Six Secret SPARQL Ninja Tricks

SPARQL is a powerful language for working with RDF triples. However, SPARQL can also be difficult to work with, so much so that it often is not utilized anywhere near as often for its advanced capabilities, which include aggregating content, building URIs, and similar uses. This is the second piece in my exploration of OntoText’s GraphDB database, but many of these techniques can be applied with other triple stores as well.

Tip 1. OPTIONAL and Coalesce()

These two keywords tend to be used together because they take both advantage of the Null value. In the simplest case, you can take advantage of coalesce to provide you with a default value. For instance, suppose that you have an article that may have an associated primary image, which is an image that is frequently used for generating thumbnails for social media. If the property exists, use it to retrieve the URL, but if it doesn’t, use a default image URL instead.

# Turtle

 

article:_MyFirstArticle

       a class:_Article;

       article:hasTitle “My Article With Image”^^xsd:string;

       article:hasPrimaryImage “path/to/primaryImage.jpg”^^xsd:anyURI;

       .

 article:_MyFirstArticle

       a class:_Article;

       article:hasTitle “My Article Without Image”^^xsd:string;

       .

 

With SPARQL, the OPTIONAL statement will evaluate a triple expression, but if no value is found to match that query then rather than eliminating the triple from the result set, SPARQL will set any unmatched variables to the value null. The coalesce statement can then query the variable, and if the value returned is null, will offer a replacement:

#SPARQL

 

select ?articleTitle ?articleImageURL where {

    ?article a class:_Article.

    ?article article:hasTitle ?title.

    optional {

         ?article article:hasPrimaryImage ?imageURL.

         }

    bind(coalesce(?imageURL,”path/to/defaultImage.jpg”^^xs:anyURI) as ?articleImageURL)

    }

This in turn will generate a tuple that looks something like the following:

articleTitle	articleImageURL
My Article With Image	path/to/primaryImage.jpg
My Article Without Image	path/to/defaultImage.jpg

Coalesce takes an unlimited sequence of items and returns the first item that does not return a null value. As such you can use it to create a chain of precedence, with the most desired property appearing first, the second most desired after that and so forth, all the way to a (possible) default value at the end.

You can also use this to create a (somewhat kludgy) sweep of all items out a fixed number of steps:

# SPARQL

 

select ?s0 ?s1 ?s2 ?s3 ?s4 ?o ?hops where {

values ?s1 {my:_StartingPoint}

bind(0 as ?hops0)

?s1 ?p1 ?s2.

filter(!bound(?p1))

bind(1 as ?hops1)

optional {

    ?s2 ?p2 ?s3.

    filter(!bound(?p2))

    bind(2 as ?hops2)

    optional {

        ?s3 ?p3 ?s4.

        filter(!bound(?p3))

        bind(3 as ?hops3)

        optional {

            ?s4 ?p4 ?o.

            filter(!bound(?p4))

            bind(4 as ?hops4)

            }

        }

    }

bind(coalesce(?hops4,?hops3,?hops2,?hops1,?hops) as ?hops)

}

The bound() function evaluates a variable and returns true() if the variable has been defined and false() otherwise, while the ! operator is the not operator – it flips the value of a Boolean from true to false and vice-versa. Note that if the filter expression evaluates to false(), this will terminate the particular scope. A bind() function will cause a variable to be bound, but so will a triple expression … UNLESS that triple expression is within an OPTIONAL block and nothing is matched.

This approach is flexible but potentially slow and memory intensive, as it will reach out to everything with four hops of the initial node. The filter statements act to limit this: if you have a pattern node-null-null, then this should indicate that the object is also a leaf node, so no more needs to be processed. (This can be generalized, as will be shown below, if you’re in a transitive closure situation).

Tip 2. EXISTS and NOT EXISTS

The EXISTS and NOT EXISTS keywords can be extraordinarily useful, but they can also bog down performance dramatically is used incorrectly. Unlike most operators in SPARQL, these two actually work upon sets of triples, returning true or false values respectively if the triples in question exist. For instance, if none of ?s, ?p or ?o have been established yet: the expression:

# SPARQL

 

filter(NOT EXISTS {?s ?p ?o})

WILL cause your server to keel over and die. You are, in effect, telling your server to return all triples that don’t currently exist in your system, and while this will usually be caught by your server engine’s exception handler, this is not something you want to test.

