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Innovation Management: The Value of Seeing What You Have

If your job is to get your company, team, or community to innovate, you know how organizational forces can make it hard to even try something new. Visualizing the resources available is an effective first step in overcoming some of those organizational forces. Simply being able to see, and show, what you have allows you to make a compelling case for marshaling resources and even spark some initial interactions in that direction.

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How Data Visualization Supports the Formation of Better Hypotheses

Since Exaptive launched in 2011, we’ve worked with many researchers, particularly in medicine and the natural sciences. PubMed®, a medical journal database, pops up repeatedly as a key tool for these researchers to develop hypotheses. It’s a tool built in a search-and-find paradigm with which we’re all familiar. Execute a keyword search. Get a list of results. Visualization can make search - and, therefore, research - much more meaningful.

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Machine Learning Helps Humans Perform Text Analysis

The rise of Big Data created the need for data applications to be able to consume data residing in disparate databases, of wildly differing schema. The traditional approach to performing analytics on this sort of data has been to warehouse it; to move all the data into one place under a common schema so it can be analyzed.

This approach is no longer feasible with the volume of data being produced, the variety of data requiring specific optimized schemas, and the velocity of the creation of new data. A much more promising approach has been based on semantic link data, which models data as a graph (a network of nodes and edges) instead of as a series of relational tables.

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Moving Beyond Data Visualization to Data Applications

One thing we love doing at Exaptive – aside from creating tools that facilitate innovation – is hiring intelligent, creative, and compassionate people to fill our ranks. Frank Evans is one of our data scientists. He was invited to present at the TEDxOU event on January 26, 2018.

Frank gave a great talk about how to go beyond data visualization to data applications. The verbatim of his script is below the video. Learn more about how to build data applications here

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A Graph-Analytics Data Application Using a Supercomputer!

We recently had to prototype a data application over a supercomputer tuned for graph analysis. We built a proof-of-concept leveraging multiple APIs, Cray’s Urika-GX and Graph Engine (CGE), and a handful of programming languages in less than a week.

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A Data Exploration Journey with Cars and Parallel Coordinates

Parallel coordinates is one way to visually compare many variables at once and to see the correlations between them. Each variable is given a vertical axis, and the axes are placed parallel to each other. A line representing a particular sample is drawn between the axes, indicating how the sample compares across the variables.

Previously, I wrote how it's possible to create a basic network diagram application from just three components in the Exaptive Studio. Many users will require more scalable from a data application, and fortunately the Studio allows for the creation of something like our Parallel Coordinates Explorer. Often times, a parallel coordinates diagram can also become cluttered, but fortunately, our Parallel Coordinates component lets users rearrange axes and highlight samples in the data to filter the view.

It helps to use some real data to illustrate. One dataset that many R aficionados may be familiar with is the mtcars dataset. It's a list of 32 different cars, or samples, with 11 variables for each car. The list is derived from a 1974 issue of Motor Trend magazine, which compared a number of stats across cars of the era, including the number of cylinders in the engine, displacement (the size of the engine, in cubic inches), economy (in miles per gallon of fuel), and power output.

Let's say we're interested in fuel economy, and want to find out characteristics could signify a car with good fuel economy. Anecdotally, you may have heard that larger engines generate more power, but that smaller engines generate better fuel economy. You may also have heard that four-cylinder engines are typically smaller in size than larger engines. Does this hold true for Motor Trend's mtcars data?

To find out we'll use a xap (what we call a data application made with Exaptive) that lets a user upload either a csv or Excel file and generates a parallel coordinates visualization from the data. But a data application is more than a data visualization. We're going to make a data application that selects and filters the data for rich exploration. 

In our dataflow programming environment, we use a few components to ingest the data and send a duffle of data to the visualization. Then a hand-full of helper components come together make the an application with which an end-user can explore the data.

Here's the dataflow diagram, with annotations. 

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Finding Netflix's Hidden Trove of Original Content with a Basic Network Diagram

Nexflix has collected an impressive amount of data on Hollywood entertainment, made possible by tracking the viewing habits of its more-than 90 million members. In 2013, Netflix took an educated guess based on that data to stream its own original series, risking its reputation and finances in the process. When people were subscribing to Netflix to watch a trove of television series and movies created by well-established networks and studios, why create original content? Now, few would question the move.

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When Earth is Like an Egg: 3D Terrain Visualization

Some of the most satisfying breakthroughs happen when technology gets used in a way it was never intended. While working with our graphic design group at Sasaki on ways to generate a dot pattern for a decorative screen, we came across some open-source software called StippleGen. Stippling is a way of creating an image by means of dots. StippleGen was created to optimize stippling for, among other things, egg painting. The software does a great job of laying out dots with greater density on the darker areas of the image while keeping a comfortable spacing between the dots. What's more, the voronoi algorithm it uses gives an irregular, organic pattern. The ah-ha moment came when I realized this could be applied to a different problem, visualizing terrain; specifically, optimizing terrain meshes in 3D software based off elevation data (a.k.a. Digital Elevation Model (DEM)).


Here's a typical use of StippleGen:

Used to create this: 

So how do we get from eggs to terrain? A given terrain, unlike an egg, is typically a mix of high variation areas, like canyons, with more uniform areas, like plains or plateaus. A typical DEM heightmap can be seen in the following image (top left) alongside some more familiar, human-readable representations of the same terrain that you might see on maps. Shaded relief is a useful trick for representing terrain in 2D where the terrain appears to be lit from one side.

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Affecting Change Using Social Influence Mapping

If you've ever tried to get a company to adopt new software you know how challenging it can be. Despite what seem to you like obvious benefits and your relentless communication, people selectively ignore or, worse, revolt against the change. Change efforts will even stumble in the face of this wisdom of the ages:

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Embracing the Hairball

One of the perennial challenges in visualizing complex networks is dealing with hairballs: how do you draw a network that is so large and densely interconnected that any full rendering of it tends to turn into an inscrutable mess? There are various approaches to addressing this problem: BioFabric, Hive Plots, and many others. Most involve very different visual abstractions for the network.

There is something compelling, however, in seeing the full, messy complexity of a network laid out in one image. Many of the alternate approaches have the disadvantage of being less intuitive. Most people are accustomed to inferring network structure from a collection of dots and lines; not so much from a matrix representation. I wondered if there wasn't a way to retain the immediacy and intuitiveness of, say, a force-directed layout, while somehow ordering it and stretching it out in a way that would give the important elements room to breathe. In this blog post I will describe an effort to find this middle ground.

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Cowboys and Inventors: The Myth of the Lone Genius

I recently moved from Boston to Oklahoma City. My wife got offered a tenure-track position at the University of Oklahoma, which was too good an opportunity for her career for us to pass up. Prior to the move, I had done a lot of traveling in the US, but almost exclusively on the coasts, so I didn't know what living in the southern Midwest would bring, and I was a bit trepidatious. It has turned out to be a fantastic move. There is a thriving high-tech startup culture here. I've been able to hire some great talent out of the University, and we're now planning to build up a big Exaptive home office here. Even more important, I was delighted to find a state that was extremely focused on fostering creativity and innovation. In fact, the World Creativity Forum is being hosted here this week, and I was asked to give a talk about innovation. As I thought about what I wanted to say, I found myself thinking about . . . cowboys.

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