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

6 essential steps of healthcare mobile app development

So, you’ve done your research and selected the market niche and the type of your mHealth app. Now it’s time for planning and estimating the project scope, budget, and main features of your product. Healthcare mobile app development can be daunting and time-consuming unless you’re well prepared.

Follow these steps to make sure you don’t miss out on anything important.

Understand your audience

Target audience analysis is a crucial part of your product discovery phase. The target audience represents more than just users of your app. It’s a huge market segmented by various factors, including country, gender, age, education, income level, lifestyle, etc.

There’s no way to build a successful medical app without understanding users’ needs. Each region has its specifics and regulations, so start by choosing the targeted countries for your platform. In 2020, the global mobile health market revenue share in North America was 38%. Europe and the Asia-Pacific took other parts of the pie with shares of over 25%.

Your audience research will give you a clue on necessary features and general expectations from a mHealth app.

Outline core features for MVP

Unfortunately, you cannot have all the cool features at once — otherwise, the development time and cost will be outrageous. That’s why you should separate must-have from nice-to-have features for your MVP. If you’re stuck on prioritizing the features, the business analysis will help you better understand your product requirements and business needs.

The key features of your medical app for doctors will depend on the chosen application type. Here is a brief list of mHealth apps functionality:

• Patient data monitoring
• Secure doctor-patient communication
• File exchange
• Appointment scheduling
• Integration with EHR
• Integration with payment systems
• Integration with Google Kit and Health Kit
• AI health assistant
• Progress tracking and analytics
• Cloud data storage
• Cross-platform accessibility
• Notifications Go for essential features that reflect your app’s concept in the first place. You can always add more functionality after the MVP is released.

Take care of UX/UI design

While the fancy design is not a must for a medical application, usability could be the turning point for your app’s success. Paying attention to UX and UI design ensures smooth interaction between the users and your brand.

Follow these rules to make your healthcare app user-friendly:

• Optimize user journey. Make all actions as easy as possible.
• Choose an ergonomic information architecture. Highlight core features with UI design.
• Make sure your design is responsive. Adapt the app’s interface to various platforms and screen sizes.
• Empathize with your users. Find out what your audience needs and give it to them.
• Test your design. Validate your ideas through usability testing and user feedback to upscale your app. You can choose a more conservative or modern design depending on your target audience’s preferences. Planning all details and visualizing them with design prototypes will save you costs and shorten time to market.

Pick a dedicated development team

The qualifications and experience of the chosen development team make a big difference in the prosperity of your product. Hiring in-house developers increase the project cost and require additional time while working with freelancers doesn’t guarantee the expected result.

The best option is to choose a close-knit team with experience in healthcare mobile app development. Entirely focused on your project goals, it takes care of the whole development process from hiring to project management. A dedicated team accelerates the product development lifecycle and provides smooth and effective communication to achieve the best possible result.

Consider security compliance

Since healthcare applications handle a lot of personal information, it’s vital to keep patient data safe by complying with legal and privacy regulations. The following list includes regulations needed for medical apps within the US market:

HIPAA. Adherence to HIPAA is mandatory for all apps that process and store Protected Healthcare Information (PHI) such as CT and MRI scans, lab results, and doctor’s notes.
CCPA. This law informs patients about the collected data, provides reports, and removes the data at the request.
NIST. It’s a cybersecurity framework that offers a wide range of tools and services explicitly for mHealth applications.
In other words, there is no way of launching a medical app without following cybersecurity standards.

Choose app monetization model

Whether you’re building a medical app or any other app, you can choose from the same list of monetization strategies. Your options include:

Freemium. The idea is to give access to basic features while offering advanced functionality for a premium account.
Certified content. This strategy involves providing free access to a limited amount of content. After users reach the limit, they need to sign up and pay for a subscription.
Relevant advertising. You can bet on targeted mobile ads that use GPS or beacon-based localization.
Subscription model. Offer different subscription plans for doctors or patients.
Whichever monetization strategy you choose, make sure it’s not annoying or disruptive for user performance.

Discover more.