However, if you do have at least one of the variables pinned down by the time this expression is called, these two expressions aren’t quite so bad. For starters, you can use EXISTS and NOT EXISTS within bind expressions. For example, suppose that you wanted to identify any orphaned link, where an object in a statement does not have a corresponding link to a subject in another statement:

# SPARQL

 

select ?o ?isOrphan where {

      ?s ?p ?o.

      filter(!(isLiteral(?o))

      bind(!(EXISTS {?o ?p1 ?o2)) as ?isOrphan)

      }

In this particular case, only those statements in which the final term is not a literal (meaning those for which the object is either an IRI or a blank node) will be evaluated, The bind statement then looks for the first statement in which the ?o node is a subject in some other statement, the EXISTS keyword then returns true if at least one statement is found, while the ! operator inverts the value. Note that EXISTS only needs to find one statement to be true, while NOT EXISTS has to check the whole database to make sure that nothing exists. This is equivalent to the any and all keywords in other languages. In general, it is FAR faster to use EXISTS this way than to use NOT EXISTS.

Tip 3. Nested IF statements as Switches (And Why You Don’t Really Need Them)

The SPARQL if() statement is similar to the Javascript condition?trueExpression:falseExpression operator, in that it returns a different value based upon whether the condition is true or false. While the expressions are typically literals, there’s nothing stopping you from using object IRIs, which can in turn link to different configurations. For instance, consider the following Turtle:

#Turtle

 

petConfig:_Dog a class:_PetConfig;

    petConfig:hasPetType petType:_Dog;

    petConfig:hasSound “Woof”;

    .

 

petConfig:_Cat a class:_PetConfig;

    petConfig:hasPetType petType:_Cat;

    petConfig:hasSound “Meow”;

    .

 

petConfig:_Bird a class:_PetConfig;

    petConfig:hasPetType petType:_Bird;

    petConfig:hasSound “Tweet”;

    .

 

pet:_Tiger pet:says “Meow”.

pet:_Fido pet:says “Woof”.

pet:_Budger pet:says “Tweet”.

You can then make use of the if() statement to retrieve the configuration:

# SPARQL

select ?pet ?petSound ?petType where {

    values (?pet ?petSound) {(pet:_Tiger “Meow”)}

    bind(if(?petSound=’Woof’,petType:_Dog,

            ?petSound=’Meow’,petType:_Cat,

            ?petSound=’Tweet’,petType:_Bird,

            ()) as ?petType)

}

where the expression () returns a null value.

Of course, you can also use a simple bit of Sparql to infer this without the need for the if s#tatement:

# SPARQL

 

select ?pet ?petSound ?petType where {

    values (?pet ?petSound) {(pet:_Tiger “Meow”)}

    ?petConfig petConfig:hasSound ?petSound.

    ?petConfig petConfig:hasPetType ?petType.

}

with the results:

?pet	?petSound	?petType
pet:_Tiger	“Meow”	petType:_Cat

As a general rule of thumb, the more that you can encode as rules within the graph, the less that you need to rely on if or switch statements and the more robust your logic will be. For instance, while a dogs and cats express themselves in different ways most of the time, both of them can growl:

#Turtle

 

petConfig:_Dog a class:_PetConfig;

    petConfig:hasPetType petType:_Dog;

    petConfig:hasSound “Woof”,”Growl”,”Whine”;

    .

 

petConfig:_Cat a class:_PetConfig;

    petConfig:hasPetType petType:_Cat;

    petConfig:hasSound “Meow”,”Growl”,”Purr”;

    .

?pet	?petSound	?petType
pet:_Tiger	“Growl”	petType:_Cat
Pet:_Fido	“Growl”	petType:_Dog

In this case, the switch statement would break, as Growl is not in the options, but the direct use of SPARQL works just fine.

Tip 4. Unspooling Sequences

Sequences, items that are in a specific order, are fairly easy to create with SPARQL but surprisingly there are few explanations for how to build them . . . or query them. Creating a sequence in Turtle involves putting a list of items in between parenthesis as part of an object. For instance, suppose that you have a book that consists of a preface, five numbered chapters, and an epilogue. This would be expressed in Turtle as:

#Turtle

 

book:_StormCrow book:hasChapter (chapter:_Prologue chapter:_Chapter1 chapter:_Chapter2 chapter:_Chapter3

     chapter:_Chapter4 chapter:_Chapter5 chapter:_Epilogue);

Note that there are no commas between each chapter.

