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Data Science and the Open World Assumption

A funny thing happened in the last few years. We began to lose the Closed World Assumption. Now I can understand that this is not exactly huge, earth-shattering news; most people do not in fact realize that they've been using the Closed World Assumption to begin with. However, I'd contend that this event is having a transformative effect upon the way that we interact with data, one that may very well change the perspective about information in ways perhaps as profound as Ted Codd's introduction of the relational model in the 1970s. Open the Closed World Doors In basic terms, the closed world assumption can be stated as "When we model something, our model is complete."  Most people who have had to define a data model recognize that this statement is at best a convenient fiction - any effort to completely define almost any object ultimately comes down to identifying which attributes of that object are relevant to the particular business domain - yet even with this observation, … [Read More...]

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From Data to Wisdom

Much is made today of the possible wisdom to be gleaned from a wealth of available data. I am one of those who, through experience, believe this to be true. However, there are a couple steps necessary to achieve wisdom from data. Knowledge from information … [Read More...]

dam-growth

Why DAM is a growth industry

This statistic from this press release by a market research group caught my attention last week: investment in digital asset management (DAM) is expected to grow from $1.16 billion in 2014 to $4.12 billion by 2019. That’s a growth rate of more than 28% per … [Read More...]

Direction Uncertainty

Chaos and Confusion

I have worked as an IT consultant for nearly 20 years and at the beginning of my career a mentor said to me “Chaos and Confusion is the environment we work in”.  Our job is to bring sane practical perspectives to address the hype surrounding new IT tools and … [Read More...]

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Getting Started with Giraph

Apache Hadoop's core analytical tools (e.g. MapReduce, Hive, Pig) are great for performing batch analytics over large, unstructured data sets.  However, a myriad of data sets are comprised of a more graph-like structure. Examples of such data sets … [Read More...]

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MarkLogic Gets JavaScript

Six months ago, I was down at the MarkLogic World San Francisco conference just south of SFO, listening to the opening speeches. I was there primarily to hear more about what would end up being in the second phase of their Semantics offering, when a mention … [Read More...]