All posts on August, 2017


Users review the top cloud data integration tools

As the world of cloud computing becomes more globalized, IT professionals need multiple levels of security and transparency to manage cloud relationships. Using a cloud data integration solution, an enterprise can configure a number of disparate application programs sharing data in a diverse network, including cloud-based data repositories. This allows enterprise tech professionals to manage, monitor and cleanse data from various web-based and mobile applications more effectively.

IT Central Station users have identified agile data transformation, a clear, customizable dashboard and efficient data replication as valuable features when looking for a cloud data integration solution. According to their reviews, the IT Central Station community has ranked Informatica Cloud Data Integration, Dell Boomi AtomSphere,  IBM App Connect and SnapLogic as leading cloud data integration solutions in the market.

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How much is a good deal worth?

If you knew that every single commercial or ad you ever had to see would only be for the exact products you wanted, what would you be willing to give up in trade? Would you be willing to post your home address for others to see? Your income bracket? Your birth date? Mind you, this information wouldn’t only go to your approved social media connections…would you be willing to tell the whole internet?

For most of us, the automatic answer is no, but the reality of social media isn’t too far off. Some platforms like Facebook rely on targeted advertising to keep the lights on, and to keep the service free for the public to use.

You might argue that it’s a small price to pay for the ability to connect to practically anyone in the world, and sure, advertising in and of itself is not inherently bad. But when advertising crosses the line into invasion of privacy, consumers have to ask hard questions about what personal data they’re giving away.

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What is data mining? How analytics uncovers insights

Organizations today are gathering ever-growing volumes of information from all kinds of sources, including websites, enterprise applications, social media, mobile devices, and increasingly the internet of things (IoT).

The big question is: How can you derive real business value from this information? That’s where data mining can contribute in a big way. Data mining is the automated process of sorting through huge data sets to identify trends and patterns and establish relationships, to solve business problems or generate new opportunities through the analysis of the data.

It’s not just a matter of looking at data to see what has happened in the past to be able to act intelligently in the present. Data mining tools and techniques let you predict what’s going to happen in the future and act accordingly to take advantage of coming trends.

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When identity data eclipses digital identity

When I first became involved in the identity space, which was about 10 years now, the definition of ‘digital identity’ was being hotly debated. This debate raged on over the years, but out of it, a stoic pragmatism has emerged. Digital identity is many things, but what it has in common across all definitions, is data. You are what your attributes say you are…well if you have had them verified to a decent degree of probability that is.

Identity data is a valuable commodity. In terms of attractive assets, it has cybercriminals chomping at the bit to get at it. According to a study by the Identity Theft Center, data breaches increased by 40% in 2016 over the 2015 figures. Identity data is also, of course, highly valuable to the individual behind the data, and service that individual wants to access. We need to make the identity data work for the individual, not the cybercriminal. But to do this, we need to start to break the silo barriers down.

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13 frameworks for mastering machine learning

13 frameworks for mastering machine learning
13 frameworks for mastering machine learning

Image by W.Rebel via Wikimedia

Over the past year, machine learning has gone mainstream with a bang. The “sudden” arrival of machine learning isn’t fueled by cheap cloud environments and ever more powerful GPU hardware alone. It is also due to an explosion of open source frameworks designed to abstract away the hardest parts of machine learning and make its techniques available to a broad class of developers.

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Oracle’s Hurd, AT&T’s Donovan on their massive cloud migration deal

If worries about digital transformation projects keep you up at night, imagine how it would feel to be responsible for moving thousands of internal databases to the cloud for a company with more than $160 billion in annual sales and 260,000 employees. That’s the job that AT&T Communications CEO John Donovan is undertaking, and he’s working with Oracle CEO Mark Hurd to do it. 

When the companies announced in May that they were working together, Hurd called the agreement “historic.” While hyperbole is part of everyday life in tech, lessons learned from the massive project are bound to reverberate across enterprises in a variety of fields, as Hurd noted in the following discussion with Donovan and IDG News Service Editor in Chief Marc Ferranti.

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What is machine learning? Software derived from data

You’ve probably encountered the term “machine learning” more than a few times lately. Often used interchangeably with artificial intelligence, machine learning is in fact a subset of AI, both of which can trace their roots to MIT in the late 1950s.

Machine learning is something you probably encounter every day, whether you know it or not. The Siri and Alexa voice assistants, Facebook’s and Microsoft’s facial recognition, Amazon and Netflix recommendations, the technology that keeps self-driving cars from crashing into things – all are a result of advances in machine learning.

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IBM speeds deep learning by using multiple servers

For everyone frustrated by how long it takes to train deep learning models, IBM has some good news: It has unveiled a way to automatically split deep-learning training jobs across multiple physical servers — not just individual GPUs, but whole systems with their own separate sets of GPUs.

Now the bad news: It’s available only in IBM’s PowerAI 4.0 software package, which runs exclusively on IBM’s own OpenPower hardware systems.

Distributed Deep Learning (DDL) doesn’t require developers to learn an entirely new deep learning framework. It repackages several common frameworks for machine learning: TensorFlow, Torch, Caffe, Chainer, and Theano. Deep learning projecs that use those frameworks can then run in parallel across multiple hardware nodes.

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