Reaction: INFORMATION VISUALIZATION FOR TEXT ANALYSIS

The chapter gives an overview of a wide array of tools used in the area of Information Visualization of text.

The tools are meant for different purposes which tells us that mining and analysis of text from a source or combined sources has the potential to expose hidden meaning or connections between them. There are some tools which enable you to find connections between different entities scattered across different documents. The TRIST tool is really interesting to see because it supports so many features and views to help to user in textual analysis. This article also goes to the domain of analyzing work frequencies and literature and their citation relationships.

I think this research makes a lot of sense as literature whether it be scientific or philosophical, is present in digital format. So there is more data for these tools to work with. One very important thing to observe is that no tools cited in this article capture context of words. Obviously this is not in the scope of the document but I can see it as a potential extension for some of these tools. It would be interesting to see if these tools can be made intelligent and also can help not just in textual analysis but also in text production for instance. Based on the trends of words and the contexts that they are used in, we should be able to provide the writer with a suggestions of word that he/she can use in their literature.

I think the article is very well supported with illustrations. I found the 2007 State of Union address and the Name voyager examples as really cool. I felt that the paper was well organized especially regarding how the tools were ordered and presented with their application areas.

Reaction:Information Visualization for Text Analysis

The paper presents various types of visualizations for analyzing text. The author's purpose of this paper is to put foth ideas for understanding text collections from an analytical point of view. The paper descirbes the various types of visualizations that can be applied on collection of text. Information visualization effectively deduces relationships between the words of the document and also establishes relationships between two or more documents.

The various visual illustrations and the examples make it easier to understand how one type of visualization differs from than the other.The examples within the same subtopic is also useful in understanding how the purpose of a particular visualization is achieved using different representations. I agree to the authors point of view that since information visualization has a wode non technical audience, it will continue to go in popularity. Overall the paper is quite informative.

Reaction: Jigsaw: Supporting Investigative Analysis through Interactive Visualization

Keeping in trend with the other papers in this set, this paper also aims towards making analysis of the information an easy task. A given text or data is analysed for various reason and using various parameters. Though the conventional use of reports for such analysis is still widely used, it at times might not provide a comprehensive analysis. The analysis using reports might not lead to proper identification of entities, relationship between the entities and other information. Hence in this paper the authors propose a tool called “Jigsaw” which provides an in-depth analysis of the information.

The Jigsaw method as the name suggests first identifies the entities form a text and then relates them with each other just like a jigsaw puzzle. One point which is stressed upon by the authors in this paper is that they still believe and support the use of reports for analysis but propose using Jigsaw as an additional tool for better understanding. Jigsaw provides a multiview perspective to the analyst and represents the data with an interactive visualization. The information is presented using four views which are tabular connections views, semantic graph view, scatter plot view and a text view to provide a perspective to the analyst. The entity action in one view is translated to the other view as an event and represented for analysis. The tool has features which allow the analysts to query or search for keywords in the data and analysts can also draw diagrams while inferring information from the text as Jigsaw integrates with Microsoft OneNote. However the authors state that for proper viewing of the data, Jigsaw might be required to be viewed on different screens which can prove to be an overhead for the tool.

Towards the second half of the paper, the authors discuss about the implementation of Jigsaw. Jigsaw is built using Java and accepts XML as the input. It is designed by following the MVC architecture. But as for visualization, the tool provides the analysed data in terms of list, graph, text and scatter plot. These have been well illustrated in the paper with examples. Thus as it is inferred from the paper, Jigsaw can be primarily used for investigative purpose; the tool can be further worked upon to extend the usage. Also in my opinion, the authors should target at increasing the number of visuals or views in which the data can be presented by the tool.

Reaction: Information visualization for text analysis

The main theme of this paper is to analyse data in a text. The authors have presented this analysis using visualization. What I liked about this paper is that the aim of the work is clearly defined in the start of the paper which includes text mining and use of visualization for the same; visualization of words or phrases and forming concordances of the same and finally visualizing the relationships between words and their usage in the language.

