By CKM Advisors Natural Language Analysis Team,
Last year we posted a popular piece offering our view on…Continue
By: Nicholas Hartman, Director at CKM Advisors
Today we'd like to share with you some fun charts that have come out of our internal linguistics research efforts. Specifically, studying weather events by analyzing social media traffic from Twitter.
We do not specialize in social media and most of our data analytics work focuses on the…Continue
Added by Nicholas Hartman on December 15, 2013 at 7:24pm — No Comments
We have been using tables in the relational database, mostly for the transactional purposes, and that proves effective. Considering the data size and analytic purpose, however, the data structure might need to be redesigned for better efficiency.
To determine how to decompose the complexity of big data, we have observed the way the organisms function. In the physical world, the universe is organized into a hierarchy of…Continue
Added by Yuanjen Chen on November 3, 2013 at 10:29pm — No Comments
To be short, in-memory computing takes advantage of physical memory, which is expected to process data much faster than disk. In-place, on the other hand, fully utilizes the address space of 64bit architecture. Both are gifts from the modern computer science; both are essences of the BigObject.
In-place computing only becomes possible upon the introduction of 64bit architecture, whose address space is big enough to hold the entire data set for most of cases we are dealing with today.…Continue
Added by Yuanjen Chen on October 29, 2013 at 1:00am — No Comments
This is my first post here. I'm glad to introduce this newly launched big data analytic engine, the BigObject. In the past 2 years we have been working on an optimal approach to handle big data for analytic purposes and challenging the existed models, some assumptions of which are no longer valid. For example, as the data size grows so rapidly, is it still practical that we stick to the relational models neglecting the time spending in data retrievals? What impact did…Continue
Added by piALGO on October 17, 2013 at 8:32am — No Comments
There’s been a great deal of discussion over the past several weeks regarding data mining and predictive models. Terms like “meta data” and “algorithm” are fast moving from the domain of IT practitioners and into the realm of water cooler discussion. This might be a good opportunity to briefly review some of these concepts in order to better understand data mining practices and standards.
First, some terms.
Meta Data - refers…Continue
Added by James Sullivan on August 15, 2013 at 6:00am — No Comments
During analysis of movements of individuals in public places, there are only two dimensions that can represent movement of an individual, shown via data saved between starting and end point, even incorporating elevators and stairs to different (shop) levels). That is a multi-linear way of looking at movements of individuals in crowds in a specific environment. Most big shopping corporates use these kinds of analysis methods.
But what if (like in 3D environments) movement can be up and…Continue
Added by Emmanuel P. Gruijs on May 24, 2013 at 3:14am — No Comments
Big Data can help in mapping and understanding customer behaviors, and in developing one-to-one marketing programs or innovative services. However, Big Data is too often presented as a technological capability subsequently requiring armies of data scientists to mine and analyse data.
Yes, managing and exploiting the growing amount of internal and external data is a necessary condition to steer business performance. But it is far from a sufficient condition.
In a recent meeting…Continue
Added by Patrick Glenisson on June 16, 2012 at 6:53am — No Comments
From MIT's Technology Review -
"Data science is so new that there are no textbooks on the subject, and no university curricula designed to turn out data scientists. Yet it's integral to everything from quantitative trading on Wall Street to ad targeting on the web and the optimization of real-world supply chains."