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Remembering Big Data Gravity – PART 2

I ended my previous blog wondering if awareness of Data Gravity should change our behavior. While Data Gravity adds Value to Big Data, I find that the application of the Value is under explained.

Exponential growth of data has naturally led us to want to categorize it into facts, relationships, entities, etc. This sounds very elementary. While this happens so quickly in our subconscious minds as humans, it takes significant effort to teach this to a machine.

A friend tweeted this to me last week: I paddled out today, now I look like a lobster. Since this tweet, Twitter has inundated my friend and me with promotions from Red Lobster. It is because the machine deconstructed the tweet: paddled <PROPULSION>, today <TIME>, like <PREFERENCE> and lobster <CRUSTACEANS>. While putting these together, the machine decided that the keyword was lobster. You and I both know that my friend was not talking about lobsters.

You may think that this maybe just a funny edge case. You can confuse any computer system if you try hard enough, right? Unfortunately, this isn’t an edge case. 140 characters has not just changed people’s tweets, it has changed how people talk on the web. More and more information is communicated in smaller and smaller amounts of language, and this trend is only going to continue.

When will the machine understand that “I look like a lobster” means I am sunburned?

I believe the reason that there are not hundreds of companies exploiting machine-learning techniques to generate a truly semantic web, is the lack of weighted edges in publicly available ontologies. Keep reading, it will all make sense in about 5 sentences. Lobster and Sunscreen are 7 hops away from each other in dbPedia – way too many to draw any correlation between the two. For that matter, any article in Wikipedia is connected to any other article within about 14 hops, and that’s the extreme. Completed unrelated concepts are often just a few hops from each other.

But by analyzing massive amounts of both written and spoken English text from articles, books, social media, and television, it is possible for a machine to automatically draw a correlation and create a weighted edge between the Lobsters and Sunscreen nodes that effectively short circuits the 7 hops necessary. Many organizations are dumping massive amounts of facts without weights into our repositories of total human knowledge because they are naïvely attempting to categorize everything without realizing that the repositories of human knowledge need to mimic how humans use knowledge.

For example – if you hear the name Babe Ruth, what is the first thing that pops to mind? Roman Catholics from Maryland born in the 1800s or Famous Baseball Player?

data gravityIf you look in Wikipedia today, he is categorized under 28 categories in Wikipedia, each of them with the same level of attachment. 1895 births | 1948 deaths | American League All-Stars | American League batting champions | American League ERA champions | American League home run champions | American League RBI champions | American people of German descent | American Roman Catholics | Babe Ruth | Baltimore Orioles (IL) players | Baseball players from Maryland | Boston Braves players | Boston Red Sox players | Brooklyn Dodgers coaches | Burials at Gate of Heaven Cemetery | Cancer deaths in New York | Deaths from esophageal cancer | Major League Baseball first base coaches | Major League Baseball left fielders | Major League Baseball pitchers | Major League Baseball players with retired numbers | Major League Baseball right fielders | National Baseball Hall of Fame inductees | New York Yankees players | Providence Grays (minor league) players | Sportspeople from Baltimore | Maryland | Vaudeville performers.

Now imagine how confused a machine would get when the distance of unweighted edges between nodes is used as a scoring mechanism for relevancy.

If I were to design an algorithm that uses weighted edges (on a scale of 1-5, with 5 being the highest), the same search would yield a much more obvious result.

data gravity1895 births [2]| 1948 deaths [2]| American League All-Stars [4]| American League batting champions [4]| American League ERA champions [4]| American League home run champions [4]| American League RBI champions [4]| American people of German descent [2]| American Roman Catholics [2]| Babe Ruth [5]| Baltimore Orioles (IL) players [4]| Baseball players from Maryland [3]| Boston Braves players [4]| Boston Red Sox players [5]| Brooklyn Dodgers coaches [4]| Burials at Gate of Heaven Cemetery [2]| Cancer deaths in New York [2]| Deaths from esophageal cancer [1]| Major League Baseball first base coaches [4]| Major League Baseball left fielders [3]| Major League Baseball pitchers [5]| Major League Baseball players with retired numbers [4]| Major League Baseball right fielders [3]| National Baseball Hall of Fame inductees [5]| New York Yankees players [5]| Providence Grays (minor league) players [3]| Sportspeople from Baltimore [1]| Maryland [1]| Vaudeville performers [1].

Now the machine starts to think more like a human. The above example forces us to ask ourselves the relevancy a.k.a. Value of the response. This is where I think Data Gravity’s becomes relevant.

You can contact me on twitter @bigdatabeat with your comments.

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Posted in Architects, Big Data, Cloud, Cloud Data Management, Data Aggregation, Data Archiving, Data Governance, General, Hadoop | Tagged , , , , , , | Leave a comment

Five Examples Of How Master Data Management (MDM) Helps Integrate Social Media Data Into Your Business

Are you trying to figure out how to integrate social media data into your business?

A recent poll, taken during a webinar on The Power of Social & Mobile Data, revealed almost 50% of respondents are trying to integrate social media data into their business. Half indicated that the Marketing executive was most interested. For almost 20 percent it’s the Product Development or Merchandising executive. (more…)

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Posted in Big Data, Master Data Management | Tagged , , , , , , , , , , , , , , | 4 Comments