offices last month. John Smith went to Washington to see the Smithsonian and almost also met up with Virginia for a coffee. Negation… Hedging… Conditional phrases… Also: Name boundaries… Disambiguation… Normalization… Common problems Anna Divoli CS4HS 2013 @ Unitec
  32. 32. Eye drops off shelf. Include your children when baking cookies. Turn right here. John saw the man on the mountain with a telescope. He gave her cat food. They are hunting dogs. Human language! Anna Divoli CS4HS 2013 @ Unitec
  33. 33. Looking for: interactions between SAF and viral LTR elements (SAF is a transcription factor, LTR stands for ‘long terminal repeat’) Gene names: tinman, lilliputian, dreadlocks, lush, cheap date, methuselah, Van Gogh, maggie, brainiac, grim, reaper, cleopatra, swiss cheese, ken and barbie, kenny, out cold, lava lamp, hamlet, sonic hedgehog, werewolf, half pint, fucK, drop dead, chardonnay, agnostic, I’m not dead yet… Example: Biology… Anna Divoli CS4HS 2013 @ Unitec
  34. 34. Knowledge management - Extracting metadata Search engines Question Answering Text mining / knowledge discovery Analytics & trend analysis Applications Anna Divoli CS4HS 2013 @ Unitec
  35. 35. • Familiar Interfaces • User Centered Design & Usability • Who am I & who is Pingar • Text analytics in a nutshell • Our usability studies & findings • 1. Search interface features: Biomedical Scientists • 2. Study existing popular systems • 3. Mock ups of specific features • Take away message Outline Anna Divoli CS4HS 2013 @ Unitec
  36. 36. Specialized domain study: 1. Search interface features: Biomedical Scientists (HCIR) Anna Divoli and Alyona Medelyan Search interface feature evaluation in biosciences, HCIR 2011, Google, Mountain View, CA UI preferred features studies: 2. Study existing popular systems (EuroHCIR) Matthew Pike, Max L. Wilson, Anna Divoli and Alyona Medelyan CUES: Cognitive Usability Evaluation System, EuroHCIR 2012, Nijmegen, Netherlands 3. Mock ups of specific features (survey) Our User Studies Anna Divoli CS4HS 2013 @ Unitec
  37. 37. 1. Search interface features: Biomedical Scientists Anna Divoli CS4HS 2013 @ Unitec
  38. 38. Anna Divoli and Alyona Medelyan, Search interface feature evaluation in biosciences, HCIR 2011, Google, Mountain View, CA, USA Facets – favorite feature for search systems Anna Divoli CS4HS 2013 @ Unitec
  39. 39. animal models huntington disease Facets (in search systems) Anna Divoli CS4HS 2013 @ Unitec
  40. 40. Most liked Least liked animal models huntington disease Bio-Facets Anna Divoli CS4HS 2013 @ Unitec
  41. 41. • Faceted search is the most important stand alone feature in a search interface for bioscientists. • Few, query-oriented facets presented as checkboxes work best. • Overly simple aesthetics, although not desirable, do not hurt overall UI score. • Complex aesthetics turn users away from the systems. • Bioscientists prefer tools that help them narrow their search, not expand it. • For generic search: doc-based facets. For domain-specific search: query-based facets. Facets as search features for biomedical scientists: Findings Anna Divoli CS4HS 2013 @ Unitec
  42. 42. Search expansions★ Facetted refinement Related searches Results preview★ - hard to read Semedico - hard to read NextBio - noisy, many symbols GoPubMed - small font size PubMed - too general - not useful + overall look - color Semedico + g - unc + useful categories - slow functionality - too complex/busy - too many colors Semedico + “reviews” category - limited functional. - poor design PubMed + quick paper access + simple + vertical list - nothing special Solr + “top terms” useful - too many symbols - too busy - colors GoPubMed + - not - not scientific + colors - too small - too busy Bing + relevant - poor context - no variety PubMed + s - snipbr ff ig br ff ig br ff ig br ff ig ig • Useful categories • Simple • Vertical list • Too complex/busy • Too many colors • Poor design • Limited functionality • Too many symbols • Not special/ Colorless Facets as search feature: likes & dislikes Anna Divoli CS4HS 2013 @ Unitec
  43. 43. • Familiar Interfaces • User Centered Design & Usability • Who am I & who is Pingar • Text analytics in a nutshell • Our usability studies & findings • 1. Search interface features: Biomedical Scientists • 2. Study existing popular systems • 3. Mock ups of specific features • Take away message Outline Anna Divoli CS4HS 2013 @ Unitec
