InfoVis:Wiki - User contributions [en]

Focus-plus-Context

2007-04-11T21:46:45Z

200.238.102.170:

{{Definition|The basic idea with '''focus

Data Visualization Links

2007-04-11T17:51:30Z

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*[http://www.edwardtufte.com/tufte/ The Work of Edward Tufte and Graphics Press]
*[http://www.math.yorku.ca/SCS/Gallery/ Gallery of Data Visualization] The Best and Worst of Statistical Graphics
*[http://www.dmreview.com/portals/portal.cfm?topicId=230006 Data Visualization Portal] DM Review Magazine
*[http://www.phil.uni-sb.de/FR/Medienzentrum/Grafikexperiment/verweise.html Verweise zu "Graphische Präsentation von Daten"] List of links and articles related to the graphical representation of data
*[http://www.b-eye-network.com/newsletters/ben/2370 Recommendations for Your Data Visualization Bookshelf] (Article by Stephen Few)
*[http://gapminder.org/index.html Gapminder] Gapminder is a non-profit venture for development and provision of free software that visualise human development.
*[http://www.google.com/coop/cse?cx=015508547728725058312:e72-8qt96bi Data Visualization Search] Search engine for everything Data Visualization. Narrows down searches to known good sources. - Please contribute links to search.

== Web Services ==
*[http://services.alphaworks.ibm.com/manyeyes/app Many Eyes] for shared visualization and discovery
*[http://www.data360.org/ Data360] A platform for storing, tracking and reporting your data.
*[http://www.swivel.com/ Swivel] Swivel is a Web site for curious people to explore data.

== Articles ==
* [http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html Tufte Design Principles] 
* [http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920 Elegance Through Simplicity] Article about redesigning graphs (by Stephen Few)
* [http://home.ched.coventry.ac.uk/Volume/vol0/intro.htm DISCUSS Charts and Graphs - Introduction] Good Graphs, Bad Graphs, Ugly Graphs
* [http://home.ched.coventry.ac.uk/Volume/vol0/philosop.htm DISCUSS Charts and Graphs - Philosophy] Tell the Truth, Design Variation, Data Ink, Data Density
* [http://www.intelligententerprise.com/showArticle.jhtml?articleID=22102226 The Information Cannot Speak for Itself] Article about about effective data presentation (by Stephen Few) 
* [http://www.intelligententerprise.com/showArticle.jhtml?articleID=26100530 Common Mistakes in Data Presentation] (by Stephen Few)
* [http://www.intelligententerprise.com/showArticle.jhtml?articleID=31400009 Tapping the Power of Visual Perception] Why do people respond to some forms of presentation better than others? (by Stephen Few)
* [http://www.intelligententerprise.com/showArticle.jhtml?articleID=46800517 Eenie, Meenie, Minie, Moe: Selecting the Right Graph for Your Message] Don't rely on arbitrary decisions to pick your data presentation method. By categorizing your message type and applying the correct design solutions, you can remove all the guesswork.(by Stephen Few)
* [http://www.masterviews.com/2001/12/15/what_is_the_dataink_ratio.htm What is the data-ink ratio?]
* [http://www.washington.edu/computing/training/560/zz-tufte.html Graphics and Web Design Based on Edward Tufte's Principles]
* [http://researchweb.watson.ibm.com/dx/proceedings/pravda/truevis.htm How NOT to Lie with Visualization]
*[http://www.dmreview.com/article_sub.cfm?articleId=1023904 Data Visualization: ''Quantitative vs. Categorical Data: A Difference Worth Knowing''] Column published in DM Review Magazine, April 2005 Issue By Stephen Few
*[http://www.dmreview.com/article_sub.cfm?articleID=1018118 Data Visualization: ''Grid Lines in Graphs are Rarely Useful''] Column published in DM Review Magazine, February 2005 Issue By Stephen Few
*[http://www.dmreview.com/article_sub.cfm?articleId=1016296 Data Visualization: ''Bad Graphs: The Stealth Virus''] Column published in DM Review Magazine, January 2005 Issue By Stephen Few
*[http://www.ncsu.edu/labwrite/res/gh/gh-tables.html Designing Tables] NC State University LabWrite Resources
*[http://www.ncsu.edu/labwrite/res/gh/gh-graphtype.html Graph Types] Selecting a Graph Type, NC State University LabWrite Resources
*[http://www.ncsu.edu/labwrite/res/gh/gh-bargraph.html Bar Graphs, Histograms] NC State University LabWrite Resources
*[http://www.ncsu.edu/labwrite/res/gh/gh-linegraph.html Line Graphs and Scatter Plots] NC State University LabWrite Resources
*[http://www.ncsu.edu/labwrite/res/visualrev/res-visualrev.html Revising your Visuals] NC State University LabWrite Resources
*[http://www.ncsu.edu/labwrite/res/gt/gt-stat-home.html Using Error Bars in your Graph] NC State University LabWrite Resources
*[http://www.b-eye-network.com/view/2674 The Surest Path to Visual Discovery] by Stephen Few illustrating the [[Visual Information-Seeking Mantra]]
*[http://www.b-eye-network.com/view/2470 BizViz: The Power of Visual Business Intelligence] by Stephen Few about a few simple data analysis techniques using Spotfire
*[http://www.b-eye-network.com/view/2208 Data Visualization: Rules for Encoding Values in Graph] by Stephen Few
*[http://researchvault.sourcemedia.com/paperview.html?sitecode=DM

