Big Data is probably one of the most misused words of the last decade. It was widely promoted, discussed, and spread around by business managers, technical experts, and experienced academics. Slogans like “Data is the new oil” were widely accepted as unquestionable truth.

These beliefs pushed  technologies forward. Its stack, formerly developed by Yahoo! and now owned by the Apache Software Foundation, was recognized as “The” Big Data…

There is a library called threading in Python and it uses threads (rather than just processes) to implement parallelism. This may be surprising news if you know about the Python's Global Interpreter Lock, or GIL, but it actually works well for certain instances without violating the GIL. And this is all done without any overhead -- simply define…

I.  Introduction

This is a continuation of my previous blog, “Natural Language Understanding – Application Notes with Context Discriminant”. 

Background:

Natural Language Understanding (NLU) is a subtopic of Natural Language Processing (NLP). Successful implementations of NLU are difficult because of limitations in prevailing technology. SiteFocus solved these limitations with a new approach to NLU. This approach has been successfully…

Summary:  Which is more important, the data or the algorithms?  This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years.  And it all leads back to Hadoop.

Recently I was challenged to speak on the role of data in data…

Introduction

Business ventures based on existing or disruptive business models taking on the route of Initial Public Offering are always a challenge to investors who want to profit from early investment into those would be “unicorn IPO”. A good investment may get worse before it gets better. Others may get worse and never recover. Aside from the macroeconomics and consumer trends that could affect the outcome of such investments, the fundamentals of these new public offerings…

Introduction

Deep Learning can be used to automate just about every repetitive task that is currently or formerly performed by humans. Factory robots, autonomous cars, Internet of Things are example of these automations. Yet, mentally challenging tasks such as conducting research or strategic planning with natural language textual documents remain a daunting task for automation. We look into the root cause of this challenge and have implemented a solution to automate these…

By JIANG Buxing, Data Scientist 

As big data concept gains momentum, unstructured data analytics technology is becoming hot.  It is said that 80% of all data in an enterprise is unstructured, which is roughly true when it is measured by the space, considering the large size of the audio and video data. With huge amounts of data at hand, some technique is required to analyze them.

But make sure you are not misled by the universal unstructured data analytics…

Search any data related posting and you’ll soon be up to your eyeballs in reports on the promise of the new data era, techniques to help build a better data engine or incorporate new data widgets, and infographics and visualizations showing the kinds of insights you can get with the right mix of techniques.

Got it. Data is big. It’s expected to grow in volume to 35 to 45 zetabytes by 2020. As in seven sets of three zeros big.

It’s also transforming business processes big.…

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before.…

With the holiday shopping season officially kicked off, sales data is on everyone's mind. But that's not the only data people are talking about. Talk of data brokers and consumer tracking is becoming more commonplace, as is a certain backlash against big data. This week, we highlight some articles touching on these subjects and round out the discussing with some coverage of natural language processing techniques.

Data Brokers

Everyone knows about online…

Variety, Velocity, Volume and Veracity are the four Vs for Big Data. Most of the technologies available have shown how to treat the Volume. However, due to the increasing number of streaming data sources, the Velocity problem is as relevant as never before. Moreover, Veracity and especially Variety problems have increased the difficulty of the challenge.…

The cornerstone of the small work done for getting the info for these great charts with IEPY, was to be able to catch mentioned companies.

Click here for an interactive version of this chart…

Organizations are struggling with a fundamental challenge – there’s far more data than they can handle.  Sure, there’s a shared vision to analyze structured and unstructured data in support of better decision making but is this a reality for most companies?  The big data tidal wave is transforming the database management industry, employee skill sets, and business strategy as organizations race to unlock meaningful connections between disparate sources of…

Summary:  These are the features common to most NOSQL databases.  Be on the lookout for any fundamental differences.

Before we get to the specific pros and cons of the four NOSQL database types there are some features and capabilities true of most of these that you should know.…

Summary: Gartner says that predictive analytics is a mature technology yet only one company in eight is currently utilizing this ability to predict the future of sales, finance, production, and virtually every other area of the…