Now, there is a little magic that Turtle parsers do in the background when parsing such sequences. They actually convert the above structure into a string with blank nodes, using the three URIs rdf:first, rdf:rest and rdf:nil. Internally, the above statement looks considerably different:

# Turtle

 

book:_StormCrow book:hasChapter _:b1.

_:b1 rdf:first chapter:_Prologue.

_:b1 rdf:rest _:b2.

_:b2 rdf:first chapter:_Chapter1.

_:b2 rdf:rest _:b3.

_:b3 rdf:first chapter:_Chapter2.

_:b3 rdf:rest _:b4.

_:b4 rdf:first chapter:_Chapter3.

_:b4 rdf:rest _:b5.

_:b5 rdf:first chapter:_Chapter4.

_:b5 rdf:rest _:b6.

_:b6 rdf:first chapter:_Chapter5.

_:b6 rdf:rest _:b7.

_:b7 rdf:first chapter:_Epilogue.

_:b7 rdf:rest rdf:nil.

While this looks daunting, programmers might recognize this as being a very basic linked list, whether rdf:first points to an item in the list, and rdf:rest points to the next position in the list. The first blank node, _:b1, is then a pointer to the linked list itself. The rdf:nil is actually a system defined URI that translates into a null value, just like the empty sequence (). In fact, the empty sequence in SPARQL is in fact the same thing as a linked list with no items and a terminating rdf:nil.

Since you don’t know how long the list is likely to be (it may have one item, or thousands) building a query to retrieve the chapters in their original order would seem to be hopeless. Fortunately, this is where transitive closure and property paths come into play. Assume that each chapter has a property called chapter:hasTitle (a subproperty of rdfs:label). Then to retrieve the names of the chapters in order for a given book, you’d do the following:

# SPARQL

 

select ?chapterTitle where {

    values ?book {book:_StormCrow}

    ?book rdf:rest*/rdf:first ?chapter.

    ?chapter chapter:hasTitle ?chapterTitle.

    }

That’s it. The output, then, is what you’d expect for a sequence of chapters:

pointsTo

chapter:_Prologue

chapter:_Chapter1

chapter:_Chapter2

chapter:_Chapter3

rdf:nil

The property path rdf:rest*/rdf:first requires a bit of parsing to understand what is happening here. property* indicates that, from the subject, the rdf:rest path is traversed zero times, one time, two times, and so forth until it finally hits rdf:nil. Traversing zero times may seem a bit counterintuitive, but it means simply that you treat the subject as an item in the traversal path. At the end of each path, the rdf:first link is then traversed to get to the item in question (here, each chapter in turn. You can see this broken down in the following table:

 

path	pointsTo
rdf:first	chapter:_Prologue
rdf:rest/ rdf:rest/rdf:first	chapter:_Chapter1
rdf:rest/r rdf:rest/ rdf:rest/df:first	chapter:_Chapter2
rdf:rest/ rdf:rest/ rdf:rest/ rdf:rest/rdf:first	chapter:_Chapter3
rdf:rest/ rdf:rest/ rdf:rest/ rdf:rest/rdf:rest	rdf:nil

 

If you don’t want to include the initial subject in the sequence, then use rdf:rest+/rdf:first where the * and + have the same meaning as you may be familiar with in regular expressions, zero or more and one or more respectively.

This ability to traverse multiple repeating paths is one example of transitive closure. Transitive closures play a major role in inferential analysis and can easily take up a whole article in its own right, but for now, it’s just worth remembering the ur example – unspooling sequences.

The ability to create sequences in TURTLE (and use them in SPARQL) makes a lot of things that would otherwise be difficult if not impossible to do surprisingly easy.

As a simple example, suppose that you wanted to find where a given chapter is in a library of books. The following SPARQL illustrates this idea:

# SPARQL

 

select ?book where {

    values ?searchChapter {?chapter:_Prologue}

    ?book a class:_book.

    ?book rdf:rest*/rdf:first ?chapter.

    filter(?chapter=?searchChapter)

}

This is important for a number of reasons. In publishing in particular there’s a tendency to want to deconstruct larger works (such as books) into smaller ones (chapters), in such a way that the same chapter can be utilized by multiple books. The sequence of these chapters may vary considerably from one work to the next, but if the sequence is bound to the book and the chapters are then referenced there’s no need for the chapters to have knowledge about its neighbors. This same design pattern occurs throughout data modeling, and this ability to maintain sequences of multiply utilized components makes distributed programming considerably easier.