The paper has presented various techniques and all of them correspond to each of the visualization techniques discussed in the class. For text mining, the visualization techniques discussed includes TAKMI text mining system, JIGSAW system, BETA system and triage system. As discussed in the paper, amongst the four systems, even I found the triage system to be effective as a system for visualization. The icons and grouping of related items in this system proves to be effective for analysts while studying the data. The next attempt in this paper is to visualize concordances in the text and word frequencies. Amongst the methods described for this visualization, I liked the Sunburst (modified as the DocuBurst) method the most here. This system gives a good view of the text treemap and effectively links the words in a text. Baby Name Voyager is another example that had been discussed many times in the class and has been pointed as an effective method for visualization of text for analysis. Towards the end of this paper, the author discusses on methods for visualizing the numbers and relations between the citations. It has been observed that many of the works have been used as references and have been cited widely. This visualization discussion helps in analysing the most referred or cited work or field f work across the references. The linked bubble graph used here serves to be useful and helps for the purpose.

Thus this work gives us an overview of using visualization as a tool for analysing the text and data. It has provided us with an example for each of the techniques discussed in the class.

Reaction: INFORMATION VISUALIZATION FOR TEXT ANALYSIS

The paper provides several examples of tools used in text mining and how they try to visualize their results in order to make it easy for understanding the results of text mining. The paper is self describing and easy to read in the sense that I liked the way it is organized into –

1. Visualization for text mining where the author speaks about how visualization the text mining results is becoming a promising tools citing several examples on the way like the TAKMI’s text mining system, BETA system of IBM’s Web Fountain project
2. Visualization in documents and website where Marti explains that word frequencies i.e. concordance visualization along with other methods like tag cloud on websites and theme river on sites earlier covered in class viz. NameVoyager help in understanding text collection
3. Visualization in literature and citations is explained by Marti as though the text is semi-structured can be can help in literary analysis.

I agree with Marti’s view that, visualization has also been applied to online conversations and other forms of social interaction which have textual components and I believe such analysis help in getting acquainted with trends that follow day-to-day activities. IBM’s manyeyes.com should get a special mention in this type of visualization which is done by the author.

I feel that it was a good overall short but precise and upto the point.

Visualizing how a population grows to 7 billion

Quite an interesting video visualizing population growth to 7 billion. -

Source

Meet: lab member Ju Hee Bae previews her InfoVis presentation

Come offer us some feedback on the talk!

Interactive music experience

Check out this site! They have designed an interactive music experience using WebGL. You can click the mouse to burst the bubbles that appear and the beam follows the direction of the your mouse movement.

http://lights.elliegoulding.com/

Reaction: Imaging Vector Fields using Line Integral Convolution

The paper discuss about two main onvolution techniques DDA and LIC,diffrences in approachs, their performance considerations and usage.DDA approximate local vector using straight line whereas LIC relies on a central location from where rendering can be done in diffrent direction.Both the alogrithms have applications in diffrent areas like motion imaging and rendering linear fields. LIC is definately provides better quality but at the cost of performance and speed as LIC is believed to be slower than DDA. Paper is well-organized and supported with examples and images but clear understanding. It also leaves scope for lot of improvements.

Reaction: Imaging Vector Fields Using Line Integral Convolution

The paper starts off with introducing some interesting techniques to image vector fields. It introduces two major techniques DDA and LIC. The former assumes that the local vector can be approximated using a straight line while the latter assumes it starts at the center of a pixel and moves out in positive and negative directions. That is the reason why DDA is able to render linear fields more accurately. Illustrations show that LIC is accurate in displaying textures and vector fields. The paper has supported LIC technique and its output through before and after images.

LIC has the following advantages:

1. Presents data in a more detailed manner.

2. Can removing aliasing efficiently.

3. Different techniques can be interfaced with LIC.

4. LIC can be generalized to higher dimensions.

Results say that LIC is about 10 times slower than DDA. It would be interesting to see how accurate LIC is after techniques to determine their efficiency are invented.

