June 20, 2019 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems

Mapping an Ancient Empire

Geocomputation is an indispensable modern tool for analyzing and viewing large-scale data such as population demographics, natural features and political borders. And if you’ve read some of my other posts, you can probably tell that I like working with maps. Recently, a Wolfram Community member asked:

“How do I make an interactive map of the
Byzantine Empire through the years?”

To figure out a solution, we’ll tap into the Wolfram Knowledgebase for some historical entities, as well as some of the high-level geocomputation and visualizations of the Wolfram Language. Once we’ve created our brand-new function, we’ll submit it to the Wolfram Function Repository for anyone to use.

Byzantine Empire

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May 28, 2019 — Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D

Did We Really Write What We Said We Wrote?

Several Months Ago…

I wrote a blog post about the disputed Federalist Papers. These were the 12 essays (out of a total of 85) with authorship claimed by both Alexander Hamilton and James Madison. Ever since the landmark statistical study by Mosteller and Wallace published in 1963, the consensus opinion has been that all 12 were written by Madison (the Adair article of 1944, which also takes this position, discusses the long history of competing authorship claims for these essays). The field of work that gave rise to the methods used often goes by the name of “stylometry,” and it lies behind most methods for determining authorship from text alone (that is to say, in the absence of other information such as a physical typewritten or handwritten note). In the case of the disputed essays, the pool size, at just two, is as small as can be. Even so, these essays have been regarded as difficult for authorship attribution due to many statistical similarities in style shared by Hamilton and Madison.

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May 23, 2019 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems

Just as Wolfram was doing AI before it was cool, so have we been doing data science since before it was mainstream. A prime example is the creation of Wolfram|Alpha—a massive project that involved engineering, modeling, analyzing, visualizing and interfacing with terabytes of data, developing a natural language interface, and deploying results in a sensible way. Wolfram|Alpha itself is a tool for doing data science, and its continued success is largely because of the underlying strategy we used to build it: a multiparadigm approach driven by natural curiosity, exploring all kinds of data, using advanced methods from a range of areas and automating as much as possible.

Any approach to data science can only be as effective as the computational tools driving it; luckily for us, we had the Wolfram Language at our disposal. Leveraging its universal symbolic representation, high-level automation and human readability—as well as its broad range of built-in computation, knowledge and interfaces—streamlined our process to help bring Wolfram|Alpha to fruition. In this post, I’ll discuss some key tenets of the multiparadigm approach, then demonstrate how they combine with the computational intelligence of the Wolfram Language to make the ideal workflow for not only discovering and presenting insights from your data, but also for creating scalable, reusable applications that optimize your data science processes.

Doing Data Science Better with Wolfram and the Multiparadigm Approach

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April 26, 2019 — Tim Shedelbower, Visualization Developer, Algorithms R&D

Connect the dots. It was exciting to draw from number to number until the sudden discovery of a hidden cartoon. That was my inadvertent introduction to graph theory very early in school. Little did I know adults used the same concept to discover hidden patterns to solve problems, such as proving that a single crossing of seven Königsberg bridges to four land masses is not possible, but coloring a map distinctly with four colors is. These problems inspired the methods we know today as graph theory. And in honor of the work of late mathematician and connect-the-dot author Elwyn Berlekamp, we see how sophisticated this “child’s play” can be by examining the different styles and themes we can apply to graphs.

Connect the dots with Graph and PlotTheme

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April 11, 2019 — Swede White, Public Relations Manager

Fishackathon

Every year, the U.S. Department of State sponsors a worldwide competition called Fishackathon. Its goal is to protect life in our waters by creating technological solutions to help solve problems related to fishing.

The first global competition was held in 2014 and has been growing massively every year. In 2018 the winning entry came from a five-person team from Boston, after competing against 45,000 people in 65 other cities spread across 5 continents. The participants comprised programmers, web and graphic designers, oceanographers and biologists, mathematicians, engineers and students who all worked tirelessly over the course of two days.

To find out more about the winning entry for Fishackathon in 2018 and how the Wolfram Language has helped make the seas safer, we sat down with Michael Sollami to learn more about him and his team’s solution to that year’s challenge.

Wolfram Language at Fishackathon

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March 21, 2019 — Chapin Langenheim, Editorial Project Coordinator, Project Management

Starry night over water

Over the past 16 weeks, Wolfram Community has gained over 1,000 new members—surpassing 21,000 members total! We’ve also seen more activity, with 800,000 pageviews and 160,000 new readers in that time period. We enjoy seeing the interesting and unique projects Wolfram Language users come up with and are excited to share some of the posts that make Wolfram Community a favorite platform for sharing, socializing and networking.

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February 14, 2019 — Toni Schindler, Wolfram|Alpha Scientific Content

Imagine you could import any website to obtain meaningful data for further processing, like creating a diagram, highlighting places on a map or integrating with other data sources. What if you could query data on the web knowing only one simple query language? That’s the vision of the semantic web. The semantic web is based on standards like the Resource Description Framework (RDF) and SPARQL (a query language for RDF). The upcoming release of Version 12 of the Wolfram Language introduces experimental support for interacting with the semantic web: you will be able to Import and Export a variety of RDF data formats as well as query remote SPARQL endpoints and in-memory data using either a query string or a symbolic representation of SPARQL.

Computational Musicology: Using Wikidata And MusicBrainz

Wikidata MusicBrainz

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January 24, 2019 — Jacob Wells, Technical Specialist, European Sales

Do you select a bottle of wine based more on how fancy the sleeve is than its price point? If so, then you’re like me, and you may be looking to minimize the risk of wishful guesses. This article may provide a little rational weight to your purchasing decisions.

Due to my research using the Wolfram Language, I can now mention the fact that if you are spending less than $40 on a random bottle of wine, you have a less than 0.1% chance of finding a 95+-rated wine. I could also perhaps reel off some flavors and characteristics of wines from Tuscany, for example—cherry, fruit, spice and tannins. My aim is to show you how I took a passing idea of mine and brought it to fruition using the Wolfram Language.

How I became a wine expert using the Wolfram Language

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January 10, 2019 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems

So far in this series, I’ve covered the process of extracting, cleaning and structuring data from a website. So what does one do with a structured dataset? Continuing with the Election Atlas data from the previous post, this final entry will talk about how to store your scraped data permanently and deploy results to the web for universal access and sharing.

Deploying and Sharing with the Wolfram Language

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October 11, 2018 — Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D

Hero

Between October 1787 and April 1788, a series of essays was published under the pseudonym of “Publius.” Altogether, 77 appeared in four New York City periodicals, and a collection containing these and eight more appeared in book form as The Federalist soon after. As of the twentieth century, these are known collectively as The Federalist Papers. The aim of these essays, in brief, was to explain the proposed Constitution and influence the citizens of the day in favor of ratification thereof. The authors were Alexander Hamilton, James Madison and John Jay.

On July 11, 1804, Alexander Hamilton was mortally wounded by Aaron Burr, in a duel beneath the New Jersey Palisades in Weehawken (a town better known in modern times for its tunnels to Manhattan and Alameda). Hamilton died the next day. Soon after, a list he had drafted became public, claiming authorship of more than sixty essays. James Madison publicized his claims to authorship only after his term as president had come to an end, many years after Hamilton’s death. Their lists overlapped, in that essays 49–58 and 62–63 were claimed by both men. Three essays were claimed by each to have been collaborative works, and essays 2–5 and 64 were written by Jay (intervening illness being the cause of the gap). Herein we refer to the 12 claimed by both men as “the disputed essays.”

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