An overview of Augmented reality applications and their future impact on AI

Application

Category

Notes

 

 

Toyota – vehicle demo

 

Automotive

Created with agency Brandwidth, the app works by overlaying images of the inner workings of the Hybrid drivetrain onto physical vehicles.

Customers can interact with the app to be able to discover more about the car’s (C-HR model) key features, like the hybrid’s motor, battery, and its fuel tank.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

Aurasma app

Educational

The Aurasma app, for example, is already being used in classrooms so that students can view their classes via a smartphone or tablet for a more rich learning environment.

 

https://www.inc.com/james-paine/10-real-use-cases-for-augmented-rea…

 

Quiver

Educational

The app has a set of coloring pages that make cells pop off the page and let children spin a globe in midair. Quiver lets students go beyond the pictures in their textbooks to interact with three-dimensional figures.

 

https://www.edutopia.org/blog/ar-apps-for-student-learning-monica-b…

 

 

Elements 4D

Educational

This app lets students combine different elements to see chemistry in action. Teachers can print out and assemble blocks that become trigger images for an AR experience

 

https://www.edutopia.org/blog/ar-apps-for-student-learning-monica-b…

 

 

Blippar

Educational

One of my favorites is how Blippar is used with Brainspace magazine. You can scan the Brainspace cover and inside pages to connect with interactive content that brings the two-dimensional presentation to life.

 

https://www.edutopia.org/blog/ar-apps-for-student-learning-monica-b…

 

 

Arloon Plants

Educational

With this app, students can explore interactive plants to learn about structure and parts.

 

https://www.edutopia.org/blog/ar-apps-for-student-learning-monica-b…

 

 

Math alive

Educational

Math alive uses AR software downloaded to a computer, a camera, and special cards. Students and teachers place the trigger cards under a camera to practice counting and basic numeracy skills.

 

https://www.edutopia.org/blog/ar-apps-for-student-learning-monica-b…

 

 

Ai.R-Cord

Educational

This app is aimed at helping elementary school students learn concepts by using augmented reality experiences.

 

https://mobidev.biz/blog/augmented-reality-future-trends-2018-2020

 

 

Pokémon Go

Entertainment

The most popular AR game till date is Pokémon Go which allows users to catch virtual Pokémon that are hidden throughout the map of the real world. It uses real locations to encourage players to far and wide in the real world to discover Pokemon. The game enables the players to search and catch more than a hundred species of Pokemon as they move in their surroundings

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

REAL STRIKE

Entertainment

The users get a real life shooting experience in this game and can record their fights and also create their own videos.

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

YouTube – Beauty Try-On

 

Entertainment

the feature will allow users to virtually try-on make-up through the YouTube app

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

Acura

 

Entertainment

This month, Acura live-streamed an AR race featuring its 2018 TLX sedan. From behind the wheel of Acura’s newest model, four influencers competed in the “What a Race” AR experience for the fastest overall time during three-lap individual runs.

 

https://business.adobe.com/sea/resources/5-realworld-examples-of-au…

 

Disney

 

Entertainment

Disney Research developed technology that makes coloring book characters 3D as they are being colored.

 

https://business.adobe.com/sea/resources/5-realworld-examples-of-au…

 

Zombies, Run!

Entertainment

already allow an AR experience (gamified), via audio, while running outdoors.

 

https://www.vxchnge.com/blog/augmented-reality-statistics

 

Rekorderlig – mixed reality experiential marketing ‘Rekorderland’

Marketing

a mixed reality experience location in London’s Southbank for the summer.

There for six weeks, the concept was based on Midsummer, one of Sweden’s most celebrated traditions. The experience involves visitors wearing Magic Leap AR headsets, to view holographic imagery and light waves together, blurring the lines between the real and virtual worlds. Visitors also will be able to bring their Rekorderlig drinks to life using Snapchat codes in the bar area.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

 

Modiface on Amazon

Marketing/Retail

L’Oréal’s Modiface is another of the best-known examples of AR within retail, with the feature allowing customers to digitally try on make-up through the brand’s app.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

Foot Locker – in-store poster

Marketing

Foot Locker is one of the coolest examples to date, with the retail brand using a Snapchat AR filter to create the illusion that LeBron James – basketball player for the LA Lakers – is bursting out of a Nike poster in-store

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

‘Magnum Beauty Store’ pop-up experience

Marketing

Combining beauty and ice-cream, the ‘Magnum Beauty Store’ pop-up experience involved seven different zones, where visitors could engage in activities such as the personalisation of ice creams and eyebrow shaping.

There was a large AR element, too, relating to the theme of “Release Your Beast”. This involved visitors engaging with Magnum’s ‘four beasts’ – the leopard, polar bear, lion and tiger – through an interactive LED wall.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

VF – virtual mannequins

Merketing

Timberland and Vans – has opened a new retail space One of the space’s key features is avatar-based virtual mannequins, which showcase various seasonal looks in 3D. Visitors can use touchscreen displays to view products in different scenarios, as well as change features such as lighting and context.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

Pepsi MAX

 

Marketing

PepsiCo recently pranked commuting Londoners with an AR-enabled bus stop display. Travelers were shown a prowling tiger, a meteor crashing, and an alien tentacle grabbing people off the street.

 

https://business.adobe.com/sea/resources/5-realworld-examples-of-au…

 

AccuVein

Medical

AccuVein is a handheld device that can scan the vein network of a patient that leads to a 45% reduction in escalations. Surgeons can plan procedures before making the first cut, models can be made of tumors, and AR diagnostic tools can model disease conditions.

 

https://www.bernardmarr.com/default.asp?contentID=1540

 

AR GPS Drive/Walk Navigation

Navigation

The application makes use of the smartphone’s GPS and camera to execute a car navigation system with an augmented reality-powered technology.

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

AR GPS Compass Map 3D

Navigation

The app shows a 3D compass that gets fused with the camera image and shows your current location from GPS on a separate map with adjustable size

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

Gatwick airport passenger app

Navigation

passengers can use the AR maps from their mobile phone to navigate through the airport

 

https://www.bernardmarr.com/default.asp?contentID=1540

 

Tactical Augmented Reality (TAR)

Navigation

essentially an eyepiece that helps soldiers precisely locate their positions as well as the locations of others—both friend and foe

 

https://business.adobe.com/sea/resources/5-realworld-examples-of-au…

 

AUGMENT

Retail

Augment allows its users to see their products in 3D in a real-life environment and in real-time through tablets or smartphones to drive sales and improve user engagement

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

Ikea Place

Retail

For those who have purchased furniture and discovered once it was delivered it didn’t work in the space, the Ikea Place app will help you avoid that predicament in the future. The app was built using Apple’s ARKit technology, and it allows you to scan your room and design the space by placing Ikea objects in the digital image of your room to create a new environment with the new products.

 

https://www.bernardmarr.com/default.asp?contentID=1540

 

 

Dulux Visualiser

Retail

Dulux Visualiser helps you try out a shade of paint for your room before you buy. Just use your smartphone camera to scan your room and virtually paint it with any color of the rainbow.

 

https://www.bernardmarr.com/default.asp?contentID=1540

 

 

Measured by Lowe’s

Retail

, a virtual tape measure that can be used inside and out, and Envisioned by the Mine (owned by Lowe’s) which allows you to place 3D images of furnishings and accessories into your home or commercial space.