Tasks Taxonomy for Graphs

2007-04-11T14:53:23Z

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== Low-Level Tasks ==

From [Amar et al. 2005] and [Lee et al 2006].

* General Tasks
{|border="1"
|-
|'''Task'''||'''Description'''
|-
|Retrieve Value ||Given a set of cases, find attributes of those cases.
|-
|Filter||Given some conditions on attributes values, find data cases satisfying those conditions.
|-
|Compute Derived Value||Given a set of data cases, compute an aggregate numeric representation of those data cases.(e.g. average, median, and count)
|-
|Find Extremum||Find data cases possessing an extreme value of an attribute over its range within the data set.
|-
|Sort||Given a set of data cases, rank them according to some ordinal metric.
Determine Range Given a set of data cases and an attribute of interest, find the span of values within the set.
|-
|Characterize Distribution||Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the set.
|-
|Find Anomalies||Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.
|-
|Cluster||Given a set of data cases, find clusters of similar attribute values.
|-
|Correlate||Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.
|-
|Scan|| Quickly review a set of items.
|-
|Set Operation|| Given multiple sets of items, perform set operations on them. For example, find the intersection of the set of nodes.
|}

* Graph Specific Tasks
{|border="1"
|-
|'''Task'''||'''Description'''
|-
|Find Adjacent Nodes ||Given a node, find its adjacent nodes.
|}

== Graph Task Taxonomy ==

Examples are illustrated using 4 types of graphs:
*(FOAF): friend-of-a-friend
*(FW): food web
*(GO): gene ontology
*(ARM): airport routing map

