Figure 1 projected growth in unstructured and

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Figure 1: Projected Growth in Unstructured and Structured Data
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From an epistemological perspective, the data explosion makes it productive to visit the age old philosophical debate on the limits of induction as a scientific method for knowledge discovery. Specifically, it positions the computer as a credible generator and tester of hypotheses by ameliorating some of the known errors associated with statistical induction. Machine learning, which is characterized by statistical induction aimed at generating robust predictive models, becomes central to Data Science. From an engineering standpoint, it turns out that scale matters in that it has rendered the traditional database models somewhat inadequate for knowledge discovery. Traditional database methods are not suited for knowledge discovery because they are optimized for fast access and summarization of data given what the user wants to ask (i.e. a query), not discovery of patterns in massive swaths of data when the user does not have a well formulated query. Unlike database querying which asks “what data satisfy this pattern (query),” discovery is about asking “what patterns satisfy this data?” Specifically, our concern is in finding interesting and robust patterns that satisfy the data, where interesting is usually something unexpected and actionable, and robust means a pattern that is expected to occur in the future. What makes an insight actionable? Other than domain-specific reasons, it is prediction. Specifically, what makes an insight actionable is its predictive power in that the return distribution associated with an action can be reliably estimated from past data and therefore acted upon with a high degree of confidence. The emphasis on prediction is particularly strong in the machine learning and “KDD” communities. Unless a learned model is predictive, it is generally regarded with skepticism. This position on prediction mirrors the view expressed strongly by the philosopher Karl Popper as being a primary criterion for evaluating a theory and for scientific progress in general. 15 Popper argued that theories that sought only to explain a phenome non were weak whereas those that make “bold predictions” that stand the test of time and are not falsifiable should be taken more seriously. In his well-known treatise on this subject, Conjectures and Refutations, Popper presented Einstein’s theory of relativity as a “good” one since it made bold predictions that could be easily falsified. All attempts at falsification on this theory have indeed failed on this date. In contrast, Popper argued that a theory like Freud’s, which could be “bent” to accommodate almost any scenario is weak in that it was virtually unfalsifiable.
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