Reaction : Over Two Decades of Integration-Based, Geometric Flow Visualization

This paper acts a pointer to different research areas depending on the reader's interest. The visuals provided in this paper are very interesting and serves as a backbone in understating the areas. The paper is based on geometric flow visualization. It is in fact flooded with the information regarding that.

The jargon's like streamline, streak line are difficult for a novice reader like me. Some sort of description or examples should have been give to those which would have helped me in understanding. The description of various techniques to perform geometric visualization is very impressive. I see that flow visualization has lots of practical usages.

The fact that the last research was done six years back in this field shocked me. It is interesting to know how the challenges like huge amount data can effect the visualization as well how algorithms try to overcome that issue. The differentiation between interactive and automatic seeding is well done. I like the way overall seeding algorithm is presented.

There has been a healthy discussion at every point of the paper. Overall the paper give a good insight about the research done in the last two decades in this field.

Reaction: Toward a Deeper Understanding of the Role of Interaction in Information Visualization

According to author for complete end-user experience in InfoViz systems is driven by two main components:- representation of data using graphics and interaction with end users. Author argues that shifting focus to interaction, which has been overshadowed by representation, can actually overcome limitations of representaion and can improve performance of infoviz systems as a whole.The goal of the paper is to develop a taxonomy of interaction techniques which can be applied at different level of granularity. Authors studied diffrent taxonomies and arrived at a convincing definition of interaction techniques as the features that provide users with the ability to directly or indirectly manipulate and interpret representations.However that does not take into account static images. Affinity diagramming method helped in concluding that representation and interaction techniques are strongly coupled and hence user intent is an appropriate parameter to classify interaction techniques.Further,from their studies, author came up with seven categories of interaction which can lead to efficient and systematic data exploration.Author explains every category with supporting theories and examples like Direct walk is explained with reference to visual thesaurus for exploring data;Reconfiguration examples include SDM and Conetress, Attribute explorer support encoding, also be achieved by color-encoding, drill-down operation in a tree map visualization to depict abstraction is explained through SequoiaView, filtering can be done using dynamic query controls and author also illustrate connect interactions with the help of vizster and name voyager. Overall, its a very informative read and leaves room for improvement by stating "categories are not collectively exhaustive".

Reaction: A Task by Data Type Taxonomy for Information Visualizations

As Marchionini rightly said, "the common goals of Information Visualization reach from finding a narrow set of items in a large collection that satisfy a well-understood information need to developing an understanding of unexpected patterns within the collection". The task of exploring the information collections become increasingly challenging as the volume of the information grows. This paper proposes that the useful starting point for designing advanced graphical user interfaces is the Visual Information-Seeking Mantra: overview first, zoom and filter, then details on demand. The paper offers a task by data type taxonomy with seven data types (one-, two-, three dimensional data, temporal and multi-dimensional data, and tree and network data) and seven tasks(overview, zoom, filter, details-on-demand, relate, history, and extracts).

I agree with the authors on several of their arguments. Though, the above types of data serve the research purposes, for successful commercial use the companies have to come up with several novel data structures and several other new tasks apart from the list mentioned above. There were several novel ways of information exploration tools such as fisheye views, but none of them seem to have sustained over time, as they all appear to be fancy in the beginning, but over repeated usage these features tend to not serving the purpose efficiently.

Reaction: Toward a Deeper Understanding of the Role of Interaction in Information Visualization

The author of this paper has emphasized on a very good point i.e the interaction aspect of information visualization has been overshadowed by the representational aspect.Moreover, representation and interaction both go hand in hand for a successful information visualization. One is incomplete without the other. There still is tremendous amount of scope for research in interaction aspect of information visualization. This paper is an attempt to encourage more research this field.The various interaction techniques have been effectively described.

