Information Search and Visualization

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

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

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

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

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

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

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. Information Search and Visualization Decision Table

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. 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. 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. 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. 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. 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. 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. 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. 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. 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=jbkSRLYSojo http://www.youtube.com/watch?v=rcuq2eyuqHQ&feature=autoplay&list=ULOnYSHQumfro&playnext=1 http://www.youtube.com/watch?v=8Ez6UQ69iQ0

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

36. Information Search and Visualization Map of the Market http://www.marketwatch.com/tools/stockresearch/marketmap • 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

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. Information Search and Visualization • Information Visualization • Facebook: Social Graph • Facebook: Friend Wheel

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

40. Information Search and Visualization • Star Plots

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