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Designing For Infinity - Abridged Version
- 1. Designing for Infinity UI Patterns for working with Big Data -d- Dustin Kirk www.dustinkirk.com @Dustin_Kirk www.webmetrics.com
- 2. Designing for Infinity – Dustin Kirk Why Infinity? Great Usability Bad Small Data Typical Design Patterns
- 3. Designing for Infinity – Dustin Kirk Why Infinity? Great Usability Bad Small Data Design Patterns For Big Data
- 4. Designing for Infinity – Dustin Kirk Designing for Infinity Design Patterns Covered 1. Searching 2. Filtering 3. Infinite Scrolling 4. Context Recognition 5. Distributed Workload 6. Automation 7. Loading Data 8. Navigation
- 5. Searching & Filtering Parts 1 & 2 of 8
- 8. Allow for entering in other locations
- 9. Search results should be visible on map
- 11. Ability to create conditionals with the use of a key press + mouse click
- 13. Visual aids teach unfamiliar terms
- 15. Replaces check boxes
- 16. Allows user to browse as well as filter
- 17. Ability to filter on multiple characters
- 18. List updates after each character entered
- 19. List keeps up to 10 items in view
- 20. List scrolls to show everything
- 22. Hides within the menu of ‘select all’
- 24. Gmail Designing for Infinity – Dustin Kirk Searching & Filtering Close, but it shows only a maximum of 10 items Auto-suggest in Gmail for Contact Disambiguation
- 26. Contacts show up one time for each method
- 27. Search by both contact name & method
- 29. List updates after each character entered
- 30. List keeps multiple items in view
- 31. List scrolls to show everything
- 34. Asynchronous data loading keeps it fast
- 35. Buffers additional data to prevent jerky loading
- 36. Calculates total height to prevent jerky scrolling
- 40. Adjust icon size the most used items are easy to locate and click
- 41. Allow the menu to resize based on screen real-estate to maximize shortcuts
- 43. Contextual help only appears when it is relevant
- 44. Eliminates need for external help documentation for users
- 48. Enables general public to contribute and govern
- 54. Use learning algorithms to identify patterns
- 55. Classify the remaining items automatically
- 57. Use learning algorithms to identify patterns
- 59. Provide users with customization tools
- 62. Automate data importing
- 65. Ability to set zoom level (day, month, year, custom)
- 67. Ability to filter data categories, choose data source to sort, and ability to zoom
- 69. Useful when control options are limited
- 71. List updates in real-time
- 72. Heading always remains at the top
- 74. Designing for Infinity – Dustin Kirk Concluding Notes Patterns Covered 1. Searching 2. Filtering 3. Infinite Scrolling 4. Context Recognition 5. Distributed Workload 6. Automation 7. Loading Data 8. Navigation
- 75. iTunes Designing for Infinity – Dustin Kirk Concluding Notes
- 76. Designing for Infinity – Dustin Kirk Concluding Notes Takeaway Tips 1. Think about the data limits of the all the patterns you use 2. Utilize patterns that have been proven by others 3. Keep the innovation going and improve onpatterns View these slides online and share them with others www.dustinkirk.com/infinity/ @Dustin_Kirk
- 77. Dustin Kirk is a Senior User Experience Designer at Neustar in the Webmetrics product group. He earned a B.S. in Computer Science and Psychology at Rensselaer as well as completed a M.S. in Human Computer Interaction. Dustin’s passion lies in utilizing his multi-disciplinary background and interests to make user interfaces highly efficient and effective while delivering great user experiences. www.dustinkirk.com @Dustin_Kirk www.webmetrics.com
Notas do Editor
- As the amount of data that one interacts with increases, the usability decreases. Think about the standard drop down list companies use for selecting your country when registering for a website. As the number of items in the selection list increases, the amount of time it takes to find an item also increases. While keyboard shortcuts such as typing the first letter exist, for countries like Spain, it takes 18 keystrokes to finally select it. This presentation identifies more efficient patterns that allow users to work with large amounts of data efficiently.
- The following design patterns have been selected as they maintain their usability or become increasingly better (think recommendation engines) as the amount of data increases.
- - This is a great example of a scalable UI for applying and displaying tags (labels)
- Create shortcuts of common actions.For example combine filtering and selecting into a single step using a drop-down menu for selection.
- Same as the iPhone SMS appAuto-suggest is highly useful when creating scalable user interfaces-Auto-suggest prevents misspelling Allows users to select the appropriate item quickly Allows the user to select one of multiple sub-items for the same item being typed. (also used when sending a SMS on iPhone to select between multiple phone numbers)
- Same as the iPhone SMS appAuto-suggest is highly useful when creating scalable user interfaces-Auto-suggest prevents misspelling Allows users to select the appropriate item quickly Allows the user to select one of multiple sub-items for the same item being typed. (also used when sending a SMS on iPhone to select between multiple phone numbers)
- Great model for selecting multiple items from a big list Ability to search Uses auto-complete Uses real-time search to narrow down results Build up lists by running multiple searches, selecting people, and viewing a list of all selected people
- Great model for creating a list Ability to search Uses auto-complete Uses real-time search to narrow down results Build up lists by running multiple searches, selecting people, and viewing a list of all selected people
- Face recognition is used in iPhoto Users tag a portion of the photos with the people’s names, and iPhoto tries to tag the rest. While it isn’t perfect, it beats doing it all manually.
- Gmail has used the same technique for their Priority Inbox which sorts out the mail you usually read from the rest
- Spore’s characters are created by players and texture mapping, character movement, and music are all created based on a system of rules
- Players describe a photo and earn points when the person they play with matches their descriptions
- - Auto-detects data key value pairs from 3rd party sources and excel files
- - Once you start navigating in 3D space, your inspiration really needs to come from videos games. For computers that means using [w][a][s][d] keyboard controls along with mouse pointing.- Nonetheless Photosynth is a great use of crowd-sourced content
- Microsoft Pivot is an excellent inspiration for anyone working on BI tools
- The techniques shared here can be found in all types of software that deals with Big DataiTunes is a good example of managing informationtagging (play-lists)filterssearchingsortingrecommendation engine