Edge Computing

I learned about edge computing a few years ago. It is a method of getting the most from data in a computing system by performing the data processing at the "edge" of the network. The edge is near the source of the data, not at a distance. By doing this, you reduce the communications bandwidth needed between sensors and a central datacenter. The analytics and knowledge generation are right at or near the source of the data.

The cloud, laptops, smartphones, tablets and sensors may be new things but the idea of decentralizing data processing is not. Remember the days of the mainframe computer?

The mainframe is/was a centralized approach to computing. All computing resources are at one location. That approach made sense once upon a time when computing resources were very expensive - and big. The first mainframe in 1943 weighed five tons and was 51 feet long. Mainframes allowed for centralized administration and optimized data storage on disc.

Access to the mainframe came via "dumb" terminals or thin clients that had no processing power. These terminals couldn't do any data processing, so all the data went to, was stored in, and was crunched at the centralized mainframe.

Much has changed. Yes, a mainframe approach is still used by businesses like credit card companies and airlines to send and display data via fairly dumb terminals. And it is costly. And slower. And when the centralized system goes down, all the clients go down. You have probably been in some location that couldn't process your order or or access your data because "our computers are down."

It turned out that you could even save money by setting up a decentralized, or “distributed,” client-server network. Processing is distributed between servers that provide a service and clients that request it. The client-server model needed PCs that could process data and perform calculations on their own in order to have applications to be decentralized. 

Google car

Google Co-Founder Sergey Brin shows U.S. Secretary of State John Kerry the computers inside one of
Google's self-driving cars - a data center on wheels. June 23, 2016. [State Department photo/ Public Domain]

Add faster bandwidth and the cloud and a host of other technologies (wireless sensor networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing) and you can compute at the edge.  Terms like local cloud/fog computing and grid/mesh computing, dew computing, mobile edge computing, cloudlets, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented reality and more that I haven't encountered yet have all come into being.

Recently, I heard a podcast on "Smart Elevators & Self-Driving Cars Need More Computing Power" that got me thinking about the millions of objects (Internet of Things) connecting to the Internet now. Vehicles, elevators, hospital equipment, factory machines, appliances and a fast-growing list of things are making companies like Microsoft and GE put more computing resources at the edge of the network. 

This is computer architecture for people not things. In 2017, there were about 8 billion devices connect to the net. It is expected that in 2020 that number will be 20 billion. Do you want the sensors in your car that are analyzing traffic and environmental data to be sending it to some centralized resource - or doing it in your car? Milliseconds matter in avoiding a crash. You need the processing to be done on the edge. Cars are "data centers on wheels." 

Remember the early days of the space program? All the computing power was on Earth. You have no doubt heard the comparison that the iPhone in your pocket has hundreds or even thousands of times the computing power of the those early spacecraft. That was dangerous, but it was the only option. Now, much of the computing power is at the edge - even if the vehicle is also at the edge of our solar system. And things that are not as far off as outer space - like a remote oil pump - also need to compute at the edge rather than needing to connect at a distance to processing power. 

Plan to spend more time in the future at the edge.

Monetizing Your Privacy

data

Data is money. People are using your data to make money. What if you could sell, rather than give away, your private data? Is it possible that some day your data might be more valuable than the thing that is supplying your data?

John Ellis deals with big data and how it may change business models. He was Ford Motor Company’s global technologist and head of the Ford Developer Program, so cars are the starting place for the book, but beyond transportation, insurance, telecommunications, government and home building are all addressed. His book, The Zero Dollar Car: How the Revolution in Big Data will Change Your Life, is not as much about protecting our data as users, as it is about taking ownership of it. In essence, he is suggesting that users may be able to "sell" their data to companies (including data collectors such as Google) in exchange for free or reduced cost services or things.

I'm not convinced this will lead to a free/zero dollar car, but the idea is interesting. You are already allowing companies to use your data when you use a browser, shop at a website, use GPS on your phone or in a car device. The growth of the Internet of Things (IoT) means that your home thermostat, refrigerator, television and other devices are also supplying your personal data to companies. And many companies, Google, Apple and Amazon are prime examples, use your data to make money. Of course, this is also why Google can offer you free tools and services like Gmail, Documents etc.