=== Topology-based Tasks ===

{| border="1"
|-
|'''Task'''||'''Description''' || '''Examples'''
|-
| Adjacency (direct connection)
|
* Find the set of nodes adjacent to a node?
* How many nodes are adjacent to a node?
* Which node has a maximum number of adjacent nodes?
||
*(FOAF) Find the names of the direct friends of Eric.
*(FW) How many kinds of organisms do golden eagles eat?
*(FOAF) Who is the most popular person?
|-
| Accessibility (direct or indirect connection)
|
* Find the set of nodes accessible from a node.
* How many nodes are accessible from a node?
* Find the set of nodes accessible from a node where the distance is less than or equal to n.
* How many nodes are accessible from a node where the distance is less than or equal to n?
||
*(FOAF) Who are your friends, your friends’ friends, and so on?
*(FOAF) How many friends are you connected to in this way?
*(ARM) To what cities can we go from Seoul, Korea by changing planes only once?
|-
| Commmon Connection
|
* Given nodes, find a set of nodes that are connected to all of them.
||
*(FOAF)Find all the people who know both John and Jack.
|-
| Connectivty
|
*Find the shortest path between two nodes.
*Identify clusters.
*Identify connected components.
*Find bridges.
*Find articulation points.
||
*(ARM) What is the shortest path from Seoul, Korea to Athens, Greece?
*(FOAF) Count the number of clusters.
*(FW) There may be subgraphs independent of each other. Count the number of connected components in the graph.
*(FOAF) Who is the person whose removal from the graph results in an unconnected graph?
*(FW) Which is the eating link whose removal from the graph results in an unconnected graph?
|}

=== Attribute-based Tasks ===

{| border="1"
|-
|'''Task'''||'''Description''' || '''Examples'''
|-
| On the Nodes
|
* Find the nodes having a specific attribute value.
* Review the set of nodes.
||
*(FOAF) Who do you know from the people currently shown on screen?
*(FOAF) How many people do you know from the ones currently shown on screen?
*(FOAF) Are there any foreign-sounding names?
|-
| On the Links
|
* Given a node, find the nodes connected only by certain types of links.
* Which node is connected by a link having the largest/smallest value?
||
*(GO) Find the nodes connected by “is-a” relationships from the “Biological Process” node.
*(FW) If a link has an attribute representing the percentage of the diet, what is main food of American crow?
|}

=== Browsing Tasks ===
{| border="1"
|-
|'''Task'''||'''Description''' || '''Examples'''
|-
| Follow Path
|
* Follow a given path
||
* (FOAF) A user looks into A’s friend B, B’s friend C, and C’s friend D.
* (FW) Follow the flow of energy from grasses, to a rabbit that eats grass, to a carnivore that eats the rabbit, and to a carnivore that eats that carnivore.
|-
| Revisit
|
* Return to a previously visited node.
||
*(FOAF) After they follow a path in the above task, they may want to see A’s other friends.
*(FW) Find another carnivore that eats the same rabbit.
|}

=== Overview Tasks ===
This is a compound exploratory task to get estimated values quickly. For example, we might ask users to estimate the size of the social network. Note that sometimes it is more important to be able to estimate the answer than to get an accurate one. Some of the topology tasks can be done easily using an overview of the graph as well. For example, using particular layout algorithms, it is easy to see whether or not there are clusters and connected components. Other algorithms help to find shortest paths between nodes. Overview also helps to find patterns.

Examples:
* estimate size of the network
* estimate the number of connected components
* is the network clustered?
* can you identify different patterns of connection?
* (FOAF) has the network a small-world structure?

== High-Level Tasks ==

High-Level tasks which are not a combination of lower level tasks.

* When we compare two or more food webs, we can ask the following questions: What do they have in common? What are the differences among those food webs? Is there any missing or conflicting information?
* Due to errors in the data, several nodes may represent the same entity. For example, the co-authorship graphs often have duplicate author nodes. One important task is to identify whether two or more nodes represent the same person.
* How has the graph changed over time?

== Other tasks ==

There seems to be a set of tasks in the world that match very few of these, but show up often. I welcome others' ideas of how to categorize them:

"what is the general structure of this graph?"
http://www.networkweaving.com/blog/2006/09/nola-networks.html

"what is the distribution of vertex degree in this graph?" (That is, "how are well-linked nodes different from under-linked nodes?")
http://research.microsoft.com/research/pubs/view.aspx?type=Publication

HomeFinder

2007-04-11T12:26:34Z

200.238.102.170:

[[Category:Techniques]]
== Authors ==
* [[Williamson, Christopher]]
* [[Shneiderman, Ben]]

== Short description ==
The dynamic Homfinder was created to search and visualize Databases. As the name implies, the first use case scenario was a home finding application, where the user was able to find real estates, by adjusting graphical widgets (sliders, ...).