Source Prolead brokers usa

Using Amazon S3 for Object Storage

Image by Mohamed Hassan from Pixabay

Introduction

It is the 21st century and we are literally obsessed with data. Data seems to be everywhere and companies currently hold huge amounts of it regardless of the industry they belong to. 

This brings us to the problem of storing data in a way that it can be accessed, processed, and used efficiently. Before cloud solutions, companies would have to spend a lot of money on physical storage and infrastructure to support all the data the company had. 

Nowadays, the more popular choice is to store data in a cloud as this is often the cheaper and more accessible solution. One of the most popular storage options on the market is Amazon S3 and in this article, you are going to learn how to get started with it using Python (and boto3 library).

So let’s first see how AWS S3 works.

AWS S3 Object Storage

As we have mentioned before AWS S3 is a cloud storage service and its name S3 actually stands for Simple Storage Service. Additionally, AWS S3 is unstructured object storage. What does that mean? 

This means that data is stored as objects without any explicit structure such as files or subdirectories. The object is stored directly in the bucket with its metadata. 

This approach has a lot of advantages. The flat structure allows fast data retrieval and offers high scalability (if more data needs to be added it is easy to add new nodes). The metadata information helps with searchability and allows faster analysis. 

These characteristics make object storage an attractive solution for bigger and smaller companies. Additionally, it may be the most cost-effective option of storing data nowadays.

So, now that you have a bit of background on AWS S3 and object storage solutions, you can get started and create an AWS account.

Creating an Account with AWS and Initiating Setup

In order to create an account, head to AWS and click on create an AWS account. You will be prompted to fill in a form similar to the one below.

There are currently five steps in the set up so you will need to fill all of them (including your address, personal, and billing information). When asked for the plan you can choose a free one. After completing the signup process, you should see a success message and be able to access the AWS Management Console.

Once you are in the AWS Management Console you will be able to access S3 from there.

Now you can open S3 and create your first bucket. The name that you choose for the bucket has to be unique across the entire S3 platform. I suggest you use only lowercase letters for the name (e.g. konkibucket). For the AWS region, choose the location closest to you and leave the rest of the settings as default.

Once the bucket is created you should be able to see it from S3. 

Now you can add and delete files by accessing the bucket via the AWS S3 console. 

The next step is to learn how to interact with the bucket via Python and the boto3 library. 

Installing boto3

The first step is to use pip to install boto3. In order to do this you can run the following command:

pip3 install boto3

Setup Credentials File with AWS Keys

The next step is to set up a file with AWS credentials that boto will use to connect to your S3 storage. In order to do it, create a file called ~/.aws/credentials and set up its contents with your own access keys.

[default]
aws_access_key_id=AKIAIOSFODNN7EXAMPLE
aws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

In order to create keys, you will need to head to the AWS Management Console again and select IAM under Security, Identity & Compliance.

Once in IAM, select the users option as shown in the screenshot below:

This will lead you to the user management platform where you will be able to add a new user. Make sure you grant a new user programmatic access.

Also, make sure that the user has AmazonS3FullAccess permission.

Once the user creation process is successful you will see the access keys for the user you have created.

You can now use your credentials to fill in ~/.aws/credentials file.

Once the credentials file is set up you can get access to S3 via this Python code:

import boto3
s3_resource = boto3.resource('s3')

If you want to see all the buckets in your S3 you can use the following snippet:

for bucket in s3_resource.buckets.all():
   print(bucket.name)

Uploading a File to S3

It’s time to learn how to upload a file to S3 with boto3.

In order to send a file to S3, you need to create an object where you need to specify the S3 bucket, object name (e.g. my_file.txt) and a path from your local machine from which the file will be uploaded:

s3_resource.Object('<bucket_name>', '<my_file.txt>').upload_file(Filename='<local_path_file>')

Yes, it’s that simple! If you now look at the S3 Management Console the bucket should have a new file there.

Downloading a File from S3

Like uploading a file, you can download one once it actually exists in your S3 bucket. In order to do it, you can use the download_file function and specify the bucket name, file name and your local path destination.

s3_resource.Object('<bucket_name>', '<my_file.txt>').download_file('<local_path_file>')

You have just learned how to upload and download files to S3 with Python. As you can see the process is very straightforward and easy to set up.

