Upgrade : Get Unstuck from the LAMP Stack to the MEAN Stack

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Upgrade : Get Unstuck from the LAMP Stack to the MEAN Stack



Understanding the role each technology plays in the stack is crucial when moving from LAMP to MEAN for web development. Whether you’re looking for consolidation of technologies or to leverage in-house JavaScript expertise, the MEAN stack can offer a lot to a flexible web development organization. The team here at BACK& wanted to try and help sort out the pros and cons of each stack. So below I’ll detail the MEAN stack, how it compares to the LAMP stack, and offer a few tips for optimizing your choices.

The Operating System

The first choice in any tech stack is the operating system. While the LAMP stack locked the operating system to a variant of Linux, the MEAN stack has no such restrictions. Linux is still a good choice for an app built on MEAN, but it is by no means the only option; any operating system that can run Node.js is a viable alternative.

The Web Server

In the MEAN stack, the web server – provided by Apache in LAMP – is provided by Node.js. This can improve the performance of the application, as Node.js is entirely non-blocking and event-based, allowing for true concurrency among requests. Node.js is lightweight and relatively new, however, which ultimately means that your organization will be largely on its own when it comes to non-standard extensions. While there is active plug-in development for Node.js, the technology is not as matured as Apache. This usually means that you need to write your own plug-ins to cover the areas where Node.js is missing functionality. Additionally, choosing Node.js locks all code on your web server into JavaScript. For new development this isn’t a major concern, but converting a back-end of significant complexity can be time-consuming.

The Data Store

The MEAN stack replaces LAMP’s use of MySQL (or another relational database) with MongoDB (or an equivalent non-relational database). For many web apps, this will be the most significant change. Translating the data in an existing SQL database requires a lot of forethought to eliminate redundant/unnecessary object attributes, and will likely require a custom software suite to accomplish. However, once this is done the database will be much faster for data retrieval.

The Code

MEAN makes use of Express.js and AngularJS to drive web page presentation and control flow, tasks covered by PHP or Python in the LAMP stack. Express.js serves as the controller layer, directing application flow and marshaling data for AngularJS, which handles data presentation. The primary benefits offered by these scripts are a simplified back-end architecture – for example, Express.js weighs in at only 1,143 lines of code – and a purely client-side presentation layer in AngularJS that can be easily embedded into any existing web application. Furthermore, usage of Express.js and AngularJSo n top of Node.js gives your technology stack the added benefit of being entirely in one language, meaning your front-end developers now have the ability to trace all the way down the stack without having to learn another programming language.

Additional Considerations

Probably the biggest choice to be faced when converting from LAMP to MEAN is the choice of data store. While the MEAN stack is designed to work with a non-relational database, there are plug-ins for Node.js that allow the stack to run off of a relational database just as easily. The front end handles everything in JSON, so the only true consideration is how the data is stored before it is retrieved, or the difference between Relational and Non-Relational databases.
Relational databases, with their support for highly complex structured queries, lend themselves well to performing complex calculations with data. Non-relational databases excel at managing operational data, such as a list of objects in a system. The lack of a schema allows for fluid object definitions that don’t require extensive code changes, and by removing the need for extensive and complicated queries the system can often operate more efficiently than a similar architecture build over a relational database.

Conclusion

Converting to the MEAN stack gives your development team a number of benefits, the three most significant being a single language from top to bottom, flexibility in deployment platform, and enhanced speed in data retrieval. However, the switch is not without trade-offs; any existing code will either need to be rewritten in JavaScript or integrated into the new stack in a non-obvious manner. Ultimately the choice to switch to a MEAN stack from LAMP will be based in your organization and the priorities for the project under development.
Courtesy of Blog.BackAnd
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Ag 3.0 : Why IoT, Big Data & Smart Farming is the Future of Agriculture

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The farming industry will become arguably more important than ever before in the next few decades.
The world will need to produce 70% more food in 2050 than it did in 2006 in order to feed the growing population of the Earth, according to the UN Food and Agriculture Organization. To meet this demand, farmers and agricultural companies are turning to the Internet of Things for analytics and greater production capabilities.
Technological innovation in farming is nothing new. Handheld tools were the standards hundreds of years ago, and then the Industrial Revolution brought about the cotton gin. The 1800s brought about grain elevators, chemical fertilizers, and the first gas-powered tractor. Fast forward to the late 1900s, when farmers start using satellites to plan their work.
The IoT is set to push the future of farming to the next level. Smart agriculture is already becoming more commonplace among farmers, and high tech farming is quickly becoming the standard thanks to agricultural drones and sensors.
Below, we've outlined IoT applications in agriculture and how "Internet of Things farming" will help farmers meet the world's food demands in the coming years.