  44. 44. Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD Anna Divoli CS4HS 2013 @ Unitec
  45. 45. Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD Anna Divoli CS4HS 2013 @ Unitec
  46. 46. Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD Anna Divoli CS4HS 2013 @ Unitec
  47. 47. Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD Anna Divoli CS4HS 2013 @ Unitec
  48. 48. Boston Oct 2012 A A B C D E F B C D E F A B C D E F A B C D E F A B C D E F A B C D E F Exploring UI features - Systems Tested: Yippy, Carrot, MeSH, ESD
  49. 49. • Menu highlighting • Hierarchical folder layout • Expand hierarchy with “+” and “–” • Dual view (tree on left, results on right) • Ability to change visualisations of taxonomy • Search function is important • Familiar interface with folders • Too simple or too much writing - would be nice to have color • Lots of scrolling • Dots in carrot circle – confusing • Double click on foam tree is unintuitive • Too broad taxonomies Exploring UI features (Yippy, Carrot, MeSH, ESD): likes & dislikes Anna Divoli CS4HS 2013 @ Unitec
  50. 50. • Familiar Interfaces • User Centered Design & Usability • Who am I & who is Pingar • Text analytics in a nutshell • Our usability studies & findings • 1. Search interface features: Biomedical Scientists • 2. Study existing popular systems • 3. Mock ups of specific features • Take away message Outline Anna Divoli CS4HS 2013 @ Unitec
  51. 51. 60.0%26-40 12.7%41-60 0%61 or older 27.3%25 or younger Age: 52.7%College/University 43.6%Graduate School 3.6%High School Highest level of education: 47.3%Yes, but very little 21.8%Yes 30.9%No Do you have experience using taxonomies? 47.3%Very 47.3%Second nature 5.5%Somewhat How comfortable you are with computers? Taxonomy UI preferences: The 51 participants Anna Divoli CS4HS 2013 @ Unitec
  52. 52. 44.2%popularity (A) 42.3%alphabetically (B) 13.5%no preference Concept sorting Anna Divoli CS4HS 2013 @ Unitec
  53. 53. 42.3%A 51.9%B 5.8%no preference Displaying Counts Anna Divoli CS4HS 2013 @ Unitec
  54. 54. 72.5%in frames (A) 23.5%with labels (B) 3.9%no preference Using Labels Anna Divoli CS4HS 2013 @ Unitec
  55. 55. 47.1%A 37.3%B 15.7%no preference Plus/minus signs or arrows Anna Divoli CS4HS 2013 @ Unitec
  56. 56. 11.8%B 70.6%C 3.9%no preference 13.7%A Search Results Display Anna Divoli CS4HS 2013 @ Unitec
  57. 57. 74.5%partial 64.7%hidden 2.0%no preference Search Functionality Anna Divoli CS4HS 2013 @ Unitec
  58. 58. Outcome: User-friendly Taxonomy Editor Anna Divoli CS4HS 2013 @ Unitec
  59. 59. Outcome: User-friendly Taxonomy Editor Anna Divoli CS4HS 2013 @ Unitec
  60. 60. Lessons Learnt Anna Divoli CS4HS 2013 @ Unitec Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away. Antoine de Saint-Exupery, French writer (1900 - 1944) • Follow basic design principles: alignments, right use of colors, etc • Avoid clutter! Go for clean and simple always! • Aim for intuitive User Interfaces. Do not make users think! • You will never please everybody! Choose the users and use cases you plan to support.
  61. 61. Ignas Kukenys PhD (2010) in Computer Vision, University of Otago Previously: NextWindow Expert in: computer vision, machine learning, classification methods Alyona Medelyan PhD (2009) in Natural Language Processing, University of Waikato Previously: Google, European Media Lab Expert in: keyword and entity extraction, Wikipedia mining Ralf Heese . Diploma in Computer Science, PhD candidate, Humboldt-Universität Berlin . Previously: Humboldt-Universität Berlin, Freie Universität Berlin Expert in: semantic databases, semantic technologies Andy Chao BSc (2011) in Computer and Information Sciences, AUT Hayden Curran Computer Science, University of Waikato (2008-2010) Collaborators Annika Hinze Ian Witten Max Wilson Jeen Broekstra Alumni Matthew Pike Steve Manion David Milne Jose Chen Anna Huang Anna Divoli . PhD (2006) in Biomedical Text Mining, University of Manchester . Previously: University of Chicago, UC Berkeley, Inpharmatica Expert in: biomedical text mining, text analytics, user search interfaces, usability Waikato Uni Waikato Uni Nottingham Uni Rivuli Development Nottingham Uni PhDc Canterbury Uni PhDc CSIRO postdoc NIH fellow Yangtze Uni Lecturer Acknowledgements: Pingar Research Team And all 65+ anonymous studies participants!
  62. 62. http://farm5.static.flickr.com/4087/5019944289_5e2f0637c9.jpg Take away message: Involve the user! Anna Divoli CS4HS 2013 @ Unitec

×