Summary

In this article, you have learned about AWS S3 and how to get started with the service. You also learned how to interact with your storage via Python using the boto3 library. 

By now you should be able to upload and download files with Python code. There is still much more to learn but you just got started with AWS S3!

Source Prolead brokers usa

Data Analytics: How it Drives Better Decision-Making

Source Prolead brokers usa

Data Center Infrastructure Market is Projected to Reach USD 100 Billion by 2027

According to a recent study from market research firm Global Market Insights, The need for data center infrastructure market management among organizations to offer higher energy-efficiencies will be positively driven by the influx of cloud computing, Big Data, and AI solutions. The surge in internet infrastructure activities has led to the generation of large quantities of data by individuals and connected devices.

The rising levels of data traffic have placed an immense power burden on data centers on account of the significant jump in the usage of IoT devices. This has in turn pushed data center operators to increasingly adopt efficient and cost-effective data center infrastructure solutions.
As per a report by Global Market Insights, Inc., the global data center infrastructure market could reach USD 100 billion in annual revenue by 2027.

Owing to the adoption of data analytics, cloud computing, and emerging technologies such as AI, machine learning, and IoT, hyper-scale data centers have seen huge demand lately. Big tech giants like Facebook, Amazon, and Google are investing heavily in the construction of hyper-scale data center facilities.

These data center infrastructures need high capability and modernized infrastructure for supporting critical IT equipment and offer enhanced data protection. High-density networking servers in these data centers demand security management, power and cooling combinations for enabling energy-efficient operation.

Increasing government initiatives regarding the safety of customer data are encouraging businesses to establish their own data center facilities in the Asia Pacific. For instance, China’s Cybersecurity Law states data localization requirements on Critical Information Infrastructure Operators (CIIOs). The Law guides network operators to analyze, store and process customer data within the country. With this, it is estimated that the Asia Pacific data center infrastructure market may speculate sturdy progress over the forecast period. Multiple initiatives such as Smart Cities, Made in China, and Digital India, may also boost the adoption of IoT and cloud computing in the region.

Mentioned below are some of the key trends driving data center infrastructure market expansion:

1) Growing demand for hyperscale data centers

Expansion of hyperscale data centers owing to the usage of cloud computing, data analytics, and emerging technologies like IoT, AI, and machine learning is fueling industry outlook. Hyperscale data centers need high capability and modernized infrastructure to improve protection and support the critical IT equipment.

High-density networking servers in hyperscale data centers demand cooling, security management and power solutions in order to facilitate energy-efficient operation. Major cloud service providers like Facebook Inc., Amazon, and Google LLC are making huge investments in the construction of hyperscale data center facilities.

2) Increasing adoption of data center services

The service segment is anticipated to account for a substantial market share on account of surging demand for scalable infrastructure for supporting high-end applications. Data center services such as monitoring, maintenance, consulting, and design help operators to better manage data centers and their equipment.

Enterprises often need professional, skilled and managed service providers for the management of systems and optimization of data center infrastructure to obtain efficiencies. Professional service providers having the required technical knowledge and expertise in IT management and data center operations allow streamlining of business processes. These services help to significantly decrease the total cost of operations and maintenance of IT equipment.

3) Robust usage of cooling solutions

There is a proliferation of AI, driverless cars, and robots which is encouraging data center service providers to move strategic IT assets nearer to the network edge. These edge data centers are in turn rapidly shifting towards liquid cooling solutions to run real applications having full-featured hardware and lessen energy consumption for the high-density applications.

Key companies operating in the data center infrastructure market are Panduit Corporation, Hewlett Packard Enterprise Company, Black Box Corporation, Vertiv Group Co., ClimateWorx International, Eaton Corporation, Huawei Technologies Co., Ltd., Cisco Systems, Inc., ABB Ltd, Schneider Electric SE, Degree Controls, Inc., and Dell, Inc.

Source: https://www.gminsights.com/pressrelease/data-center-infrastructure-…

Source Prolead brokers usa

NetSuite ERP ushering a digital era for SMEs

Source Prolead brokers usa