A 3D Analytic Framework for Visual Data Discovery

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The final word in decision making belongs to humans. To simplify understanding of machine algorithms outputs or arbitrary data sets, we need to take advantage of the most advanced of all the human cognitive systems – the visual cortex. Our version of the Visual Data Discovery Framework attempts to do just that.To visually present different types of data, we employ a Unified Visual Data Representation Space (UVDRS), where a data set is visually represented as a set of objects and relations between them. This is very high level of abstraction and it let us represent almost any data set. We discover data “insight” by finding meaningful relations in data sets together with maximizing amount of information about data objects inside the same screen space.

The UVDRS design was prompted by searching for potential linkages between even the most unthinkable relations. Initial studies of transformative discoveries such as Nobel Prize winning discoveries are particularly promising.

To further simplify visual recognition of the relative strength of relations, we moved our Discovery Framework to a 3D space where we can:

  • Visually represent the relative strength of relations
  • Simultaneously explore bigger number of relations and objects
  • Use the unique 3D space rotation capabilities in order to:
    • overview whole relational volume, concentrating on objects and relation details
    • to “literally” see different sides of 3D objects in order to collect a multifaceted object’s information
  • In case of complex objects representing data clusters or hierarchies we can
    • zoom-in inside cluster’s UVDRS or
    • traverse UVDRS hierarchies to find strong or in range relational dependencies
  • For objects with multiple attributes
    • we can explore relations independently per each attribute’s space or
    • use weighted multi-attribute spaces.

In some cases the UVDRS is the natural abstract data representation, especially when it comes to financial portfolio visual modelling. It even has a commonly used name: visualization of portfolio diversification.

There is one other important application – it is very difficult to detect, explain or predict System Level Events composed of multiple simultaneous local events and involving multiple relations amongst multiple distributed domain objects. The financial “bubbles” are examples of such events. We used our framework to visualize the stock market crash of October 1987.

The development of UVDRS has resulted in one patent granted: US 8,423,445 B2 (2013). A second one is pending: #13784611.

 

Grand Challenge: Unified Visual Data Representation

by Edward Rotenberg, PhD
wired.com, June 19, 2014

All creatures have the ability to sense the surrounding world, but in various ways and degrees. You might envy the bloodhound’s exceptional nose, but humans possess visual prowess that (although it doesn’t match the eagle’s eye in distance) is unsurpassed in the ability to detect and make sense of patterns. Our eyes and brains work as a team to discover meaningful patterns that help us make sense of the world [1].Digital computers take input in direct quantitative form constructed from digits. Human extract most of quantitative information from 3D visual environment: distances between observable objects, sizes of objects, colors intensity and hue, proximity, similarity, symmetry … “A striking fact about human cognition is that we like to process quantitative information in graphic form” [2].A pattern recognition (sense-making) stage comes after low-level extraction stage of human visual perception.A relatively recent in human history process of visual symbolic information reading (e.g. letters & numbers) and their derivatives (text, tables, etc.) severely limit the amount of quantitative information extracted from this visual input. Therefore we disable substantial part of information directed to pattern recognition (sense-making) stage.

This is why Graphs (e.g. line chart, bar chart, etc.) are very powerful tools for understanding digital data: they mimic human visual environment by encoding digital data as locations, distances, sizes, colors, etc., therefore enabling power of human standard pattern recognition (sense-making) process. In short, the following information conversions sequence is taking place:

Quantitative Information in Pure Symbolic Form –> Graphic Representation –> Extraction of Quantitative Information from Graphic Representation –> Pattern Recognition

Do the same by using derivatives of symbolic representation (e.g. tables, text) makes sense-making difficult and sometimes impossible (cases of medium to large amount of symbolic data) due to limitation of human cognitive capabilities to memorize symbolic information. This is why presenting information in graphic form or information visualization is so important for its understanding.

The Grand Challenge

The exponential technological development created overwhelming amounts of disparate, conflicting, and dynamic information and, therefore, huge needs to analyze and understand information. Despite vast amount of newly created effective digital algorithms for information analysis, attempts to completely remove human from a decision loop been unsuccessful. Altogether overwhelming analyzing needs, deficiency of automated algorithms and previous visualization methods created enormous demands for information visualization and visual analytics.

You can find short review of information visualization and visual analytics in attached review [4], MILESTONES part (page 3) and in research agenda [3]. Different Charts or visual representations correspond to different data types and designed to solve specific problems. But how to combine vast amounts of disparate data types together in unified visual representation suitable for discovery and satisfying visual information seeking mantra?

“The holy grail of information visualization is for users to gain insights. In general, the notion of insight is broadly defined, including unexpected discoveries, a deepened understanding, a new way of thinking, eureka-like experiences, and other intellectual breakthroughs”[4]. To make search for insight feasible between other requirements, we must have:

  • A) Reduce the search per se, such as by representing a large amount of data in a small space [3]
  • B) Create methods to synthesize information of different types into a unified VISUAL data representation (VUDR) [3]
  • C) Create a new science of interaction to support visual analytic [1]. Part of this challenge required increase level of dynamism in presenting relevant information; create synchronous multi-level interaction space that enhances human association capabilities.

Data transition B) to VUDR cannot be done without moving to high level of abstraction. It will compact the data and positively affect A). Then interaction C) will be done on high level of abstraction. It will let to have synchronous high capacity multi-level information presentation from high abstraction level to connected low levels including raw data.

Therefore the critical to the above is to find a solution of challenge B) or to find unified visual data representation (VUDR).

Interestingly VUDR design can be prompted by the process of finding insight. Let’s consider the following: the burst of recognition often happened then one arrives at insights by linking previously unconnected thoughts. The theory is computational and it is possible to formulate the search for insights as a problem of searching for the potential linkage between even the most unthinkable relations. Initial studies of transformative discoveries such as Nobel Prize winning discoveries are particularly promising. This approach is particularly relevant to visual analytics and insight-based evaluative studies because they can characterize insightful patterns in terms of structural and temporal properties [4][5].

References

[1] Stephen Few: “Visual Pattern Recognition”, COGNOS, Innovation Center, 2006.
[2]Pinker, S.:“A theory of graph comprehension”; In R. Freedle (Ed.) Artificial intelligence and the future of testing, (pp. 73–126). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., 1990.
[3] “Illuminating the Path: The Research and Development Agenda for Visual Analytic”. January 1, 2005. James J. Thomas (editor), Kristin A. Cook (editor).
[4] Chaomei Chen: “Information visualization”, 2010 John Wiley & Sons, Inc. WIREs Comp, Stat 2010 2 387–403.
[5]Chen, C. | Chen, Y. | Horowitz, M. | Hou, H. | Liu, Z. | Pellegrino, D : “Towards an explanatory and computational theory of scientific discovery” ; Journal of Informetrics, Volume 3, Issue 3, July 2009, Pages 191-209.