Infographics and Data Visualization
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If You Can't Measure It, You Can't Manage It

If You Can't Measure It, You Can't Manage It | Infographics and Data Visualization | Scoop.it
There are a number of solutions enterprises can use to track mobile data.
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5 ways chatbots are revolutionizing knowledge management

The fields of knowledge management, information management, and content management have become critical to a modern workplace. Finding, documenting, and knowing things in an environment where data is…

Via Pierre Levy
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AI goes to the movies to identify content you will want to watch

AI goes to the movies to identify content you will want to watch | Infographics and Data Visualization | Scoop.it
The digital technology and media and entertainment industries are beginning to come together to solve a common problem—how to extract, unlock, harness and make better use of the massive amounts of video content and data they produce.  Anyone who has ever appeared in or produced a movie, commercial or business video is aware that many times a good portion of ... Read More
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Note: Massive Refactoring in Progress - Design Patterns for Deep Learning Architectures

Note: Massive Refactoring in Progress - Design Patterns for Deep Learning Architectures | Infographics and Data Visualization | Scoop.it
Deep Learning Architecture can be described as a new method or style of building machine learning systems. Deep Learning is more than likely to lead to more advanced forms of artificial intelligence. The evidence for this is in the sheer number of breakthroughs that had occurred since the beginning of this decade. There is a new found optimism in the air and we are now again in a new AI spring. Unfortunately, the current state of deep learning appears too many ways to be akin to alchemy. Everybody seems to have their own black-magic methods of designing architectures. The field thus needs to move forward and strive towards chemistry, or perhaps even a periodic table for deep learning. Although deep learning is still in its early infancy of development, this book strives towards some kind of unification of the ideas in deep learning. It leverages a method of description called pattern languages.

Via Carlos Lizarraga Celaya
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Exercise Caution: The Key Aspect To Pay Attention To For Novice Content Curators

Exercise Caution: The Key Aspect To Pay Attention To For Novice Content Curators | Infographics and Data Visualization | Scoop.it

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Designatude's curator insight, December 16, 2017 4:20 AM

A great read #content

Erica Simoson's curator insight, June 4, 1:34 PM
Some things are worth "Scooping"! Although you read a lot of different resources, only "scoop" the ones that are truly helpful and creditable.
Laurie Bleier's curator insight, July 27, 12:20 PM

Google has stated that “quality content” is a main factor in search engine positioning. Quality content is user friendly, relevant and unique. Sites that adopt these guidelines will enjoy better search engine positioning. The importance of unique content cannot be stressed enough. A common problem with low ranking websites is that they have failed to invest in website copywriters and simply copy/paste from the suppliers’ sites, or plagiarize. This is understandable. If you want to rank well on Google, unique content is vital! Learn more about quality content here.

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On Election Day, most voters use electronic or optical-scan ballots

On Election Day, most voters use electronic or optical-scan ballots | Infographics and Data Visualization | Scoop.it
While more than 46 million Americans already have cast their votes this year, 80 million or so more will be voting on Election Day itself. If you’re one of them, there’s a good chance you’ll use one of two basic forms of voting technology to record your choices: optical-scan ballots, in which voters fill in bubbles, complete arrows or make other machine-readable marks on paper ballots; or direct-recording electronic (DRE) devices, such as touch screens, that record votes in computer memory.


Via Sharrock
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6 Ways Designers Need to Adapt in the Age of AI

6 Ways Designers Need to Adapt in the Age of AI | Infographics and Data Visualization | Scoop.it
Have you noticed anything interesting the last time you uploaded a picture on Facebook? Perhaps you picked up on the fact that sometimes…

Via Siarhei Mardovich
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5-lessons-from-your-brain-l.jpg (1177x1239 pixels)

5-lessons-from-your-brain-l.jpg (1177x1239 pixels) | Infographics and Data Visualization | Scoop.it

Via Siarhei Mardovich
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19 Data Science Tools for people who aren't so good at Programming

This article highlights 19 tools which automate data science / machine learning and eliminate programming skills used in model building.

Programming is an integral part of data science. Among other things, it is considered that a mind which understands programming logic, loops, functions has higher chances of becoming a successful data scientist. So, what about people who never studied programming subject in their school or college ?