Ellis talks about a car that pays for itself with your use and data, but the book could also be the Zero Dollar House or maybe an apartment. Big technology companies already profit from the sale of this kind of information. Shouldn't we have that option?

Duly noted: the data we supply also helps us. Your GPS or maps program uses your route and speed to calculate traffic patterns and reroute or notify you. The health data that your Apple watch or fitness band uploads can help you be healthier, and in aggregate it can help the general population too.

I remember years ago when Google began to predict flu outbreaks in geographic areas based on searches for flu-related terms. If all the cars on the road were Net-enabled and someone was monitoring the ambient temperature and their use of windshield wipers, what could be done with that data? What does an ambient temperature of 28 F degrees and heavy wiper use by cars in Buffalo, New York indicate? Snowstorm. Thousands or millions of roaming weather stations. And that data would be very useful to weather services and companies (like airlines and shipping companies) that rely on weather data - and are willing to pay for that data.

Am I saying that you should give up your privacy for money or services? No, but you should have that option - and the option to keep all your data private.

Harvard Partners with 2U for Online Program

Harvard University has perhaps the ultimate university branding in the United States and a multi-billion-dollar endowment and has worked with online course provider edX to offer MOOCs and online courses. But Harvard announced this week that three of its schools would create a new business analytics certificate program with 2U, an online program management company.

I have no real knowledge of 2U https://2u.com and this collaboration between 2U and Harvard caught me by surprise.

Professors at the Harvard Business School, the John A. Paulson School of Engineering and Applied Sciences, and the department of statistics in Harvard's main college, the Faculty of Arts and Sciences, will create a program to teach students how to leverage data and analytics to drive business growth.

Rather than undergrads or grad students, this is aimed at executives in full-time work. It will use 2U’s online platform and will feature live, seminar-style classes with Harvard faculty members.

This is no MOOC. The program will cost around $50,000 for three semesters, with an estimated time requirement of 10 hours per week.

more at https://www.seas.harvard.edu/news/2017/08/hbs-seas-and-fas-partner-with-2u-inc-to-offer-harvard-business-analytics-program

Social Media Research Tools

Social media can be viewed as a distraction. Some people rely on it as a news source. Companies use it for marketing purposes. And some of us study it in a more academic way.

In higher education, we at least touch on all four approaches. Some teachers find it all a useless annoyance. In communications and journalism courses, it is studied as another medium. In business school, it has moved into marketing and advertising courses and conversations. Beyond the theories of social media use, there is learning about the design and analysis of social media.

Studying online communities and social networks is leading to developing new tools and methods for analyzing and visualizing social media data. One of the better compilations of social media research tools has been curated by researchers at the Social Media Lab at Ted Rogers School of Management, Ryerson University.  Their site has more than fifty tools that they have reviewed academically. Many are free tools to use and are fairly simple to implement and use to collect data for analysis, while others require some programming experience.    

http://socialmediadata.org/social-media-research-toolkit/


Using MOOC Data

codeThe MOOC - massive and online and sometimes open - has been around long enough that there is now massive data collected about these courses and their participants. And yet, there is not much agreement about what it all means for changing education online or offline.

EDUCAUSE recently posted "Harvard and MIT Turn MOOC Data into Knowledge" which uses data from the Harvard/MIT edX Data Pipeline. This is an open-source effort to manage MOOC data among higher education institutions.

What can we learn from all the clicks within learning management systems (edX, Canvas, Moodle, etc.)? We could find out how much time students spend reading texts, watching videos, and engaging with fellow students in discussions.

Data at the MOOC scale offers possibilities for new insights.This Harvard/MIT partnership also offers possibilities with MIT perhaps focusing on analyzing Big Data and Harvard, via its Graduate School of Education, addressing the educational responses.

Using edx2bigquery (Google’s BigQuery) and XAnalytics (a dashboard that connects to Pipeline), will allow other institutional representatives to interact with edX data. This is all beyond my experience, but I look forward to results.