 

https://www.bernardmarr.com/default.asp?contentID=1540

 

Sephora Virtual Artist

Retail

Sephora uses AR technology to allow customers to try out different looks and eye, lips and cheek products as well as colors right on their own digital face.

 

https://www.bernardmarr.com/default.asp?contentID=1540

 

Rolex virtual try-on

Retail

The company has developed a virtual try-on experience where prospective customers can try out different styles and models

 

https://www.bernardmarr.com/default.asp?contentID=1540

 

ASOS – virtual catwalk

 

Retail

The feature works when a user points their smartphone camera at a suitable flat surface and clicks the ‘AR’ button on the product page in the app. Models then virtually appear, giving the customer a new and more intimate way of viewing products.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

Gucci – ‘try on’ shoes in AR

 

Retail

Pointing their smartphone camera downwards, users can choose to see a digital overlay of 19 different sneakers on their own feet, swiping left or right to change to a different pair. The app also allows users to take photos, which can then be shared on social media or in messaging apps.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

 

Adidas – more virtual sneakers

 

Retail

, it partnered with Snapchat to create an AR lens for customers to virtually preview its new Ultraboost 19 running shoes.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

Sun-Seeker

Science

Sun-Seeker is an AR app which provides a flat compass view and a 3D view showing the solar path, its hour intervals, its equinox, winter and summer solstice paths, sunrise and sunset times, twilight times, magic hours and also a Map view showing solar direction for each daylight hour.

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

Man City – AR-driven stadium tour

 

Sports

In 2019, the football club has integrated AR into its stadium visitor experience, introducing an immersive tour in celebration of its 125th birthday.

 

Visitors of the tour will be able to use AR to ‘sit next to’ and ‘interact’ with Manchester City manager, Pep Guardiola, at the top table of a press conference. Alongside this, the tour includes 3D holographic content, and a 360-degree cinema screen – all designed to offer fans a truly immersive experience.

 

https://econsultancy.com/14-examples-augmented-reality-brand-market…

 

 

ARGON4

Tech

This is a fully-featured web browser that has the ability to display augmented reality content created with the argon.js Javascript framework. argon.js makes it easier for adding augmented reality content to the web applications in a platform and technology-independent way and supports the real-time AR capabilities of the Argon4 Browser.

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

AR Browser SDK

Tech

This is a browser created by ARLab. This browser allows the users to add augmented reality geolocation view to the Android and or iOS application in less than 5 minutes.

 

https://www.newgenapps.com/blog/augmented-reality-apps-ar-examples-…

 

heads-up displays (HUDs)

Tech

The HUD can make drivers aware of potential hazards that might have gone unnoticed without posing a distraction or obstructing their view of the road. Nissan’s Invisible-to-Visible (I2V) technology already uses AR and AI to make drivers aware of potential hazards, such as nearby objects.

 

https://mobidev.biz/blog/augmented-reality-future-trends-2018-2020

 

Source Prolead brokers usa

Interpretive Analytics in One Picture

Source Prolead brokers usa

Job Scope For MSBI In 2021

MSBI or Microsoft Business Intelligence is a effective software program application tool superior through Microsoft to offer the wonderful employer intelligence solutions. It includes numerous tool which assist in finding answers for Data mining queries and commercial enterprise organization intelligence systems.

Skill required:

MSBI aspirants want to decorate the subsequent abilties list to provide effective paintings. In addition to those competencies, he/she need to have an first-rate records of Data Warehousing mind with bodily information modeling. Let’s see the talents list. 

• Database skills: An professional desires to be privy to software utility development and its pleasant practices. He/She has a eager know-how of information designing, mining, and growing numerous gadgets, reporting, strategies, attempting out, deployment suggestions, and loads of such operations. 

• Testing competencies: When software is developing, it’s miles required to undergo finding out at every degree. Testing is needed for numerous reasons. We can check every records is processed or displayed properly. With SSIS and SSAS tool, it ensures the mistake coping with for database devices.

• Troubleshooting skills: An professional need to be privy to troubleshooting fashionable performance-associated troubles. He/She has to recognize the inspiration reason of every trouble and perform troubleshooting to remedy the problem. He/She need to be aware about how hundreds information switch takes area at some point of taking walks hours or maybe common network troubles. 

• Communication talents: Good communication competencies are essential abilities for every way characteristic. Here, the MSBI  course helps professional need a good way to deliver the on foot approach to the crew thru explaining the queries. He/She ought to be a high-quality listener as well. 

Job Roles and Responsibilities

Should offer several analyses and reviews of given facts as ordinary with the requirement. Based on Data requests, he/she need to capable of put together and execute queries. 

The MSBI expert is also accountable for accumulating the facts from the quality-of-a-kind belongings to store it in a statistics warehouse for further use. In addition, he/she need to check, debug, and troubleshoot the BI solutions for better consequences. Furthermore, he/she need to hold the confidentiality of commercial enterprise evaluations thru securing them.

 

MSBI Career opportunities

The global goals for Business Intelligence tool for statistics analysis, designing, and deploying the opinions for facts queries. As such, MSBI  online training profession opportunities are developing now and similarly. There are numerous project roles that search for BI-based totally competencies. They are as follows:

• Business Intelligence Analyst

• BI Developer

• BI Semantic Model Developer

• BI Manager

• BI Consultant

• BI Project Manager

• BI Administrator

• SQL Server BI Developer

These days BI tool is applied in nearly all industries together with Software, Retail, Healthcare, Government obligations, and loads of others. The career possibilities can be going to growth with the useful resource of similarly years. 

Top companies hiring MSBI Developers

The following are top agencies which can be hiring MSBI professionals:

• Wipro

• TCS

• Birlasoft

• Accenture

• Cognizant

• Tech Mahindra

• Fiserv

• Deloitte India Ltd

• Barclays

• HCL

• DELL

• IBM

• CSC

• Infogain

Bank of America and plenty of greater.

 

Salaries and growth

 An access-degree Business Intelligence Developer with lots an lousy lot a good deal much less than 1-300 and 65 days work experience can expect to earn round INR 2,96,320. 1-4 years of labor experience can assume to earn spherical INR 4,54,920. With 5-9 years of difficult paintings, professional specialists can assume to earn spherical INR 8,76,577. And a tremendously skilled BI Developer can expect to earn round INR 12,30,787. 

 Today, agencies require extra MSBI experts to maintain statistics first-rate reporting, confidentiality, and protection. He/She need to be aware about the current-day dispositions within the marketplace that require extra intelligence. Changing records and abilities also can need to enhance career growth at the element of commercial enterprise development. It emphasizes to make better business enterprise selections. 

Reasons to pick out MSBI as a profession

MSBI allows to overcome all IT troubles at a very much less pricey fee. Let’s take a look at some of the reasons to select out MSBI as a career. 

1. Easy Data Visualization and Exploration

Data visualization and exploration is the most vital component to be taken into consideration on this BI global. MSBI is the awesome answer that lets in a corporation in every possible way. It is the motive for the wider adoption of MSBI and massive career opportunities on this BI place.

1. End-to-prevent commercial enterprise business enterprise solutions

MSBI is a great tool that results inside the deployment of business organization products successfully and enhances the exquisite of business enterprise offerings. It facilitates to make robust company options as well, with sturdy records analytics and statistics mining.

1. Intuitive Dashboard and Scorecards

Using MSBI Dashboards, we are capable of get admission to any facts supply. Scorecard combines information from unique belongings and converts them to a single personalized enjoy. 