High Tech Farming: Precision Farming & Smart Agriculture

Farmers have already begun employing some high tech farming techniques and technologies in order to improve the efficiency of their day-to-day work. For example, sensors placed in fields allow farmers to obtain detailed maps of both the topography and resources in the area, as well as variables such as acidity and temperature of the soil. They can also access climate forecasts to predict weather patterns in the coming days and weeks.

Farmers can use their smartphones to remotely monitor their equipment, crops, and livestock, as well as obtain stats on their livestock feeding and produce. They can even use this technology to run statistical predictions for their crops and livestock.
And drones have become an invaluable tool for farmers to survey their lands and generate crop data.
As a concrete example, John Deere (one of the biggest names in farming equipment) has begun connecting its tractors to the Internet and has created a method to display data about farmers' crop yields. Furthermore, the company is pioneering self-driving tractors, which would free up farmers to perform other tasks and further increase efficiency.
All of these techniques help make up precision farming or precision agriculture, the process of using satellite imagery and other technology (such as sensors) to observe and record data with the goal of improving production output while minimizing cost and preserving resources.

Future of Farming: IoT, Agricultural Sensors, & Farming Drones




Smart agriculture and precision farming are taking off, but they could just be the precursors to even greater use of technology in the farming world.
BI Intelligence, Business Insider's premium research service, predicts that IoT device installations in the agriculture world will increase from 30 million in 2015 to 75 million in 2020, for a compound annual growth rate of 20%.
The U.S. currently leads the world in IoT smart agriculture, as it produces 7,340 kgs of cereal (e.g. wheat, rice, maize, barley, etc.) per hectare (2.5 acres) of farmland, compared to the global average of 3,851 kgs of cereal per hectare.
And this efficiency should only improve in the coming decades as farms become more connected. OnFarm, which makes a connected farm IoT platform, expects the average farm to generate an average of 4.1 million data points per day in 2050, up from 190,000 in 2014.
Furthermore, OnFarm ran several studies and discovered that for the average farm, yield rose by 1.75%, energy costs dropped $7 to $13 per acre, and water use for irrigation fell by 8%.
Given all of the potential benefits of these IoT applications in agriculture, it's understandable that farmers are increasingly turning to agricultural drones and satellites for the future of farming.

Courtesy of BI
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How To: 4 Ways to Make Big Data Actionable

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The oil and gas industry can be a volatile and unpredictable marketplace. However, this sector plays a fundamental role in global commerce, and if it is not running efficiently, the effects ripple throughout our economy.
There's good reason why: Demand and consumption have been growing steadily over the past few decades. Now, the United States is the third largest producer, generating more than 12 percent of the world’s oil.

So, how did oil and gas operations manage to eliminate inefficiencies, expedite production and improve processes, all the while driving profitability? Somewhat ironically, these operatons become technology companies.
According to Shiva Rajagopalan, CEO of Seven Lakes Technologies, at the end of the day, the COO lives and breathes numbers, and the numbers all need to match.
Rajagopalan is an oil and gas industry expert, who developed his enterprise software solution to give oil and gas operators better insights into the data they need to eliminate inefficiencies in their drilling and production operations. 
In an interview he told me that he believes that companies as large as those in oil and gas, or as small as a five-person startup, can make their data actionable to drive down costs and increase production. Here's how.

1. Be disciplined with big data.


According to a recent survey by The Economist Intelligence Unit, companies that master the emerging discipline of big data management can reap significant rewards and separate themselves from their competitors.
Big data is a term that describes the large volume of data -- both structured and unstructured -- that inundates a business on a day-­to-­day basis. And, thanks to constant tech innovations, such as sensing capabilities, the volume of data produced by a drilling operation is enormous.
“But it’s not the amount of data that’s important. It’s what organizations do with the data that matters,” Rajagopalan said.
For the oil and gas industry, superior data discipline reduces the inaccurate reporting of active well counts and decreases incorrect reserves reported, and penalties assessed due to incorrect updates in the forecasting system.
While oil and gas is an enormous case study for how companies can integrate data into their operations, the benefits they receive hold true for all companies. Implementing proper workflow analytics helps to automate processes and streamline manual business operations to make every arm of the company more efficient.
“With software, organizations can shrink the time it takes to convert data into value in the hands of its operators, Rajagopalan said. "Software improves the quality of analytics and creates confidence within the organization, so those on the front lines are making informed decisions.
"Being disciplined with big data means you get accounting, production and budget systems all communicating effectively."

2. Scale successful results.


The best way for the corner office executive to be on the same page as those in the field is via access to the same information. However, this requires the organization to trust that the data is flowing smoothly across the organization without multiple sources causing errors or bad reporting.
“Repeatability provides employees with a level of trust in the data and systems with which they make decisions,” Rajagopalan said. “They reconcile information less and take accountability more. They continue to respond in this way, and the system continues to shrink their time to achieve business value.”