Via Carlos Lizarraga Celaya
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How to Increase Social Media Engagement | Propel Marketing Blog

How to Increase Social Media Engagement | Propel Marketing Blog | Infographics and Data Visualization | Scoop.it
Social media is a great way to engage with your audience, but what works for every business and audience will be different. This post will give you some general ideas for improving your social media presence and your posts in such a way that will increase audience engagement.
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Simplified Analytics: A to Z of Digital Transformation

Simplified Analytics: A to Z of Digital Transformation | Infographics and Data Visualization | Scoop.it
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A Snapshot of Current Trends in Visualization

A Snapshot of Current Trends in Visualization | Infographics and Data Visualization | Scoop.it

The five articles featured in this issue of Computing Now represent the best visualization research in recent years. Some demonstrate visualization's significant role as a ubiquitous technology that impacts nearly every walk of life. Others reflect the current and emerging trends in visualization and its subfields.


Via Andreas Maniatis
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Andreas Maniatis's curator insight, January 9, 2014 9:53 AM

A gathering of the most recent influential articles from IEEE's Computing Now, exemplifying current trends in computer-generated visualization.

Loubna Fainine's curator insight, March 23, 2016 8:58 PM

A gathering of the most recent influential articles from IEEE's Computing Now, exemplifying current trends in computer-generated visualization.

Analytics's curator insight, March 24, 2016 3:11 AM

A gathering of the most recent influential articles from IEEE's Computing Now, exemplifying current trends in computer-generated visualization.

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10 Data Visualization Tools To Bring Analytics Into Focus - InformationWeek

10 Data Visualization Tools To Bring Analytics Into Focus - InformationWeek | Infographics and Data Visualization | Scoop.it
Data visualizations can help business users understand analytics insights and actually see the reasons why certain recommendations make the most sense.
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Saving lives with big data analytics that predict patient outcomes

Saving lives with big data analytics that predict patient outcomes | Infographics and Data Visualization | Scoop.it
Cerner's Enterprise Data Hub allows data to be brought together from an almost unlimited number of sources, and that data can be used to build a far more complete picture of any patient, condition or trend. Insights derived from data can help healthcare providers understand health outcomes not just for individuals but for entire groups of individuals or populations. They can identify and predict high risk segments within a population and help take preventive action, creating long term benefits for patients, hospitals, governments and society at large. To unlock the true potential of data for population health, data from a range of disparate sources, including clinics, hospitals, pharmacies, fitness centres and even homes and employment places, would have to be brought together and analysed. However, traditional healthcare IT solutions tended to be limited in scope and restricted to a particular source of data This was the challenge being faced by Cerner Corporation (Cerner), a leader in the healthcare IT space, whose solutions are used in over 35 countries at more than 27,000 provider facilities, such as hospitals, integrated delivery networks, ambulatory offices, and physicians’ offices. Cerner was expanding its historical focus on electronic medical records (EMR) to help improve health and care across the board. To do so, it aimed to assimilate and normalise the world's healthcare data in order to reduce cost and increase efficiency of delivering healthcare, while improving patient outcomes. Mr David Edwards, Vice President and Fellow at Cerner explained, "Our vision is to bring all of this information into a common platform and then make sense of it -- and it turns out, this is actually a very challenging problem." The firm accomplished this by building a comprehensive view of population health on a Big Data platform that’s powered by a Cloudera enterprise data hub (EDH). Management tooling, scalability, performance, price, security, partner integration, training, and support options were key criteria for the selection of a partner. Today, the EDH contains more than two petabytes (PB) of data in a multi-tenant environment, supporting several hundred clients. It brings together data from an almost unlimited number of sources, and that data can be used to build a far more complete picture of any patient, condition, or trend. The end-result is better use of health resources. The platform ingests multiple different Electronic Medical Records (EMRs), Health Level Seven International (HL7[1]) feeds, Health Information Exchange information, claims data, and custom extracts from a variety of proprietary or client-owned data sources, It uses Apache Kafka, a high-throughput, low-latency open-source software platform to ingest real-time data streams. The data is then pushed back to the appropriate data storage, HDFS (Hadoop Distributed File System) cluster or HBase (a noSQL database which enables random, real-time read/write access to data). A blog post by Micah Whitacre, a senior software architect on Cerner Corp.’s Big Data Platforms team, explains how Apache Kafka helped Cerner overcome challenges related to scalability for the near real-time streaming system and in streamlining data ingestion from multiple sources, including ones outside Cerner’s data centres.
Via Yves Mulkers
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5 top data challenges that are changing the face of data centers