1. Supports Web offerings

MSBI can be covered with 1/3-celebration rolls or internet offerings to make it a more exquisite tool for the  organization. We can combine one of the most preferred net offerings which might be important for the business enterprise. 

1. Easy to use

Earlier, BI equipment are utilized by professionals, however now MSBI can be utilized by nearly each person who has easy knowledge. It is straightforward to use and maintains in assessment to all extremely good conventional BI system. 

Based on Microsoft Excel Features

A individual who is familiar with Microsoft Excel can able to art work on MSBI with out problems. With Excel, it is simple to build up, keep, and control the information. Further, the equal facts may be used to generate reviews and files.

Source Prolead brokers usa

How to Improve Content Marketing Results with Big Data

Companies and businesses around the world have been forced during the last year to shift their activity online. This is because people are spending more time online than ever. There, you can promote your business, organize contests, connect with customers, and improve your brand awareness. At the base of all these activities stay content marketers, who are working every day to identify the needs and expectations of the online communities. Every business has its profile and, of course, a target audience. To reach, engage, and convert it, you need time, effort, knowledge, and data. 

Data is collected by every business or platform. Your business collects and stores data on the behavior of your customers online or on the engagement your social media posts are getting. Big data is represented by all this data a customer is generating, data that can offer you tremendous insight into the psychology of your customer. But how to use big data to your advantage? How to improve content marketing results with big data? Find out below. 

Target Audience 

An important step of a good marketing strategy is the plan that is made before actually starting. And something essential to decide ahead is your target audience. Even though you may think that your products or services suit a wide majority of people, you need to define your target audience. Like this, you will offer content, products, and services that meet their needs and expectations. And this can make the visitors of your website be converted to customers. So, to get better results with your content marketing strategy, you can use big data to identify your target audience. 

You already collect information about your community, but some of it can be of huge help when defining the target audience. Analyze information about their likes and dislikes, about the mentions on social media, or the sources of traffic. Like this, it will be easier to understand who your followers are. 

Marketing Goals 

Every marketing strategy has its goals. This helps both the marketing team and the business understand how to focus their activity in the period that follows but also track the progress they have made. As a ninjaessay who offers uni assignment help on productivity topics highlights, setting goals is helpful because they give you a general direction to follow. 

Before actually starting to apply your marketing strategy, it is important to set some goals. And big data can help any business and marketing team do this. Big data provides you an insight into how your goals are implemented or the changes that have been made. Big data allows businesses to analyze their progress and propose improvements so that the goals they have set will be accomplished. 

Converting the Visitors 

Experts who offer essay help on marketing topics are aware of the fact that one of the goals of any marketing strategy is to convert the visitors into customers. This is something all businesses want to do, no matter their domain. They depend on customers to increase profits and growth, so they are a central piece of any marketing strategy. 

But converting visitors into customers can sometimes be difficult. Some visitors turn into customers just for a short time, while others become your lifelong ones. Understanding which are the factors that make a customer stay more in the purchase cycle can help any business improve its content marketing results. And all these insights come from big data. 

Big data can help marketers understand which are the things that attract the customers and which are the ones that keep them away. More important, big data offers valuable insights into the performance of the content you have created, to understand how it is seen by visitors. You need to create quality over quantity and offer content that is valuable and relevant. You need to understand the moments that made a visitor become your customer. And to do this successfully, you need the insights big data has. 

Increase Their Retention 

There are many mistakes content marketers make, but one of these is focusing too much on attracting new visitors and followers, and too little on their retention. Acquiring new visitors is also a little bit more expensive than increasing their retention. In your content marketing strategy, you need to also focus on the retention of customers. It is easier to engage the ones that you already have than work on attracting new ones. However, the latter will happen anyway. 

Big data can offer you insights into some metrics that are essential when we look at customer retention. Big data offers information on the total number of subscribers and their behavior online. For example, you might want to know which is the average time spent on your website. You might want to know which type of content has the highest engagement. You might want to learn more about customers’ click-through rates. You will want to know more about the general level of satisfaction your customers have. And big data offers you exactly what you need. 

All this information will help both the business and its content marketing team to understand which customers are satisfied and not with your services. This allows for further interventions, such as increasing the satisfaction of the disgruntled ones and making the satisfied ones even more loyal. 

Final Words 

Data is collected online and offline by every business and company. And big data can offer the insights you need to build a powerful content marketing strategy that increases your brand awareness. Big data is important because it can help you identify patterns, set goals, and define your target audience. Moreover, it helps you convert visitors into customers and increase their retention.

Source Prolead brokers usa

Four Alternative Data Trends to Watch in 2021

Given the recent pandemic’s uncertainty and disturbances, it is not surprising that investors seek more information before making a decision. Although alternative data is not new, it has been the recent focus for many investing firms. A recent study concluded that the global alternative data market size would grow at a 44% CAGR from 2020 to 2026, reaching an impressive $11.1 billion. The following sections explore the importance of alternative data and its predicted trends this year. 

What is alternative data?

Alternative data datasets contain any type of information that is not typically enclosed in traditional sources, such as financial reports or SEC filings. For instance, any information sourced from websites, web traffic, satellites, sensors, social media, or cell phones are considered alternative data. 

This information is now increasingly being used by investors to make better investment decisions. There is only one purpose for using these data: to have better insight into the markets and boost portfolio returns. 

• A new role for alternative data

Before 2020, the prominent role of using alternative data was to make better and more profitable long-term decisions, while ESG also had an upward trend. Despite this, as the pandemic changed the world as we knew it, investors have started to use alternative data for an entirely new purpose: immediate risk management. As businesses struggled to remain open, the workforce turned to remote work, while investors actively sought alternative information to better understand market volatility from different perspectives. 

Alternative data has already been used by hedge funds for some years now. Still, there is evidence that it is quickly expanding to numerous other industries, including energy, retail, transportation and logistics, and more. Machine learning methods have also been on the rise due to investors’ interest in collecting and analyzing large datasets. 

• New alternative data types 

One of the recent emerging trends refers to consumption data analysis. This offers transaction-related information to investors that can help them predict business sales, so it is not surprising that this trend is expected to spike in 2021 and beyond. 

Although leveraging machine learning methods is not so mainstream just yet, early adopters can benefit from major advantages. For example, in 2018, it was rumored that Cambridge Analytica collected information from 50 million Facebook profiles to use in the Trump presidential campaign back in 2016. Despite this, Facebook’s stock price did not experience any slowdown until later that year – primarily due to investors’ reaction to Facebook’s announcement that user growth is slowing down. 

In other words, investors using alternative data can identify these events and act on them before the general market, simply because most investors are pretty inefficient when it comes to digesting unstructured information. Those who are proficient in machine learning can determine which events will impact the stock price, resulting in higher returns. 

• Lessons from 2020: sustainability and ESG performance

ESG has become a significant point of interest for investors, especially as policymakers, such as the European Commission, have proposed different methods to improve the integration of sustainability factors in the investment decision-making process. 

In 2021, it is expected that this focus will increase, along with individual investors’ interest in sustainability. For example, 64% of active retail investors focused on sustainable investment funds in 2017 compared to 2012, according to a study. 

However, generating profits with ESG strategies has become more difficult since most investors have access to the same resources: the company’s sustainability reports. Another issue of this traditional source of data is that the company itself reports the information, so it may not reflect the reality. In 2021, investors are expected to become creative and start using alternative data, such as social media information, to discover unique signals and create profitable investment strategies. 