3. Avoid the garbage-in, garbage-out effect.


In order to balance production and expenses, companies with field operations will require a more surgical effort than in previous decades. The unstructured data, which may not have been considered in the past, becomes relevant.
Working in industries with field workers, all of this data needs to be tagged, integrated and synced together with the appropriate validation checkpoints so everyone from the COO to the asset manager has the exact same view of operations.

4. Work smarter, not harder.


Yes, it’s an age-old cliché, especially for entrepreneurs. But clichés are only repeated so much in the first place because they are so relevant. Oil and gas is an important part of the world’s energy balance. It is simply time to rethink and refocus to outwit and outmaneuver the current market forces.
“Equip your team to drive significant and lasting value. Disparate source systems, ungoverned information and unreliable data block their view to operational excellence. Give them tools to turn meaningful insights into shared action,” Rajagopalan said.
By employing and utilizing intuitive new technology that keeps the company moving in the same direction while enabling actionable insights across the organization, COOs can set aggressive goals and lead their companies to the end zone time after time in a sustainable manner.

Courtesy Of Ayodeji Onibalusi
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5 Key Tips On How To Succeed In Your Social And Digital Marketing Campaigns

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Social Media Networks have evolved rapidly in the past decade or so that they have been in existence and have cumulatively made it easier for everyone to connect and share as the slogan of one of the more popular services says. So the fact of the matter is that they have made it possible for people to connect across both space and time. But the real value of these networks for business are only just beginning to get unlocked thanks to the power of digital marketing and as such this type of marketing has become a must for any enterprise that wants to stay ahead of the curve.

The formal definition of Social Media Marketing is that it entails the use of social media platforms and websites in order to promote a product or a service. Thanks to the highly measurable nature of digital media the social media platforms often contain a motley of tools which are useful in offering a wide-ranging set of actionable information that is of immense value to the platform as a whole. Some of these tools include; data analytics, geographical information, progress reports, demographics, success rate and engagement with your specific digital campaign.

Social Media Channels



This whole sub field has created a new marketing professional who is referred to as the digital marketer. Social Media Marketing which is a sub field of Digital Marketing is largely concerned with targeting potential customers on social media networks where they congregate in their droves. The combined total number of people who interact daily on all the major social media networks is estimated at 2 billion people, slightly under 30% of the total world population. Facebook is the largest player in this genre and on its own it registers traffic of 1.2 billion daily active users.

Used well and employing a predefined method your business stands to reap huge rewards from these platforms. Let us briefly explore the key concepts that are a must have for any expert digital marketer of long standing who wishes to work with some of the biggest brands in helping transform their user engagement and converting website visitors into loyal customers who will purchase the products that you have on offer. Without much further ado let us get down to the key insights that we gleaned;

The Key Takeaways




·         Assemble The Team - As with any campaign, especially a military campaign which is a matter of life and death, what happens in the boardroom before even the first shots are fired is pivotal to the success or otherwise of the campaign. The first step in any campaign is to assemble a winning team.

The dream team should expect social media savvy above any other skill, they should be comfortable using theses platforms to communicate and communicate effectively. This means that they should possesses skills in writing and editing wining copy, photo and video editing, and the knowledge of using metric tools and responding to feedback in real time.

·         Plan The Campaign – Now that you have an enthusiastic team that is armed with the minimum communication skills mentioned above, you need to get into the actual social media marketing campaign planning phase. You must employ a tactical strategy if there is any hope of you getting anywhere fast.

The tactical strategy is hinged upon organizing your campaigns short-term objectives and goals for each of the social media platforms that you will be using. Assign tasks to team members who have the innate potential and skills to see the tasks through. Generate and brainstorm on content ideas and on the channels most appropriate for each piece of content.



·         Choose The Platforms – There are various social media channels and platforms and each is designed for specific content and specific engagements goals. For instance Twitter is a micro-blogging platform is designed for sharing real time information and offering links to the full content sources.

Instagram and SnapChat are image sharing platforms, YouTube and Vimeo are video sharing platforms and Facebook comes across as a veritable jack of all trades geared towards connecting family and friends. So you must exercise great care in choosing the channels well suited for your content. An easy way to go about this is to use social interaction reporting tools to see where your audience is and target them where they are already at.

·         Establish Timelines – Set your goals carefully and realistically and do make sure that you strive to stick to them as much as possible. This will keep the entire team focused on the tasks at hand and help them to direct their efforts and energy to make sure that not only everything runs efficiently but also that the tasks get completed on time.