== Advantages using dynamic Homfinder ==
* Continuous update of the query results
* Fully visual interface (no query language needed)
* Reversible operations
* Immediate results
* almost no training needed

== Disadvantages using dynamic Homefinder ==
* complex dynamic Queries need a lot of screen real-estate
* underlying data structures need to support rapid querying

== Suitable Datatypes ==
* data should be graphical representable
* slider widgets rely on numerical data

== Interaction ==
* multiple slider interface
* buttons
* dynamic map display that displays house location
* single view

== Example and use cases ==
Dynamic queries allow users to formulate queries by adjusting graphical widgets, such as slider, and see the results immediately.
By providing a graphical visualization of the database and search results. Users can easliy change the parameters for search.
The interfaces were used to explore a real-estate database and find homes meeting specific search criteria.

[[Image:Homefinder.gif]] Userinterface of the Home Finder [Ben Shneiderman (HCIL), Christopher Williamson (student at HCIL)]

Today many Webpages uses the HomeFinder for their costumers to find houses for their needs.

== Design issues ==
'''* Graphical silders:''' Each slider consists of a label, a field indicating its current value, a slider bar with a drag box and a value at each end of the slider bar indicating minimum and maxim um values. Sliding the drag box with the mouse changes the value of the slider. Clicking with the mouse on the slider bar increases or decreases the value one step at a time.

'''* Graphical database illustration:''' The database is represented on the screen in graphical form. The user give the input for the programm with sliders for attributes like the number of rooms, price and distance of locations. The Dynamic Queries uses this parameters to find houses, which fullfil these conditions. The search result of the query can be highlighted by coloring, changing points of light, marking of regions, or blinking.

[[Image:HomeFinderSlider.gif]] A Slider of HomeFinder [Williamson, C. and Shneiderman, B. (Jan. 1992)]

==Internal Links==
*[[Dynamic query]]

== References ==
*[ACM, 1992] ACM, I ACM Press New York, http://portal.acm.org/citation.cfm?id=133216

Perspective Wall

2007-04-11T06:27:53Z

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== Authors ==
*[[Robertson,_George|George Robertson]] 
*[[Mackinlay,_Jock_D.|Jock D. Mackinlay]] 
*[[Card,_Stuart|Stuart K. Card]] 

== Short description ==
{{Quotation|Tasks that involve large information spaces overwhelm workspaces that do not support efficient use of space and time. For example, case studies indicate that information often contains linear components, which can result in 2D layouts with wide, inefficient aspect ratios. The paper describes a technique called the Perspective Wall for visualizing linear information by smoothly integrating detailed and contextual views. The Perspective Wall technique takes advantage of hardware support for 3D interactive animation to imitate the architecture of the eye system. It folds a 2D layout into a 3D wall that smoothly integrates a region for viewing details with perspective regions for viewing context. This intuitive distortion of the layout provides efficient space utilization and allows smooth transitions of views. Analysis of the Perspective Wall technique indicates a threefold improvement over simple 2D visualizations.The resulting visualization supports efficient use of space and time.| [George Robertson et al., 1991]}}

== Figures ==
[[Image:Perspective wall.jpg|200px]]

== External Links ==
* [[http://portal.acm.org/citation.cfm?id=108870 paper relating the perspective wall of Robertson et al.]]
* [[http://people.cs.vt.edu/~north/infoviz/PerspectiveWall.ppt#1 short presentation of perspective walls]]
* [[http://www.inf.ethz.ch/personal/lombardo/archives/da/node6.html examples]]
== References ==
* [Robertson et al., 1991] [[Robertson,_George|George Robertson]], [[Mackinlay,_Jock_D.|Jock D. Mackinlay]], [[Card,_Stuart|Stuart Card]]. The Perspective Wall: Detail And Context Smoothly Integrated. In ''Proceedings of CHI '91 Conference'', pages 173--179, April 28 - June 5, 1991, New Orleans, Louisiana, June 1991. Association for Computing Machinery.
[[Category:Techniques]]