• Aggregation of alternative data

Alternative data have always been quite fragmented, coming from a wide variety of sources. In 2021, it is expected that this information will turn into a more organized mass of data as alternative data providers step in to offer quality, cleaner datasets. 

For example, investors who focus on certain types of stocks, such as retail, may want to access alternative data relevant to the particular industry, combining geolocation information with transactions and social sentiment. Combining these different datasets into a curated, well-presented form can provide a significant advantage. 

Even more, this aggregation of alternative data can define more clearly the previous uncertain international market opportunities. In other words, combining different datasets can make emerging markets, such as China, more transparent and safer for investors. This aggregation allows investors to remain informed at all times and keep up with international market opportunities. 

Summary

All in all, there is no surprise that alternative data is on the rise. In a world driven by uncertainty and volatility, more and more investors seek alternative data datasets that offer them a glimpse into information that is not widely available. This provides a competitive advantage that helps find the alpha and drive higher returns than the market. 

Source Prolead brokers usa

5 Dominating IoT Trends Positively Impacting Telecom Sector in 2021

The Internet of Things (IoT) has matured to support the telecom industry by becoming an integral part of concepts like self-driving vehicles and industrial IoT. The Telecom industry, in fact, is amongst the biggest players in the IoT.

The use of IoT in telecom already impacts the communication between devices and humans. Users experience extraordinary user interfaces, as telecom is revolutionizing services, inventory, network segmentation, and operations. The telecom industry is nowhere stopping reliance on IoT.

The further expansion of IoT allows telecom to deliver revenue generation & customer retention solutions. By 2026, telecom is expecting a good $1.8 trillion revenue using IoT services.

The beginning was drastically witnessed in 2020. Further, major IoT trends, right from edge computing to 5G and expanding network footprints are all prepared to influence telecom in 2021, and ahead.

Global IoT Trends that Impacts Telecom Sector in 2021

Here are some global IoT trends that are going to influence the telecom sector this year, and even after.

1. IoT Data Streaming Amalgamates with ‘Big Data’

Operators in telecom have access to rich insights already. However, analyzing & managing data in action is challenging. Streaming analytics allows pre-processed data to be transmitted in real-time. (DataStream means incessant, regularly updated, and relevant flow of data in real-time).

• For example, critical IoT data, including hardware sensor information & IoT devices. Such enormous data require dealing with complex algorithms. Using the stream processing tools, it becomes easier to manage such colossal complicated data.

Data Stream Processing tools that can help:

• Apache tools (Kafka, Spark)
• Amazon tools (Amazon Kinesis, Kinesis Streams, Kinesis Firehose)

It is critical to integrate IoT data streams into Big Data Analytics. The reason? It is significant to pre-process numerous data flows in real-time. It helps to keep data streams up-to-date while accelerating data quality stored in the cloud. Faster data processing at the edge can also be expected.

Why is streaming analytics even needed?

Analytics uncover potential glitches/anomalies in behavior patterns assembled using IoT sensors. It minimizes the negative impact of the events that happened.

(Note: Anomalies in the telecom business are unusual data patterns in a complicated environment. If left unaddressed, it causes massive chaos).

The big data in the 5G IoT ecosystem (spawned either by Machine-to-Machine communication or Internet of Things devices) is only going to increase exponentially in the future. That calls for data stream processing in real-time to better automate the decision-making process.

Such deep data processing implemented at the edge then sent to the cloud:

• Improves quality of data
• Enhances safety & security immensely

Hence data streaming analytics is essential for the telecom industry to grow.

2. IoT Network (5G Empowered)

5G’s impact on the industry is still fresh. In 2021 and ahead, the 5G technology will remain in action. New categories of modems, gadgets, and modems are expected. That is going to be excellent for improving data transmission speed.

The cross-industry opportunities (generated with 5G) are even concentrating to mature smart concepts like smart city and Industry 4.0 or the Fourth Industrial Revolution (where both IoT and M2M communication plays a significant role to enhance communication via smart machines & devices that identifies issues in processes without the human intrusion).

The potential of that happening relies majorly on 5G rather than not-so-efficient traditional networks. Here’s what the telecom industry can expect:

• 100x Faster Network Speed – IoT enjoys speedier data transfer from IoT-connected devices and sensors with a maximum speed of 10 Gbps.
• Excellent Bandwidth – Telecom seamlessly manages sudden spikes besides network congestion using 5G Radio Access Network architecture. Hence, a more stable connection.
• Exclusive Capacity – Multiple connected devices communicate & transfer data in real-time. 5G enabled IoT network reaches its full potential delivering 1K times the higher capacity.
• Excessively Reduced Latency – In an IoT environment that is 5G enabled, telecom can expect just 5 milliseconds to transfer data. That’s what the industry requires to maintain devices, remote control in real-time, and effective M2M communication.

5G mass adoption is already observed by multiple MNOs like AT&T and Verizon. They rely on advanced 5G technologies (CAT-M1 & NB-IoT) to enable huge Internet of Things to connect with devices, which are cost-effective and less complex and support extensive battery life and minimum throughput.

Cellular IoT fragments that leverage 5G capabilities:

LTE-M or Long-Term Evolution for Machines, which is an LTE upgrade:

• Supports low complex CAT-M devices.
• Improved battery life

NB-IoT or Narrowband IoT is a Machine-To-Machine focused Radio Access Technology (RAT) that enables huge IoT rollouts. It ensures last-mile connectivity using its extensive-range communication at low power. Narrowband IoT radio modules are highly cost-effective, without needing additional gateways or routers. Hence, simplifying IoT arrangements.

Use Cases of NB-IoT:

1. Monitoring street lights, complete waste management, and remote parking. It initiates complete Smart City Management.
2. Seamlessly tracking all sorts of pollution, including water, land, and air for upkeeping the environment’s health.
3. Scrutinizing alarm systems, air conditioning, and complete ventilation system.

Many have already rolled out both NB-IoT and LTE-M, forecasting 52% of all cellular IoT connections by 2025. As per Enterprise IoT Insights, in the next four years, IoT networks (enabled by 5G) will massively increase by 1400%, which was 500 million last year will grow to 8 billion by 2024.

Telecom will go beyond where they currently are. What’s stopping IoT connectivity monetization is an excessive competition that deploys a pool of connectivity solutions at cheaper rates. Hence, there is a dire need of going beyond what’s existing in terms of monetization models.

3. Nurturing Telecom Infrastructure (and Software)

Telecom has to modify the existing infrastructure, to harmonize with advanced IoT devices. Without it, the industry might have to deal with compatibility concerns. Here’s what needs to be done:

• Upkeep Safety Hazards: Extreme weather, natural disaster, fire, or any other damage are bound to happen. The telecom equipment experiences the aftereffect of such events. What preventive measures are telecom considering to secure the equipment? That’s when IoT steps in. The much-needed compatibility between telecom infrastructure and IoT plays a significant role in saving equipment during such events. Another concern that’s prevailing in telecom is cybersecurity. Advanced technology like blockchain will certainly help combat this concern, improving the efficiency of IoT management platforms.

• Developing IoT Solutions Compatible with Existing Software: Telecom firms need to comprehend how existing software will match the demands of new IoT. New IoT technologies and devices are already in the market. The industry just needs to figure out how current software suits the advanced demands of IoT.

