1. 1 Information Search and Visualization  Information Terminology  Information Retrieval  Information gathering, seeking, filtering, and visualization  Task objects: e.g., video clips, documents  Task actions: browsing and searching  Interface actions: Scrolling, joining, zooming, linking  Database Management – refers to structured relational database systems, well defined attributes and sort-keys  Data mining, data warehouses, data marts  Knowledge networks, semantic webs

2. 2 Information Search and Visualization  Information Terminology  Specific fact finding: known-item search Example: find the email address of Keith Jackson  Extended fact finding Example: What are the sonnets by Shakespeare  Exploration of availability Example: Is there new work in process control published by IEEE  Open ended browsing and problem analysis Is there new research on the use of cell phones in China

3. 3 Information Search and Visualization  Searching in Text Documents and Database Querying  Google’s Link Based Ranking Measure – PageRank (Brin & Page, 1998) Computes a query independent score for each document Takes into consideration the importance of the pages that point to a given page The big dogs know where to hunt  SQL (database query language) Example: SELECT DOCUMENT# FROM JOURNAL = MY_FAVORITE_JOURNAL WHERE (DATE > 2001 AND DATE <= 2003) AND (LANGUAGE = ENGLISH) AND (PUBLISHER = HFES OR ACM)  Natural Language Queries Mainly just eliminates frequent terms

4. 4 Information Search and Visualization  Searching in Text Documents and Database Querying  Form-Fillin Queries (http://thomas.loc.gov/)http://thomas.loc.gov

5. 5 Information Search and Visualization  Searching in Text Documents and Database Querying  Phases of search Formulation: expressing the search Initiation of action: launching the search Review of results: reading messages and outcomes Refinement: formulating the next step Use: compiling or disseminating information

6. 6 Information Search and Visualization  Searching in Text Documents and Database Querying  Formulation Identify the source of the information (e.g., within a specific library) Use fields to limit the search (e.g., year or language) Recognize phrases to allow entry of names (e.g., Abraham Lincoln) –Allow for search my phrase or individual items in the phrase Apply variants to relax the search constraints –Case sensitivity (JEFFERSON, Jefferson) –Stemming (sing, singing) –Partial matches (biology, psychobiology, sociobiology) –Phonetic variations (Smith, Smyth, Smythe) –Abbreviations (ATT, NCR) –Synonyms (West Coast retrieves Washington, Oregon and California)

7. 7 Information Search and Visualization  Searching in Text Documents and Database Querying  Formulation

8. 8 Information Search and Visualization  Searching in Text Documents and Database Querying  Initiation of Action Explicit initiation (e.g., search button) Implicit initiation: each change to a component of the formulation phase immediately produces a new set of search results (e.g., Google)

9. 9 Information Search and Visualization  Searching in Text Documents and Database Querying  Review of Results Users can read messages and view textual lists Allow the user to control –The number of results –Which fields are displayed –The sequence of the results –How results are clustered

10. 10 Information Search and Visualization  Searching in Text Documents and Database Querying  Review of Results Clustering

11. 11 Information Search and Visualization  Searching in Text Documents and Database Querying  Review of Results User control

12. 12 Information Search and Visualization  Searching in Text Documents and Database Querying  Refinement In the event of few results, indicate that using fewer search criteria, or partial matches may increase the number of hits Suggested spellings If no results are found, always provide users with that information

13. 13 Information Search and Visualization  Searching in Text Documents and Database Querying  Use Results Merge, save, distributed via email, output to visualization programs, or statistical tools

14. 14 Information Search and Visualization  Multimedia Document Searches  Most systems used to locate images, video, sound and animation depend on metadata  Example: search of a photo library by date, photographer or text captions Requires significant human effort to add captions and annotate  Image search: query by image content  Map search Search by latitude and longitude Search by features (e.g., search for all cities in northwest United States with airports)

15. 15 Information Search and Visualization  Picasa  Supports browse and search of photos in public albums  Automatically organizes the user’s online photo collection based to who's in each picture

16. 16 Information Search and Visualization  Other Searching Mechanisms  Sound Search – Music-information retrieval (MIR) Users can play or sing as input, and matching songs will be returned  Video Search Segment into scenes Allow scene skipping  Animation Search Examples: search for morphing faces

17. 17 Information Search and Visualization  Video Search  Informedia  Designed at CMU to solve the problem of searching huge collections of video and audio recordings  Developed new approaches for automated video and audio indexing, navigation, visualization, search  Provides full-content search and retrieval of current and past TV and radio news and documentary broadcasts.  Generates various summaries for each story segment: headlines, filmstrip story-boards and video-skims

18. 18 Information Search and Visualization  Video Search - Informedia  Example: 12 documents returned for "El Niño" query along with different multimedia abstractions from certain documents

19. 19 Information Search and Visualization  Advanced Filtering and Search Interfaces  Filtering with complex Boolean queries Example: List all employees who live in Denver and Detroit Would most likely result in a null result since “and” implies intersection Most employees do not live in both locations Other approaches –Venn Diagrams –Decision Tables –Metaphors of water flowing through a series of filters  Automatic Filtering Selective dissemination of information Filtering email before it is placed in the Inbox

20. 20 Information Search and Visualization Decision Table

21. 21 Information Search and Visualization  Advanced Filtering and Search Interfaces  Dynamic queries Uses direct manipulation objects http://www.bluenile.com/build-your- own-diamond- ring?first_step=diamond&forceStep= DIAMONDS_STEP

