Milk, a new computer language created by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) promises a four fold increase in the speed of analytics on big data problems. Although true big data problems are still rare, albeit the term is freely used for anything from large excel sheets to relational data tables, Milk is in the right direction. Computer chip architecture designs have been stagnant, still looking to double speed every 18 months, by packing silicon ever closer with little innovation.
Efficient use of memory has been a perennial problem for analytics, dealing with sparse and noisy data. Rigid hardware designs shuttle unwanted information based on archaic design concepts never asking the question if the data transport is necessary or timely. With hardware and even memory costs in a precipitous decline, there has not been sufficient force behind seeking changes to age old designs. Now that exponentially increasing data is beginning to challenge available hardware again and the need for speed to sift through the proverbial haystack of noise to find the golden needle is in demand, we may need to innovate again. And, Milk paves the path for possible software solutions.
Using just enough data at the right time to make decisions is a good habit, not only in computing but also in every other arena. In the past two decades, computer companies and database vendors sought to sell the biggest steel to all their customers on the future promise of analytics once they collect all the garbage and store it in warehouses. Now that analytics has "arrived," reducing the garbage into usable insights has become a major problem for companies.
Extracting insights from sparse and noisy data is not easy. Perhaps academic institutions can lend a helping hand to jump start innovation at computer behemoths, as they get stuck in the status-quo.
Efficient use of memory has been a perennial problem for analytics, dealing with sparse and noisy data. Rigid hardware designs shuttle unwanted information based on archaic design concepts never asking the question if the data transport is necessary or timely. With hardware and even memory costs in a precipitous decline, there has not been sufficient force behind seeking changes to age old designs. Now that exponentially increasing data is beginning to challenge available hardware again and the need for speed to sift through the proverbial haystack of noise to find the golden needle is in demand, we may need to innovate again. And, Milk paves the path for possible software solutions.
Using just enough data at the right time to make decisions is a good habit, not only in computing but also in every other arena. In the past two decades, computer companies and database vendors sought to sell the biggest steel to all their customers on the future promise of analytics once they collect all the garbage and store it in warehouses. Now that analytics has "arrived," reducing the garbage into usable insights has become a major problem for companies.
Extracting insights from sparse and noisy data is not easy. Perhaps academic institutions can lend a helping hand to jump start innovation at computer behemoths, as they get stuck in the status-quo.
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