5 top data challenges that are changing the face of data centers | Infographics and Data Visualization | Scoop.it
Data is clearly not what it used to be! Organizations of all types are finding new uses for data as part of their digital transformations. Examples abound in every industry, from jet engines to grocery stores, for data becoming key to competitive advantage. I call this new data because it is very different from the financial and ERP data that we are most familiar with. That old data was mostly transactional, and privately captured from internal sources, which drove the client/server revolution.  New data is both transactional and unstructured, publicly available and privately collected, and its value is derived from the ability to aggregate and analyze it. Loosely speaking we can divide this new data into two categories: big data – large aggregated data sets used for batch analytics – and fast data – data collected from many sources that is used to drive immediate decision making. The big data–fast data paradigm is driving a completely new architecture for data centers (both public and private). Over the next series of blogs, I will cover each of the top five data challenges presented by new data center architectures: New data is captured at the source. The volume of data collected at the source will be several orders of magnitude higher than we are familiar with today. For example, an autonomous car will generate up to 4 terabytes of data per day. Scale that for millions – or even billions of cars, and we must prepare for a new data onslaught.  It is clear that we cannot capture all of that data at the source and then try to transmit it over today’s networks to centralized locations for processing and storage. This is driving the development of completely new data centers, with different environments for different types of data characterized by a new “edge computing” environment that is optimized for capturing, storing and partially analyzing large amounts of data prior to transmission to a separate core data center environment.  The new edge computing environments are going to drive fundamental changes in all aspects of computing infrastructures: from CPUs to GPUs and even MPUs (mini-processing units)—to low power, small scale flash storage—to the Internet of Things (IoT) networks and protocols that don’t require what will become precious IP addressing. Let’s consider a different example of data capture. In the bioinformatics space, data is exploding at the source. In the case of mammography, the systems that capture those images are moving from two-dimensional images to three-dimensional images. The 2-D images require about 20MB of capacity for storage, while the 3-D images require as much as 3GB of storage capacity representing a 150x increase in the capacity required to store these images. Unfortunately, most of the digital storage systems in place to store 2-D images are simply not capable of cost-effectively storing 3-D images. They need to be replaced by big data repositories in order for that data to thrive. In addition, the type of processing that organizations are hoping to perform on these images is machine learning-based, and far more compute-intensive than any type of image processing in the past. Most importantly, in order to perform machine learning, the researchers must assemble a large number of images for processing to be effective. Assembling these images means moving or sharing images across organizations requiring the data to be captured at the source, kept in an accessible form (not on tape), aggregated into large repositories of images, and then made available for large scale machine learning analytics.  Images may be stored in their raw form, but metadata is often added at the source.  In addition, some processing may be done at the source to maximize “signal-to-noise” ratios.
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There are 3 Key Performance Indicators for Innovation

There are 3 Key Performance Indicators for Innovation | Infographics and Data Visualization | Scoop.it
Part of the problem with innovating is that it is hard to measure your success. Many groups will work for a long time struggling to develop new ideas, refine them, and measure their likely success. For a long time, the team may feel as though it has not accomplished anything tangible. Indeed, from the standpoint of finished products, it is ... Read More
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Move over Asimov: 23 principles to make AI safe and ethical

Move over Asimov: 23 principles to make AI safe and ethical | Infographics and Data Visualization | Scoop.it
Poised to seriously disrupt the world, will the impacts of artificial intelligence be for the good of humanity, or destroy it? The question sounds like the basis of a sci-fi flick, but with the speed that AI is advancing, hundreds of AI and robotics researchers have converged to compile the Asilomar AI Principles, a list of 23 principles, priorities and precautions that should guide the development of artificial intelligence to ensure it's safe, ethical and beneficial.