• Secure Resources & Equipment: The telecommunication industry relies on different resources, like fuel to keep up the constant supply of power. These resources might be the point of attraction for stealers. Telecom needs to secure its physical equipment and resources so that the chances of theft are minimal. The effective solution is to install IoT-smart cameras that ascertain the existence of any such external interference. Telecoms need to improve their existing security protocols to secure resources & equipment.

4. Artificial Internet of Things

Another major global trend of 2021 is Artificial IoT that’s all set to make networks and IoT systems more intellectual. How that’s going to be good for telecom is by using such cognitive systems for making decisions more context and experience-based. The entire reliance, however, is on the processes data from IoT-connected devices and sensors. The entire decision-making (related to network concerns & finding apt solutions) will be automated.

 AI is highly integrated into IoT via devices, chipsets, and edge computing systems. Even 5G network functions and concerning anomalies will be automated. In fact, numerous Data Analytics & Machine Learning empowered anomaly detection tools already prevail in the market.

How is telecom impacted when the Artificial Internet of Things integrates with predictive analytics?

• It generates prediction models along with real-time data analytics to identify errors or anomalies in the network. Further, it also enables complete remote monitoring & management of the assets.
• Remote access, theft exposure, and fraud examination can be experienced with solutions that have Artificial Intelligence amalgamated with IoT.  
• When Artificial Intelligence is deployed at the network edge, the data streams are not interrupted. Data is analyzed and stored, without disturbing data streams.

5. Data Drives the Extension

Humans are inclined towards smart gadgets and devices nationwide. Telecoms enjoy the ease of communicating with each other using IoT devices. From controlling gigantic machinery to scrutinizing big data, everything happens with utmost ease due to the availability of IoT devices.

Telecom has to be accredited for transforming us into IoT technology users. Also, IoT is no sooner reducing its impact on the telecom industry. Internet of Things, in fact, is ready to expand the telecom industry for many years from now.

Customers are hungry for improved and quality connectivity. They desire to stay connected to the world outside, and how good that experience will depend on how efficient telecom products & services are. That’s what will drive expansion in this industry – the quality of connectivity delivered.

 ‘Alexa’ is the best example to prove the convenience that customers want to use the technology and IoT connected devices integrated with AI. Another example could be ‘Amazon drones’ that seamlessly deliver at home.

The Verdict: Future of Telecom Positively Has Influence of IoT

The Internet of Things and the telecom industry truly complement each other. The emerging IoT technologies like Big data and 5G helps telecom access tap remarkable benefits, including an upgrade in technology and network.

The industry is centering on expanding expertise in technologies & IoT connectivity monetization. IoT is mature enough to backup telecom and transforms it into the biggest market nationwide.

Source Prolead brokers usa

Vue.js vs AngularJS Development in 2021: Side-by-Side Comparison

What is the first name that comes to your mind when we talk about web development? And why is it JavaScript?

Well, it is because JavaScript is the fastest growing language and is ruling the development industry, be it web or mobile apps. JavaScript has solutions for both front-end and back-end development.

Did you know there are almost 24 JS frameworks, but only a few could make it to the top? We know only 3-4 JS frameworks and are still confused about which one is better for our business. 

Here is a highly demanded comparison of Vue.js and AngularJS development, which will help you decide which is better for your business. 

Before we begin with the comparison, here are a few usage stats related to JavaScript, Vue.js, and AngularJS you might be interested in. 

Usage Stats of JavaScript, Vue.js and AngularJS.

• The following stats show the historical trends in the percentage of websites using JavaScript as their reliable development mode.

• The following graph shows the market position of JavaScript as compared to other popular languages and frameworks.

• The following stat will show you how Vue.js is ruling the technical world.

• The following stat will show you the usage of AngularJS in the current web development industry.

Let us now move towards the comparison between Vue.js and AngularJS.

Vue.js vs AngularJS Development in 2021

Here is a list of points that we will be focusing on during this comparison:

 

1. Vue.js vs AngularJS: Understanding the basics
2. Vue.js vs AngularJS: Based on market trends and popularity
3. Vue.js vs AngularJS: Performance
4. Vue.js vs AngularJS: Size and loading time
5. Vue.js vs AngularJS: Integration
6. Vue.js VS AngularJS: Which Is Best For Web Development In 2021?
7. What is your choice?

1. Vue.js vs AngularJS: Understanding the basics

AngularJS was released in 2010 as a basic version. It was developed by Google, known as the Typescript-based JavaScript Framework. After releasing several versions, Angular V11.2.8 is now the latest version available, released in April 2021.

 

In this age of technology, AngularJS quickly set the benchmark in mainstream technology used by millions of developers today. Because AngularJS not only has a great launch moment, it also offers incredible structure. It provides a simple bi-directional data link, an MVC model, and an integrated module system because of which much angularJS development company prefer it as their first choice for front-end JavaScript framework.

 

While talking about the Big 3 of JavaScript, Vue.js is the youngest member of the group. Former Google employee Evan You developed it in 2014, and it quickly took the world by storm with its excellent performance and smooth functionality.

 

Vue.js is a popular progressive framework for creating user interfaces. Unlike tech-driven frameworks like AngularJS, Vue.js has been built for incremental adoption by users. All thanks to the features of Vue.js that make it the most prominent JavaScript framework on GitHub.

2. Vue.js vs AngularJS: Market trends and popularity

• Usage and Market Share: According to the angular development report, AngularJS is used by 0.4% of all websites, while Vue.js is at 0.3%.

• Ranking of websites that use the respective JS libraries: 0.8% use AngularJS and 0.5% are Vue.js of all the websites whose JS library we know and are already among the 1,000,000.

• Historical Trend- The dedicated w3techs survey that represents historical trends in the percentage of websites using selected technologies.

3. Vue.js vs AngularJS: Based on Performance

Vue.js is a growing sensation among major web development companies when it comes to evaluating Js frameworks’ performance. You can always hire programmers from India to implement it in your business.

 

The structure of AngularJS is made of massive codes but guarantees high functionality, while Vue.js is flexible and very light and offers excellent speed over high functionality. While in most cases, the extensive features and functions of AngularJS are not used for the application, although the dedicated web development team chooses Vue.js over AngularJS.

 

While all functions are supposed to be added via additional extensions in Vue.js, ultimately, the choice is preferable among beginners than the AngularJS framework. However, the well-built structure of AngularJS makes it the perfect opportunity to develop an application that requires a rich set of functionalities.

 

At the same time, if you compare the functionalities, AngularJS may be rigid, but Vue.js has opinions. That means that Vue.js gives the developer freedom to develop an application in the framework’s manner.

4. Vue.js vs AngularJS: Size and loading time

When choosing between JavaScript frameworks, the size of the library is an essential element to consider, since in some cases, the execution time depends on the size of the file.

 

Angular: 500 + KB

Vue: 80 KB

 

Although AngularJS offers you a wide range of functions, it is certainly bulky. While the latest version of AngularJS has probably reduced the size of the application at a considerable rate, it is still not as lightweight as that developed with the Vue.js framework. When the application’s loading time depends mainly on the size of the application, the Vue.js mobile application guarantees a faster loading than AngularJS.

5. Vue.js vs AngularJS: Integration

Although the general implementation of AngularJS is quite complicated, even for dedicated developers. But if you are using Angular CLI, you win half the battle. While the CLI handles everything from creating projects to optimizing code with a single command, you can deploy an application to any static host with a single command.

