In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?
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Today: MS Word meets Web Crawling. Identifying the Secret Ingredient
Authors: Roland Burger and Heather Fyson
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The Challenge
The ChallengeIt’s Christmas again, and like every Christmas, I am ready to bake my famous Christmas cookies. They’re great and have been praised by many! My recipes are well-kept secrets as I am painfully aware of the competition. There are recipes galore on the web nowadays. I need to find out more about these internet-hosted recipes and their ingredients, particularly any ingredient I might have overlooked over the years.
My recipes are stored securely in an MS Word document on my computer, while a very well regarded web site for cookie recipes is http://www.handletheheat.com/peanut-butter-snickerdoodles/. I need to compare my own recipe with this one on the web and find out what makes them different. What is the secret ingredient for the ultimate Christmas cookie?
In practice, on one side I want to read and parse my Word recipe texts and on the other side I want to use web crawling to read and parse this web-hosted recipe. The question as usual is: will they blend?
Topic. Christmas cookies.
Challenge. Identifying secret ingredients in cookie recipes from the web by comparing them to my own recipes stored locally in an MS Word document.
Access Mode. MS Word .docx file reader and parser and web crawler nodes.