The list is the brainchild of the Future of Life Institute, an organization that aims to help humanity steer a safe course through the risks that might arise from new technology. Prominent members include the likes of Stephen Hawking and Elon Musk, and the group focuses on the potential threats to our species posed by technologies and issues like artificial intelligence, biotechnology, nuclear weapons and climate change.

At the Beneficial Artificial Intelligence (BAI) 2017 conference in January, the group gathered AI researchers from universities and companies to discuss the future of artificial intelligence and how it should be regulated. Before the meeting, the institute quizzed attendees on how they thought AI development needed to be prioritized and managed in the coming years, and used those responses to create a list of potential points. The revised version was studied at the conference, and only when 90 percent of the scientists agreed on a point would it be included in the final list.

The full list of the Asilomar AI Principles reads like an extended version of Isaac Asimov's famous Three Laws of Robotics. The 23 points are grouped into three areas: Research Issues, Ethics and Values, and Longer-Term Issues.

Via Wildcat2030
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Digital Transformation helping to reduce patient's readmission

Digital Transformation helping to reduce patient's readmission | Infographics and Data Visualization | Scoop.it
Digital Transformation, Big data Analytics. This is an effort to simplify the area.
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Criminal Offences – Drunk Driving vs High Driving

Criminal Offences – Drunk Driving vs High Driving | Infographics and Data Visualization | Scoop.it

Via Christino Martin
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Interactive 3D maps in Unity

Interactive 3D maps in Unity | Infographics and Data Visualization | Scoop.it
Mantle, the game engine plugin for designing 3D maps in Unity, just leaked screenshots of its new 3D terrain and maps. Developers are no longer constrained to a limite

Via Siarhei Mardovich
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From Big Data to Big Algorithm

From Big Data to Big Algorithm | Infographics and Data Visualization | Scoop.it

Businesses now collect so much data that the resulting insights can not only describe customer behaviour in the present, but predict what it will look like in the future as well.


Via Luca Naso
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Luca Naso's curator insight, November 11, 2015 1:38 PM

Predictive analytics, or the ability to make accurate predictions, is based on good data and good algorithms. Every sensible data scientist knows this. It's good to see that this knowledge is spreading. Data alone, no matter how big, cannot give the right answers.

 

I believe that useful predictive analytics gives *accurate* predictions, not *exact* predictions. Predictive analytics which gives exact predictions is used in Science, not in Business.

LittleBIGJob's curator insight, November 13, 2015 10:43 AM

And to Predicitve analysis!

Hobiana Rakotonirina's curator insight, November 7, 2016 7:29 AM

Predictive analytics, or the ability to make accurate predictions, is based on good data and good algorithms. Every sensible data scientist knows this. It's good to see that this knowledge is spreading. Data alone, no matter how big, cannot give the right answers.

 

I believe that useful predictive analytics gives *accurate* predictions, not *exact* predictions. Predictive analytics which gives exact predictions is used in Science, not in Business.

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Visualizations That Really Work

Visualizations That Really Work | Infographics and Data Visualization | Scoop.it
Know what message you’re trying to communicate before you get down in the weeds.

Via Siarhei Mardovich
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Interactive Storytelling: 7 Examples of Online Graphic Novels - Builtvisible

Interactive Storytelling: 7 Examples of Online Graphic Novels - Builtvisible | Infographics and Data Visualization | Scoop.it
Interactive Storytelling: 7 Examples of Online Graphic Novels - a helpful post from the team at Builtvisible.com

Via Siarhei Mardovich
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12 Algorithms Every Data Scientist Should Know. Emmanuelle Rieuf.

12 Algorithms Every Data Scientist Should Know. Emmanuelle Rieuf. | Infographics and Data Visualization | Scoop.it
The full article about the 12 Algorithms Every Data Scientist Should Know was posted by Mark van Rijmenam. Mark is an entrepreneur and a Big Data strategist.…

Via Carlos Lizarraga Celaya
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How Health & Lifestyle affects Career in Middle East & North Africa

How Health & Lifestyle affects Career in Middle East & North Africa | Infographics and Data Visualization | Scoop.it
Infographic on how your career can affect your overall health and lifestyle. Did you ever stop to think about how your career is affecting your overall health?

Via Christino Martin
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