 

Talking about Vue.js, it also has CLI, which generates a robust pre-configured configuration for hassle-free application development. Developing in Vue.js is as easy as with AngularJS. You get optimized projects that run on a built-in command. Therefore, deploying the Vue.js project on any static host and enabling server-side rendering is relatively easy.

6. Vue.js VS AngularJS: Which one is better for web development in 2021?

CLI supports both the frameworks, but as compared to Vue.js, AngularJS has a small advantage when managing and deploying an application.

A. When to choose AngularJS for your application development project?

 

AngularJS is the ideal choice when:

• You need to develop a complex, large and dynamic application project.
• You want a real-time application like instant messaging and chat application.
• You need reliable and easy scalability.
• You can afford to spend time learning TypeScript to develop your application.

B. When should I choose Vue.js?

 

Despite being the youngest member of JavaScript’s Big 3, it is a popular choice for many software development companies. You should choose it when:

• You need to develop a lightweight, single-page application.
• You need high speed, flexibility, and performance.
• You want a small-scale application.
• Look for clear and simple coding.

 

Which one should you choose?

When it comes to developing an application, both frameworks will offer a great structure to your web applications. However, Vue.js VS AngularJS: which is better? The answer to this question completely depends on your business needs. 

 

The choice of the appropriate framework depends on several factors. As discussed, you may consider making the final decision. If you want a structure that is industry-appropriate and well structured, hire developers in India to provide you with AngularJS development services is the way to go.

On the other hand, if your project requires a single page layout, fast rendering, clean coding, there could be no better option than to consider vue.js development company in India for your project.

Source Prolead brokers usa

Building Effective Site Taxonomies

Several years ago, the typical company website fit into a predefined template – a home or landing page (usually talking about how innovative the company was), a products page, a business client testimonials page, a blog, and an “about us” page. However, as the number of products or services have multiplied, and as the demands for supporting those services have followed suit, your readers had to spend more time and energy finding appropriate content – and web managers had to focus more on keeping things organized than they likely wanted to do.

There are typically two different, though complementary, approaches to follow in locating resources on sites – search and semantics. Search usually involves indexing keywords, either directly, or through some third-party search engine. Search can be useful if you know the right terms, but once you get beyond a few dozen pages/articles/blog posts, search can also narrow down content too much, or fail to provide links to related content if that content doesn’t in fact have that exact search term.

Semantics, on the other hand, can be roughly thought of as a classification scheme, usually employing some kind of organizational taxonomy. The very simplest taxonomy is just a list of concepts, which usually works well for very basic sites. These usually have a fairly clear association with a single high-level menu. For instance, the example given above can be thought of as a simple (zeroeth-level) taxonomy, consisting of the following:

Root
Home
Books
Blogs
About Us 

The Root node can be thought of as an invisible parent that holds each of the child terms. Each of these could in fact point to page URLs (which is how many menus are implemented), but those pages in turn may in fact be generated. The Home page generally is some kind of (semi-static) page. It has a URL that probably looks something like this:

https://mysite.com/home

For brevity’s sake, we can remove the domain name and treat the URL as starting with the first slash after that:

/home

Your CMS also likely has a specialized redirect function that will assign this particular URL to the root. That is to say:

Notice as well the fact that “home” here is lower case. In most CMS systems, the menu item/concept is represented by what’s called a slug that can be thought of as a URL friendly ID. The slugs are typically rendered as a combination of a domain or categorical name (i.e.,mybooks) and a local name (home), separated by a “safe” delimiter such as a dash, an underscore or a colon (for instance, mybooks_). Thus, there’s a data record that looks something like:

MenuItem:
 label: Home
id: mybooks_home
description: This is the home or landing page for the site.
parent: mybooks_

This becomes especially important when you have spaces in a label, such as “About Us”, which would be converted to something like mybooks_about-us as its identifier. The combination of the prefix and the local-name together is called a qualified name and is a way of differentiating when you have overlapping terms from two different domains (among other things) which local-name term is in focus.

When you get into products, you again may have a single page that describes all of your products, but maintaining such product pages by hand can be a lot of work with comparatively little gain, and also has the very real possibility in this world of collaboration that you and another colleague may end up updating that page at the same time and end up overwriting one another.

One way around this is through the use of categories or tags. This is where your taxonomy begins to pay dividends, and it comes about through the process of tagging. Think of each product that you have to offer as an individual post or entry. As an example, let’s say that your company sells books. Your content author can create a specific entry for a book (“My Book of Big Ideas!”) that contains important information such as title, author(s), a summary, price and other things, and it’s likely that the book will end up with a URL something like

/article/my-book-of-big-ideas

You could, of course, create a page with links to each of these books . . . or you could add a category tag called Book to the book entry. The details for doing so change from CMS to CMS, but in WordPress, you’d likely use the built-in Category widget. Once assigned, you can specify a URL that will give you a listing (usually based on temporal ordering from the most recent back), with the URL looking something like:

/category/books

This breaks one large page into a bunch of smaller ones tied together by the Product category in the taxonomy. Once you move beyond a certain number of products, though, it may at that point make sense to further break the taxonomy down. For instance, let’s say that your company produces books in a specific genre, such as Contemporary Paranormal, Historical Paranormal, Fantasy, and Steampunk. You can extend the taxonomy to cover these.

Root
Home
Books
Contemporary Paranormal
Historical Paranormal
Sword and Sorcery
Steampunk
Blogs
About Us

This multilayer structure tends to be typical of first-level drop-down menus. Again, keep in mind that what is being identified here is not books so much as book categorizations. This kind of structure can even be taken down one more level (all of the above could be identified as being in the Fantasy genre), but you have to be careful about going much deeper than that with menus.

Similar structures can also be set up as outlines (especially when the taxonomy in question is likely to be extensive) that make it possible to show or hide unused portions of that taxonomy. This can work reasonably well up to around fifty or sixty entries, but at some point beyond that it can be difficult to find given terms and the amount of searching becomes onerous (making the user more hesitant in wanting to navigate in this manner).

There are things that you can do to keep such taxonomies useful without letting them become unwieldy. First, make a distinction between categories (or classes) and objects (or instances). Any given leaf node should, when selected, display a list of things. For instance, selecting Contemporary Paranormal should cause the primary display (or feed, as it’s usually known) to display a list of books in that particular genre. Going up to the Books category would then display all books in the catalog but in general only 20-25 per page.

It should be possible, furthermore, to change the ordering on how that page of books (or contemporary paranormal romance books if you’re in a subgenre) gets displayed – whether by relevance, by most recent content or alphabetically (among other sorting methods).

Additionally, there is nothing that says that a given book need be in only one category. In this case, the taxonomy does not necessarily have to be hierarchical in nature, but instead gives classes with possible states:

Fictitiousness
Fiction
Historical Fiction
Non-Fiction 
Medium
Hard Cover
Paperback
Electronic Book
Audio Book
Genre
Biography
Analysis
Fantasy
Horror
Historical
Mystery
Paranormal
Romance
Space Opera
Science Fiction

This approach actually works very well when you give a particular resource three or four different kinds of terms that can be used for classification. This way, for instance, I can talk about wanting Fiction-based electronic books that involve both paranormal elements, romance, and mystery. Moreover, you can always combine faceted search and textual search together, using the first to reduce the overall query return set, and the second to then retrieve from that set those things that also have some kind of textual relationship. This approach generally works best when you have several thousand items (instances) and perhaps a few dozen to a few hundred classes in your classification system.

