Columnar storage is a familiar data storage technique that is used by many data warehousing products because of its high effectiveness in many computing scenarios. The technique is usually a synonym of high-performance within the industry.
But is columnar storage a perfect strategy? A google shows that criticisms surrounding it are mainly about data modification. There are few discussions of its application to the read-only data analysis and computing, which will be taken care…
ContinueAdded by JIANG Buxing on September 12, 2017 at 11:00pm — No Comments
MLaaS is neither new nor rocket science or an unknown service. In today’s time there are hundreds of companies in this domain which are working as a service provider of MLaaS (SPMLaaS). Machine learning is into so many services and applications as on date and we may not even aware of them or most of them. In the area of FinTech, Medical, Law and almost every service which…
Added by Vinod Sharma on September 12, 2017 at 10:00pm — No Comments
While many complain about Google's increased monopoly and control over our lives, in this article, I offer a different perspective regarding Google search. I will go as far as to claim that Google's influence (as a search engine) is declining. Not that their traffic share or revenue is shrinking, to the contrary, both are probably increasing. And Google is moving in many other directions business-wise: Search might have been their first, most used and most well-known product, but the future…
ContinueAdded by Vincent Granville on September 12, 2017 at 7:30pm — No Comments
After Equifax's massive data breach - social security number, date of birth, and address from 143 million Americans stolen by cyber criminals - the question is: Can a financial institution be liable for using wrong Equifax data?
One would expect that this Equifax event will result in an increase in ID theft. Banks trust credit…
ContinueAdded by Vincent Granville on September 12, 2017 at 10:00am — No Comments
Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative’s new “Federal Automated Vehicle Policy” as well as similar policy just emerging in Germany. As a model of regulation on emerging AI technology we think they got this just about right.
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Added by William Vorhies on September 12, 2017 at 9:35am — No Comments
In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that everyone seems to be talking about. The first part will focus on introducing tensorflow, go through some applications and touch upon the architecture.
This post is the first part of the multi-part series on a complete tensorflow tutorial –
Added by Vivek Kalyanarangan on September 12, 2017 at 12:00am — 15 Comments
DML stands for “Dynamical Machine Learning” (more in the book, “SYSTEMS Analytics for IoT Data Science”, 2017). This match is not surprising once you realize that DML & IoT are both based on the venerable Systems Theory. Let us dig deeper . . .
Consider IoT for industrial applications. A machine is instrumented with sensors, data are collected in real-time (or at intervals), communicated to the cloud where IoT Data Science…
ContinueAdded by PG Madhavan on September 11, 2017 at 12:30pm — No Comments
This article was written by Uli Bethke.
The full title is "Dimensional Modeling and Kimball Data Marts in the Age of Big Data and Hadoop".
Is dimensional modeling dead? Before I give…
ContinueAdded by Amelia Matteson on September 11, 2017 at 11:30am — No Comments
The previous posts in this series have covered several ways that business leaders can use to understand and explore how Artificial Intelligence can impact their business. We saw that there are several key ways in which AI advances can improve human productivity in organizations. The last two articles dived into Distillation: automating…
ContinueAdded by Roy Wilds, PhD, PHEMI Systems on September 11, 2017 at 6:00am — No Comments
The following problem appeared as an assignment in the coursera course Algorithm-I by Prof.Robert Sedgewick from the Princeton University few years back (and also in the course cos226 offered at Princeton). The problem definition and the description is taken from the course website and lectures. The original assignment was to be done in java, where in this article both the java and a corresponding python implementation will…
ContinueAdded by Sandipan Dey on September 11, 2017 at 4:30am — No Comments
In this Digital age, every organization is trying to apply machine learning and artificial intelligence to their internal and external data to get actionable insights which will help them to be closer to today’s customer. A few years back it was the field only for data scientists and statisticians, who used to analyze the data, apply several techniques…
Added by Sandeep Raut on September 10, 2017 at 11:30pm — No Comments
Who’s this article for:
This blog is intended for enterprise data analysts, line of business users, and data practitioners who work with qualitative and quantitative data in decision-making.
How enterprise currently use data science and business intelligence today
Quantitative analytics based on statistical models predict outcome with data models built from historical datasets using machine-learning algorithms
Added by Sing Koo on September 10, 2017 at 4:00pm — No Comments
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.
Announcements
Added by Vincent Granville on September 9, 2017 at 1:00pm — No Comments
"Elementary particles are the building blocks of al matter everywhere in the universe.
Their properties are connected…
ContinueAdded by Toni Manzano on September 9, 2017 at 6:00am — No Comments
This article was written by Dan Shewan.
If science-fiction movies have taught us anything, it’s that the future is a bleak and terrifying dystopia ruled by murderous sentient robots.
Fortunately, only one of these things is true – but that could soon change, as…
ContinueAdded by Amelia Matteson on September 8, 2017 at 4:00pm — No Comments
Leveraging the use of big data, as an insight-generating engine, has driven the demand for data scientists at enterprise-level, across all industry verticals. Whether it is to refine the process of product development, help improve customer retention, or mine through the data to find new business opportunities—organizations are increasingly relying on the expertize of data…
Added by Ronald van Loon on September 8, 2017 at 9:30am — 1 Comment
“Arguably the most significant development in information technology over the past few years, blockchain has the potential to change the way that the world approaches big data, with enhanced security and data quality just two of the benefits afforded to businesses using Satoshi Nakamoto’s landmark technology.”…
Added by Noah Data on September 8, 2017 at 3:00am — No Comments
This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back.
18 Great Blogs Posted in the last 12…
ContinueAdded by Vincent Granville on September 7, 2017 at 4:30pm — No Comments
Social media platforms such as Twitter and Facebook enable everyone to voice their opinions about topics, companies, and products online.
These comments are a great source for companies to analyze their customers’ opinion about their brand or product. However, with billions of Tweets and posts daily, this is can take a lot of time.
Unless of course, you use R J With just a few lines of R-code and the help of machine learning, we’re able to build mood monitoring tools quickly,…
ContinueAdded by Daniel Schmeh on September 7, 2017 at 9:30am — No Comments
Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic.
Author…
ContinueAdded by Emmanuelle Rieuf on September 7, 2017 at 8:00am — No Comments
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