2006-08-01: Job openings at IBM T, J. Watson: Research Staff Member

2007-04-11T03:01:09Z

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==Research Staff Member (RSM) (JOB ID: B019410)==

The Intelligent Multimedia Interaction (IMI) group at IBM T. J. Watson
Research is seeking applicants for RSM positions. The IMI group
conducts research exploring intelligent multimodal interfaces for
information seeking and information analysis. We have developed a
number of algorithms and technologies aimed at enabling a
full-fledged, mixed-initiative multimodal/multimedia human-computer
interaction for information search and analysis.

We are currently seeking qualified researchers whose research
interests overlap with our efforts in the following areas:

'''Context-sensitive information retrieval and analysis''': design and
develop algorithms and software components that can help users to
search and analyze information in context. We are particularly
interested in candidates who have research experience in using user
interaction context and data characteristics (e.g., meta data and data
semantics) to help improve information retrieval (e.g., the ranking of
retrieved documents), information integration (e.g., combining
retrieved database data and text documents), and information
presentation (e.g., visualizing relationships of retrieved data).

'''Visual analytics''': design and develop interactive, visual illustrative
and visual query metaphors and tools to aid in fast information
exploration and information analysis. In this endeavor, we are
exploring the use of context-aware visual interfaces that combine the
human reasoning ability with the modern computer power to speed data
exploration and knowledge discovery.

'''Adaptive multimedia information presentation and summary''': design and
develop algorithms and tools to compose effective visualization of
heterogeneous information (e.g., structured data, text, and images) on
the fly for dynamic information analysis. We are currently focusing on
adaptive, user-system cooperative design of visual presentations. In
this effort, both users and the system can recommend and compose
suitable visualization formalisms, and the system can learn users?
preferences over time.

'''Adaptive multimodal interpretation and recommendation''': design and
implement algorithms to robustly interpret a user?s multimodal
requests specified in natural language, GUI, or gesture. We are
exploring the use of machine learning approaches to learn a user?s new
multimodal expressions on the fly. We also explore context-sensitive
recommendation strategies to interactively help users to author
complex requests (e.g., information analysis requests) and recover
from interpretation problems.

We are especially seeking candidates with research interests in
information retrieval, information visualization/graphics (e.g.,
non-photorealistic rendering for information depiction), natural
language processing, machine learning, human-computer interaction, and
intelligent user interfaces.

Our group is multi-disciplinary, with experts from fields ranging from
intelligent user interaction and information visualization, to natural
language processing and generation. The complimentary nature of our
skills creates an exciting environment for research and exploration.

===Qualifications ===

Candidates for this position must have an excellent research track record
in one or more above areas. The candidates must also have extensive
experience with designing, developing, and testing large
object-oriented software systems (e.g., Java and C ) and programming
in heterogeneous networking environment (e.g., Linux and Windows).

===Skills ===

*5 year C/C experience
*5 years Java experience
*5 years programming experience with TCP/IP and thorough knowledge of communication protocols
*1 year web programming experience
System administration experience and skills strongly preferred

===Educational level===

Ph.D. in CS or relevant field required. Preference will be given to
candidates who have experience in conducting or willing to engage in
interdisciplinary and collaborative research.

For more information, or to apply, please contact us imi(at)us.ibm.com.

''via [https://lists.cs.wisc.edu/mailman/listinfo/dbworld Dbworld mailing list] by Michelle Zhou''

[[Category:News]][[Category:2006/08]]

User:Mtindia

2007-04-11T02:01:33Z

200.238.102.170:

I am a Librarian and specialize in knowledge management, information visualization, data mining, etcetera.