After tremendous amount of research the authors have classified interaction techniques in seven categories. The content is extremely informative and each of these categories have been explained thoroughly with good illustrations. However, the interaction techniques might straddle between two categories. It is not always possible to classify a particular technique under one category. Interaction is an important aspect of visualization. In order to achieve success, the user intent should be given more weight. In other words, the focus should be on the user intent rather than how a particular technique provided by Info Viz. works.

Reaction:The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations

The paper gives a starting point that will aid the designers of advanced graphical user interfaces. According to the author, the data is classified into a taxonomy of seven different types and the tasks that needs to be performed is again divided into seven types.The author asserts that a better design can be developed to visualize data by taking advantage of the under utilized human perceptual abilities. The article stresses on the visual seeking mantra of overview first, zoom. filter and details on demand.

 Overall the article has been systematically organized by explaining the various data types, its advantages and disadvantages. This is ensued by briefly explaining each component of the visual seeking mantra. It is a very good read for designers of an Information Visualization tool. The bifocal display representation and the filter flow model for dynamic querying was a very good piece of information. According to me, The Visual seeking mantra, if applied in a prudent way will help in increasing the insight of the end user in optimum number of steps.

Questions:
Are the steps 'details on demand' and 'extract' related? I was not able to infer a very clear distinction between them.

Reaction: Toward a Deeper Understanding of the Role of Interaction in Information Visualization

Information visualization =  representation + interaction , I had known this, since reading the first paper so I think that this paper extends the view presented in some of the earlier papers that to attract audience towards the visualization, we need, not only excellent representation of data but also a way to present an interactive system (at higher abstraction of-course!). This paper was informative in the sense that, everyone knows how to interpret data from interactive charts, that are there on scale of millions on the web, but actually there is compartmentalization of this interactions, This was something new that I found out from the paper. It is a known fact, that, with interactive visualizations we can play around with data change it form to other and do many other eye-candy things, but the credit goes to the authors for drawing fine-lines among such various techniques to interact with a viz-system.

I strongly agree that this separation of interactive techniques presented in the paper is not the only way in which we can categorize interactions in visualization. But still it lays a founding stone for others to build upon if they wish to make the line of separation even more wider or smaller according to their way of interpreting this interaction.

Again just like the previous reading of this week, I found this paper to be definitive, when it comes to naming and definition for the techniques of interaction which we all use but when asked to distinguish often leave us stumped and loss of words.

Find: Revealing the Periodic Listening Habits of last.fm Users

Nice data! Really needs labels tho. 

Revealing the Periodic Listening Habits of last.fm Users

Reaction: Attention and Visual Memory in Visualization and computer Graphics

This paper discusses the importance of human perception in visualization. The paper talks about preattentive processing where humans automatically tend to categorize an image into different regions or different properties.

Dr. Healey presents visual examples to understand how they are perceived by humans, based on colors, shapes, boundary margins and the mix of these or other properties. He uses this to form the basis of his discussion in the paper where some features like a unique target or a different boundary are declared to be preattentive. The author describes the various scientific theories that explain why and how such preattentive features are categorized or identified quickly by the human visual system.

I specially liked the theory of feature hierarchy where the author talks about how certain elements help in presenting info without any confusion as some features are more prominent or so very distinct from another that the visual system can easily perceive it. Also, it is very interesting to know about how the human eye searches for color, text and how sometimes it is blind in identification of some changes, how memory plays an important role and how the mood of the person just before seeing the visual or repeated viewing of the visual can change his perception and understanding of the information.

This paper describes the theories and the many factors that affect visualization and perception. I wish it had included details of how a visual/ graph design can incorporate these sensitive issues to present maximum information to the user in a glance.

Tools: Building better web apps with a new Chrome Beta

Chromes native apps API. 

Building better web apps with a new Chrome Beta

Find: Pandora ditches Flash, opens up the HTML5 box for redesign

Pandora chooses html5 over flash: it's faster.

Pandora ditches Flash, opens up the HTML5 box for redesign