22. 22 Information Search and Visualization  Advanced Filtering and Search Interfaces  Metadata search (e.g., Flamenco) Attribute values are selected by the user http://flamenco.berkeley.edu/demos.html

23. 23 Information Search and Visualization  Advanced Filtering and Search Interfaces  Collaborative Filtering Users work together to define filtering criteria in large information spaces Example: If you ranked five movies highly, the algorithm provides you with a list of other movies that were rated highly by people who liked your five movies  Visual Searches Examples: Selecting dates on calendars or seats from a plane image

24. 24 Information Search and Visualization  Advanced Filtering and Search Interfaces http://www.mediabistro.com/10000words/what-is-a-treemap-5- examples-and-how-you-can-create-one_b736

25. 25 Information Search and Visualization  Information Visualization  The use of interactive visual representations of abstract data to amplify cognition  Scientific Visualization: requires two dimensions because typical questions involve Continuous variables Volumes  Information Visualization involve Categorical variables Discovery of patterns Trends Clusters Outliers Gaps in data http://www.youtube.com/watch?v=xekEXM0Vonc

26. 26 Information Search and Visualization  Information Visualization  Uses human perceptual abilities to make discoveries, decisions and propose explanations  Users can scan, recognize and recall images quickly  Users can detect changes in size, color, shape, movement and texture  IV Rule Overview first Zoom and filter Details on demand http://www.youtube.com/watch?feature=fvwrel&v=RgA4aaEfgPQ &NR=1 http://www.google.com/publicdata/ directory

27. 27 Information Search and Visualization  Information Visualization  1D Linear Data Text documents, dictionaries Organized sequentially Example: view 4000 lines of code Newest lines are in red, oldest lines in blue Browser window shows code overview and detail window

28. 28 Information Search and Visualization  Information Visualization  1D Linear Data All the words in Alice in Wonderland, arranged in an arc, starting at 12:00 Lines are drawn around the outside, words around the inside Words that appear more often are brighter

29. 29 Information Search and Visualization  Information Visualization  2D Map Data Planar data include geographic maps Each item has task domain attributes, (e.g., name) Each item has interface features (e.g., size or color) User tasks (find adjacent items, regions containing items, paths between items Proximity indicates similarity of topics Height reflects the number of documents

30. 30 Information Search and Visualization  Information Visualization  3D World Data Real world objects – molecules, human body, buildings and the relationships between the objects Users work with continuous variables (e.g., temperature) http://www.youtube.com/watch?v=8Ez6UQ69iQ0 http://www.youtube.com/watch?v=rcuq2 eyuqHQ&feature=autoplay&list=ULOnY SHQumfro&playnext=1 http://www.youtube.com/watch?v=jbkSRLYSojo

31. 31 Information Search and Visualization  Information Visualization  Multidimensional data  Extracted data from statistical databases  Tasks include finding patterns, correlations between pairs of variables, clusters, gaps and outliers www.inxight.com Example of listing of houses for sale Spreadsheet metaphor

32. 32 Information Search and Visualization  Information Visualization  Multidimensional data Hierarchical or k-means clustering to identify similar items Hierarchical: identifies close pairs of items and forms ever- larger clusters until every point is included in the cluster K-means: starts when users specify how many clusters to create, then the algorithm places every item into the most appropriate cluster http://www.cs.umd.edu/hcil/bioinfovis/hce. shtmlhttp://www.cs.umd.edu/hcil/bioinfovis/hce. shtml Example: hierarchical clustering of gene expression data Identifying clusters of genes that are activated with malignant as opposed to benign melanoma (skin cancer)

33. 33 Information Search and Visualization  Information Visualization  Temporal Data  Illnesses, Vaccinations, Surgeries, Lab Results  Events have a start/end time, and items may overlap  Tasks: finding all events before, after or during some time period or moment www.cs.umd.edu/hcil/lifelines Example: Patient Medical Record

34. 34 Information Search and Visualization  Information Visualization  Tree Data Collection of items where each item has a link to one parent item Example: Organization Chart

35. 35 Information Search and Visualization  Information Visualization  Tree Data Hyperbolic Tree Structure Limit the number of nodes in the center of the UI

36. 36 Information Search and Visualization  Information Visualization  TreeMap Each rectangle represents a stock and are organized by industry groups The rectangle is proportional to the market capitalization The color indicates gain/loss “N” indicates a link to a news story Map of the Market http://www.marketwatch.com/tools/stockresear ch/marketmap http://www.marketwatch.com/tools/stockresear ch/marketmap

37. 37 Information Search and Visualization  Information Visualization  Social Network Data When items are linked to an arbitrary number of other items Users often want to know the shortest or least costly path connecting two items Facebook Data Visualization tools http://www.toprankblog.com/2010/08/6- facebook-search-engine-data-visualization- tools/

38. 38 Information Search and Visualization  Information Visualization  Facebook: Social Graph  Facebook: Friend Wheel

39. 39 Information Search and Visualization  Information Visualization  Parallel Coordinates

40. 40 Information Search and Visualization  Star Plots

41. 41 Information Search and Visualization  Information Visualization  Overview Task Users can get a overview of the entire collection Zoom Detail View  Filter Task Users can filter-out items that are not of interest  Details-on-demand Task Users can select an item or group to set details  Relate Task Users can relate items or groups within a collection Show relationships by proximity, containment, connection or color coding

42. 42 Information Search and Visualization  Information Visualization  History Task Supports undo, replay and progressive refinement  Extract Task Allows extraction of sub-collections Send items are obtained –Save –Email –Insert to a statistical package

43. 43 Information Search and Visualization  Periodic table of data visualization methods  Web Site Web Site