----My article is on [http://www.infovis-wiki.net/index.php/Library_catalogs Library Catalogs].
----See also [http://en.wikipedia.org/wiki/User:Mtindia My contributions @ Wikipedia].
<h2>Categories of my interest are:</h2> [[Information visualization]], [[http://www.infovis-wiki.net/index.php/Knowledge_Visualization Knowledge visualization]], [[data visualization]] [[http://www.infovis-wiki.net/index.php/Scientific_Visualization Scientific visualization]] [[Meta data]] [[http://www.infovis-wiki.net/index.php/Special:Search?search=Data Mining

2005-07-27: Call for Papers for "Visual Analytics" Special Issue of IEEE TVCG

2007-04-11T00:29:08Z

200.238.102.170:

== CALL FOR PAPERS '''IEEE Transactions on Visualization and Computer Graphics (TVCG) "Visual Analytics" Special Issue in Late 2006''' ==

IEEE Transaction on Visualization and Computer Graphics seeks original manuscripts for a Special Issue on Visual Analytics, scheduled to appear in late 2006.

Visual Analytics is the science of analytical reasoning supported by the highly interactive visual interface. People use visual analytics tools and techniques to synthesize information; derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessments effectively for action. The issues stimulating this body of research provide a grand challenge in science: turning information overload into the opportunity of the decade.

Visual analytics requires interdisciplinary science beyond traditional scientific and information visualization to include statistics, mathematics, knowledge representation, management and discovery technologies, cognitive and perceptual sciences, decision sciences, and more. An important research agenda to develop the next generation
suite of visual analytics technologies is available at http://nvac.pnl.gov/agenda.stm .

Contributions to the special issue should help develop and/or apply the science of Visual Analytics, clearly showing an interdisciplinary approach. Topics of interest for this special issue include, but are not limited to:

* Science of analytical reasoning including the human analytic discourse, sensemaking methods, perception and cognition, and collaborative visual analytics

* Science of visual representations and interaction techniques including the principles for depicting information, the science of interaction, new visual paradigms, and novel systems and approaches for generating visual analytic visualization and interactions.

* Data representation and transformations including scalable data signatures through statistical and semantic approaches for information (text, imagery, video, Web, ), synthesis of diverse information and data signatures within a high dimensional information space(s) for discovery of relationships, and mixed initiative systems.

* Methods and tools for capturing the analytics process, producing unwrappable information packages, and story telling for specific and widely varying audiences.

* Methods/tools for evaluation, security

Knowledge

2007-04-10T19:04:22Z

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{{Definition|Information given meaning and integrated with other contents of understanding.}}

{{Quotation|What differentiates '''knowledge''' from [[Information]] is the complexity of the experiences that you need to reach it. In order for a set of information to become knowledge one has to be exposed to it in different ways and one has to elaborate a personal experience about it. [...] knowledge can be expressed as a pattern whose measure of interest for the user is above a certain threshold. That is, if some information is not interesting for us it's very difficult for it to become knowledge. Knowledge is not transferable, you have to build it yourself by experiencing the information.|[Dürsteler, 2007]}}

see also: [[Data]], [[Information]], [[Knowledge]], [[Wisdom]]

== References ==
*[Bates, 2005] Marcia J. Bates, [http://informationr.net/ir/10-4/paper239.html Information and Knowledge: An Evolutionary Framework for Information Science]. ''Information Research'', 10(4), July 2005.
*[Dürsteler, 2007] Juan C. Dürsteler, [http://www.infovis.net/printMag.php?num=186

VLC’2007

2007-04-10T15:45:29Z

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*'''Title''': International Workshop on Visual Languages and Computing
*'''Date:''' September 6-8, 2007
*'''Location:''' [[wikipedia:San Francisco Bay |San Francisco Bay]], [[wikipedia:USA|USA]]
*'''Proceedings Publisher:''' Proceedings A selection of the best papers will be invited for subsequent publication in a special issue of the Journal of Visual Languages and Computing
*'''Homepage:''' http://www.ksi.edu/seke/dms07.html

*'''Paper Submission Deadline:''' March 31, 2007

[[Category:Conferences]]