Faceting in effect decomposes the number of classes that you need to maintain in the taxonomy. You can also create clustering by trying to find the minimal set of attributes that make up a given class through constraints. For instance, the list of potential genres could be huge, but you could break these down into composites — does the character employ magic or not, does the story feature fantastic or mythical beings, is the setting in the past, present, or future, does the storyline involve the solving of a crime, and so forth. Each of these defines an attribute. The presence of a specific combination of attributes can then be seen as defining a particular class.

Faceting in this manner pushes the boundary between taxonomies and formal semantics, in that you are moving from curated systems to heuristic systems where classification is made by the satisfaction of a known set of rules. This approach lays at the heart of machine-based classification systems. Using formal semantics and knowledge graphs, as data comes in records (representing objects) can be tested against facets. If an object satisfies a given test, then it is classified to the concept that the test represents.

In this particular case, there are four sets of attributes. Three of them are the same for paranormal mystery vs. paranormal romance, while the fourth (whether a criminal mystery or a romance dominates the story) differentiates the two. The Modern Paranormal story, on the other hand, has just the three primary attributes without the mystery/romance attribute, and as such it is a super-class of the other two paranormal types, which is true in general: if two classes share specific attributes, there is a super-class that both classes are descended from.

Interestingly enough, there’s another corollary to this: in an attribute modeling approach, it is possible for three classes to share different sets of attributes, meaning that while any two of those classes may share a common ancestor, the other two classes may have a different common ancestor that doesn’t overlap the inheritance path.

At the upper end of such taxonomy systems are auto-classification systems that work by attempting to identify common features in a given corpus through machine learning then using input provided by the user (a history of the books that they’ve read, for instance) to make recommendations. This approach may actually still depend upon taxonomists to determine the features that go into making up the taxonomy (or, more formally, ontology), though a class of machine learning algorithms (primarily unsupervised learning) can work reasonably well if explainability is not a major criterion.

Source Prolead brokers usa

Simple Machine Learning Approach to Testing for Independence

We describe here a methodology that applies to any statistical test, and illustrated in the context of assessing independence between successive observations in a data set. After reviewing a few standard approaches, we discuss our methodology, its benefits, and drawbacks. The data used here for illustration purposes, has known theoretical auto-correlations. Thus it can be used to benchmark various statistical tests. Our methodology also applies to data with high volatility, in particular, to time series models with undefined autocorrelations. Such models (see for instance Figure 1 in this article) are usually ignored by practitioners, despite their interesting properties.

Independence is a stronger concept than all autocorrelations being equal to zero. In particular, some functional non-linear relationships between successive data points may result in zero autocorrelation even though the observations exhibit strong auto-dependencies: a classic example is points randomly located on a circle centered at the origin; the correlation between the X and Y variables may be zero, but of course X and Y are not independent.

1. Testing for independence: classic methods

The most well known test is the Chi-Square test, see here. It is used to test independence in contingency tables or between two time series. In the latter case, it requires binning the data, and works only if each bin has enough observations, usually more than 5. Its exact statistic under the assumption of independence has a known distribution: Chi-Squared, itself well approximated by a normal distribution for moderately sized data sets, see here. 

Another test is based on the Kolmogorov-Smirnov statistics. It is typically used to measure goodness of fit, but can be adapted to assess independence between two variables (or columns, in a data set). See here. Convergence to the exact distribution is slow. Our test described in section 2 is somewhat similar, but we entirely data-driven, model free: our confidence intervals are based on re-sampling techniques, not on tabulated values of known statistical distributions. Our test was first discussed in section 2.3 of a previous article entitled New Tests of Randomness and Independence for Sequences of Observations, and available here. In section 2 of this article, a better and simplified version is presented, suitable for big data. In addition, we discuss how to build confidence intervals, in a simple way that will appeal to machine learning professionals.

Finally, rather than testing for independence in successive observations (say, a time series) one can look at the square of the observed autocorrelations of lag-1, lag-2 and so on, up to lag-k (say k = 10). The absence of autocorrelations does not imply independence, but this test is easier to perform than a full independence test. The Ljung-Box and the Box-Pierce tests are the most popular ones used in this context, with Ljung-Box converging faster to the limiting (asymptotic) Chi-Squared distribution of the test statistic, as the sample size increases. See here.

2. Our Test

The data consists of a time series x1, x2, …, xn. We want to test whether successive observations are independent or not, that is, whether x1, x2, …, xn-1 and x2, x3, …, xn are independent or not. It can be generalized to a broader test of independence (see section 2.3 here) or to bivariate observations: x1, x2, …, xn versus y1, y2, …, yn. For the sake of simplicity, we assume that the observations are in [0, 1].

2.1. Step #1

The first step to perform the test, consists in computing the following statistics:

for N vectors (α, β)‘s, where α, β are randomly sampled or equally spaced values in [0, 1], and χ is the indicator function: χ(A) = 1 if A is true, otherwise χ(A) = 0. The idea behind the test is intuitive: if q(α, β) is statistically different from zero for one or more of the randomly chosen (α, β)’s, then successive observations can not possibly be independent, in other words, xk and xk+1 are not independent, much less correlated.   

In practice, I chose N = 100 vectors (α, β) evenly distributed on the unit square [0, 1] x [0, 1], assuming that the xk‘s take values in [0, 1] and that  n is much larger than N, say n = 25 N. 

2.2. Step #2

Two natural statistics for the test are

The first one S, once standardized, should asymptotically have a Kolmogorov-Smirnov distribution. The second one T, once standardized, should asymptotically have a normal distribution, despite the fact that the various q(α, β)’s are never independent. However, we do not care about the theoretical (asymptotic) distribution, thus moving away from the classic statistical approach. We use a methodology that is typical of machine learning, and described in section 2.3.

Nevertheless, the principle is the same in both cases: the higher the value of S or T computed on the data set, the most likely we must reject the assumption of independence. Among the two statistics, T has less volatility than S, and may be preferred. But S is better at detecting very small departures from independence.

2.3. Step #3

The technique described here is very generic, intuitive, and simple. It applies to any statistical test of hypotheses, not just for testing independence. It is somewhat similar to cross-validation. It consists or reshuffling the observations in various ways (see the resampling entry in Wikipedia to see how it actually works) and compute S (or T) for each of the 10 different reshuffled time series. After reshuffling, one would assume that any serial, pairwise independence has been lost, and thus you get an idea of the distribution of S (or T) in case of independence. Now compute S on the original time series. Is it higher than the 10 values you computed on the reshuffled time series? If yes, you have a 90% chance that the original time series exhibits serial, pairwise dependency. 

A better but more complicated method consists of computing the empirical distribution of the xk‘s, then generate 10 n independent deviates with that distribution. This constitutes 10 time series, each with n independent observations. Compute S for each of these time series, and compare with the value of S computed on the original time series. If the value computed on the original time series is higher,  then you have a 90% chance that the original time series exhibits serial, pairwise dependency. This is the preferred method if the original time series has strong, long-range autocorrelations.

2.4. Test data set and results

I tested the methodology on an artificial data set (a discrete dynamical system) created as follows: x1 = log(2) and xn+1 = b xn – INT(b xn). Here b is an integer larger than 1, and INT is the integer part function. The data generated behaves like any real time series, and has the following properties.