Some elements which you must include in your calendar include; categories, keywords, article types, content formats, promotion and marketing, tracking dates and the optimal times to promote certain types of content. Ideally you should have a worksheet where you manage all these tasks.



·         Stick To Your Voice - This point belongs to all forms of marketing regardless of whether it is old school or new school marketing and it cannot be over emphasized. The fact of the matter is that when your target audience interacts with your content (audio-visual or textual) they are not only hearing your message but they are also interacting with you in a very rich way.

Personal and Emotional




They are hearing the voice of the company and trying to make sense of and assimilate many points of information including tone, language, delivery, intent and much, much more. In effect they are interacting with your company on a highly personal and emotional level.

Make sure that this interaction is a true reflection of your company and that the message will make them want to do business with you. The end game of the social marketing or any digital marketing campaign for that matter is to convert people into paying customers, never lose sight of this in your efforts, if you adhere to the above points then you will be well on your way to success.



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The Top 10 AI And Machine Learning Use Cases Everyone Should Know About

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Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn.
Very basically, a machine learning algorithm is given a “teaching set” of data, then asked to use that data to answer a question. For example, you might provide a computer a teaching set of photographs, some of which say, “this is a cat” and some of which say, “this is not a cat.” Then you could show the computer a series of new photos and it would begin to identify which photos were of cats.
Machine learning then continues to add to its teaching set. Every photo that it identifies — correctly or incorrectly — gets added to the teaching set, and the program effectively gets “smarter” and better at completing its task over time.
It is, in effect, learning.
       1.       Data Security

Malware is a huge — and growing — problem. In 2014, Kaspersky Labsaid it had detected 325,000 new malware files every day. But, institutional intelligence company Deep Instinct says that each piece of new malware tends to have almost the same code as previous versions — only between 2 and 10% of the files change from iteration to iteration. Their learning model has no problem with the 2–10% variations, and can predict which files are malware with great accuracy. In other situations, machine learning algorithms can look for patterns in how data in the cloud is accessed, and report anomalies that could predict security breaches.

        2.      Personal Security
If you’ve flown on an airplane or attended a big public event lately, you almost certainly had to wait in long security screening lines. But machine learning is proving that it can be an asset to help eliminate false alarms and spot things human screeners might miss in security screenings at airports, stadiums, concerts, and other venues. That can speed up the process significantly and ensure safer events.
        3.      Financial Trading
Many people are eager to be able to predict what the stock markets will do on any given day — for obvious reasons. But machine learning algorithms are getting closer all the time. Many prestigious trading firms use proprietary systems to predict and execute trades at high speeds and high volume. Many of these rely on probabilities, but even a trade with a relatively low probability, at a high enough volume or speed, can turn huge profits for the firms. And humans can’t possibly compete with machines when it comes to consuming vast quantities of data or the speed with which they can execute a trade.
        4.         Healthcare

Machine learning algorithms can process more information and spot more patterns than their human counterparts. One study used computer assisted diagnosis (CAD) when to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed. Additionally, machine learning can be used to understand risk factors for disease in large populations. The company Medecision developed an algorithm that was able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

5.      Marketing Personalization

The more you can understand about your customers, the better you can serve them, and the more you will sell.  That’s the foundation behind marketing personalisation. Perhaps you’ve had the experience in which you visit an online store and look at a product but don’t buy it — and then see digital ads across the web for that exact product for days afterward. That kind of marketing personalization is just the tip of the iceberg. Companies can personalize which emails a customer receives, which direct mailings or coupons, which offers they see, which products show up as “recommended” and so on, all designed to lead the consumer more reliably towards a sale.

3.      Fraud Detection

Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

4.      Recommendations
You’re probably familiar with this use if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next. These recommendations are getting smarter all the time, recognizing, for example, that you might purchase certain things as gifts (and not want the item yourself) or that there might be different family members who have different TV preferences.
5.      Online Search
Perhaps the most famous use of machine learning, Google and its competitors are constantly improving what the search engine understands. Every time you execute a search on Google, the program watches how you respond to the results. If you click the top result and stay on that web page, we can assume you got the information you were looking for and the search was a success.  If, on the other hand, you click to the second page of results, or type in a new search string without clicking any of the results, we can surmise that the search engine didn’t serve up the results you wanted — and the program can learn from that mistake to deliver a better result in the future.
6.      Natural Language Processing (NLP)
NLP is being used in all sorts of exciting applications across disciplines. Machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need. It’s being used to translate obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case.
7.      Smart Cars

IBM recently surveyed top auto executives, and 74% expected that we would see smart cars on the road by 2025. A smart car would not only integrate into the Internet of Things, but also learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.

 Courtesy of Bernard Marr
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