• The theoretical distribution of the xk‘s is uniform on [0, 1]
• The lag-k autocorrelation is known and equal to 1 / b^k (b at power k)

It is thus easy to test for independence and to benchmark various statistical tests: the larger b, the closer we are to serial, pairwise independence. With a pseudo-random number generator, one can generate a time series consisting of independently and identically distributed deviates, with a uniform distribution on [0, 1], to check the distribution of S (or T) and its expectation, in case of true independence, and compare it with values of S (or T) computed on the artificial data, using various values of b. In this test with N = 100 n = 2500, b = 4, (corresponding to an autocorrelation of 0.25) the value of S is 6 times larger than the one obtained for full independence. For b = 8, (corresponding to an autocorrelation of 0.125), S is 3 times larger than the one obtained for full independence. This validates the test described here at least on this kind of dataset, as it correctly detects lack of independence by yielding abnormally high values of T when the independence assumption is violated.

To receive a weekly digest of our new articles, subscribe to our newsletter, here.

About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). You can access Vincent’s articles and books, here.

Source Prolead brokers usa

Leveraging SAP’s Enterprise Data Management tools to enable ML/AI success

Background

In our previous blog post, “Master Your ML/AI Success with Enterprise Data Management”, we outlined the need for Enterprise Data Management (EDM) and ML/AI initiatives to work together in order to deliver the full business value and expectations of ML/AI. We made a set of high-level recommendations to increase EDM maturity and in turn enable higher value from ML/AI initiatives. A graphical summary of these recommendations is shown below.

Figure 1 – High level recommendations to address EDM challenges for ML/AI initiatives

In this post, we will present a specific instantiation of technology for bringing those concepts to life. There are countless examples that could be shown, but for the purposes of this post, we will present a solution within the SAP toolset. The end result is an implementation environment where the EDM technologies work hand-in-hand with ML/AI tools to help automate and streamline both these processes.

SAP’s preferred platform for ML/AI is SAP Data Intelligence (DI).  When it comes to EDM, SAP has a vast suite of tools that store, transfer, process, harness, and visualize data. We will focus on four tools that we believe provide the most significant impact to master ML/AI initiatives implemented on DI. These are SAP Master Data Governance (MDG), SAP Data Intelligence (DI) – Metadata Explorer component, and to a smaller extent, SAP Information Steward (IS). SAP Data Warehouse Cloud (DWC) can also be used to bring all the mastered and cleansed data together and to store and visualize the ML outputs.

Architecture

As with any other enterprise data solution, the challenge is to effectively integrate a set of tools to deliver the needed value, without adding the cost overhead of data being moved and stored in multiple places, as well as the added infrastructure, usage and support costs. For enterprises that run on SAP systems, a high-level architecture and descriptions of the tools that would achieve these benefits is shown below.

Figure 2 –High-level MDG/DI architecture and data flow

1. SAP MDG (Master Data Governance) with MDI (Master Data Integration)

SAP MDG and MDI go hand in hand. MDI is provided with the SAP Cloud Platform. It enables communication across various SAP applications by establishing One Domain Model (ODM). It enables a consistent view of master data across the end-to-end scenarios.

SAP MDG is available as S/4 HANA or ERP-based. This tool helps ensure high quality and trusted master data for initial and ongoing purposes. It can become a key part of the enterprise MDM and data governance program. Both active and passive governance are supported. Based on business needs, certain domains are prioritized out of the box in MDG.  MDG provides the capabilities like Consolidation, Mass Processing and Central Governance coupled with governance workflows for Create-Read-Update-Delete (CRUD) processes.

SAP has recently announced SAP MDG, cloud edition. While it is not a replacement for MDG on S/4 HANA, MDG cloud edition is planned to come with core MDG capabilities like Consolidation, Centralization and Data Quality Management to centrally manage core attributes of Business Partner data. This is a useful “very quick start” option for customers who never used MDG, but it can also help customers already using MDG on S/4HANA to build out their landscape to a federated MDG approach for better balancing centralized and decentralized master data.

2. Data Intelligence (with Metadata Explorer component)

SAP IS and MDG are the pathways to make enriched, trusted data available to Data Intelligence, which is used to actually build the ML/AI models. We can reuse SAP IS rules and metadata terms directly in SAP DI. This is achieved in DI by utilizing its data integration, orchestration, and streaming capabilities. DI’s Metadata Explorer component also facilitates the flow of business rules, metadata, glossaries, catalogs, and definitions to tools like IS (on-prem) for ensuring consistency and governance of data. Metadata explorer is geared towards discovery, movement and preparation of data assets that are spread across diverse and disparate enterprise systems including cloud-based ones.

3. Information Steward (IS) – Information Steward is an optional tool, useful for profiling data, especially for on-prem situations. The data quality effort can be initiated by creating the required Data Quality business rules, followed by profiling the data and running Information Steward to assess data quality. This would be the first step towards initial data cleansing, and thereby data remediation, using a passive governance approach via quality dashboards and reports. (Many of these features are also available in MDG and DI). SAP IS helps an enterprise address general data quality issues, prior to using specialized tools like SAP MDG to address master data issues. It can be an optional part of any ongoing data quality improvement initiative for an enterprise.

4. Data Warehouse Cloud (DWC) – Data Warehouse Cloud is used in this architecture to bring all the mastered and cleansed data together into the cloud, perform any other data preparation or transformations needed, and to model the data into the format needed by the ML models in DI. DWC is also used to store the results of the ML models, and to create visualizations of these results for data consumers.

Figure 3 – Summary of Functionality of SAP tools used for EDM

While there are some overlaps in functionality between these tools, Data Intelligence is more focused on the automation aspects of these capabilities. DI is primarily intended as an ML platform, and therefore has functionality such as the ability to create data models and organize the data in a format that facilitates the ML/AI process (ML Data Manager). This architecture allows for capitalizing on the EDM strengths of MDG and IS. This is also consistent with the strategic direction of SAP, that is, providing comprehensive “Business Transformation as a Service” approach, leading with cloud services. Together, these tools work in a complementary way (for hybrid on-prem plus cloud scenarios), and the combination of these tools work hand in hand to make trusted data available to AI/ML.

Conclusion

In summary, the SAP ecosystem has several EDM tools that can help address the data quality and data prep challenges of the ML/AI process. SAP tools like MDG and DI Metadata Explorer component have features and integration capabilities that can easily be leveraged during or even before the ML/AI use cases are underway. If used in conjunction with the general EDM maturity recommendations summarized above, these tools will help to deliver the full business value and expectations of ML/AI use cases.

In our next post, we will continue our discussion on EDM tools, some of their newer features, how they have evolved, and how ML/AI has been part of their own evolution. As a reminder, if you missed the first post in this series, you can find it here: “Master Your ML/AI Success with Enterprise Data Management”.

Inspired Intellect is an end-to-end service provider of data management, analytics and application development. We engage through a portfolio of offerings ranging from strategic advisory and design, to development and deployment, through to sustained operations and managed services. Learn how Inspired Intellect’s EDM and ML/AI strategy and solutions can help bring greater value to your analytics initiatives by contacting us at [email protected].

LinkedIn https://www.linkedin.com/company/inspired-intellect/

 

Editor’s Note – I co-authored this blog with my colleague, Pravin Bhute, who serves as an MDM Architect for our partner organization, WorldLink.

Source Prolead brokers usa