Tip 5. Utilizing Aggregates

I work a lot with Microsoft Excel documents when developing semantic solutions, and since Excel will automatically open up CSV files, using SPARQL to generate spreadsheets SHOULD be a no brainer.

However, there are times where things can get a bit more complex. For instance, suppose that I have a list of books and chapters as above, and would like for each book to list it’s chapters in a single cell. Ordinarily, if you just use the ?chapterTitle property as given above, you’ll get one line for each chapter, which is not what’s wanted here:

# SPARQL

 

select ?bookTitle ?chapterTitle where {

    values ?searchChapter {?chapter:_Prologue}

    ?book a class:_book.

    ?book rdf:rest*/rdf:first ?chapter.

    ?chapter chapter:hasTitle ?chapterTitle.

    ?book book:hasTitle ?bookTitle.

}

This is where aggregates come into play, and where you can tear your hair out if you don’t know the Ninja Secrets. To make this happen, you need to use subqueries. A subquery is a query within another query that calculates output that can then be pushed up to the calling query, and it usually involves working with aggregates – query functions that combine several items together in some way.

One of the big aggregate workhorses (and one that is surprisingly poorly documented) is the concat_group() function. This function will take a set of URIs, literals or both and combine them into a single string. This is roughly analogous to the Javascript join() function or the XQuery string-join() function. So, to create a comma separated list of chapter names, you’d end up with a SPARQL script that looks something like this:

# SPARQL

 

select ?bookTitle ?chapterList ?chapterCount where {

    ?book a class:_book.

    ?book book:hasTitle ?bookTitle.

    {{

         select ?book

                (group_concat(?chapterTitle;separator=”\n”) as ?chapterList)

                (count(?chapterTitle) as ?chapterCount) where {

            ?book rdf:rest*/rdf:first ?chapter.

            ?chapter chapter:hasTitle ?chapterTitle.

         } group by ?book

    }}

}

The magic happens in the inner select, but it requires that the SELECT statement includes any variable that is passed into it (here ?book) and that the same variable is echoed in the GROUP BY statement after the body of the subquery.

Once these variables are “locked down”, then the aggregate functions should work as expected. The first argument of the group_concat function is the variable to be made into a list. After this, you can have multiple optional parameters that control the output of the list, with the separator being the one most commonly used. Other parameters can include ROW_LIMIT, PRE (for Prefix string), SUFFIX, MAX_LENGTH (for string output) and the Booleans VALUE_SERIALIZE and DELIMIT_BLANKS, each separated by a semi-colon. Implementations may vary depending upon vendor, so these should be tested.

Note that this combination can give a lot of latitude. For instance, the expression:

# SPARQL

 

group_concat(?chapterTitle;separator=”</li><li>”;pre=”<ul><li>”;suffix=”</li></ul>”)

will generate an HTML list sequence, and similar structures can be used to generate tables and other constructs. Similarly, it should be possible to generate JSON content from SPARQL through the intelligent use of aggregates, though that’s grist for another article.

The above script also illustrates how a count function has piggy-backed on the same subquery, in this case using the COUNT() function.

It’s worth mentioning the spif:buildString() function (part of the SPIN Function library that is supported by a number of vendors) which accepts a string template and a comma-separated list of parameters. The function then replaces each instance of “{?1}”,”{?2}”, etc. with the parameter at that position (the template string being the zeroeth value). So a very simple report from above may be written as

# SPARQL

 

bind(spif:buildString(“Book ‘{$1}’ has {$2} chapters.”,?bookTitle,?chapterCount) as ?report)

which will create the following ?report string:

Book ‘Storm Crow’ has 7 chapters.

This templating capability can be very useful, as templates can themselves be stored as resource strings, with the following Turtle:

#Turtle

 

reportTemplate:_BookReport

     a class:_ReportTemplate;

     reportTemplate:hasTemplateString “Book ‘{$1}’ has {$2} chapters.”^^xsd:string;

.

This can then be referenced elsewhere:

#SPARQL

 

select ?report where {

    ?book a class:_book.

    ?book book:hasTitle ?bookTitle.

    {{

         select ?book

                (group_concat(?chapterTitle;separator=”\n”) as ?chapterList)

                (count(?chapterTitle) as ?chapterCount) where {

            ?book rdf:rest*/rdf:first ?chapter.

            ?chapter chapter:hasTitle ?chapterTitle.

         } group by ?book

    }}

    reportTemplate:_BookReport reportTemplate:hasTemplateString ?reportStr.

    bind(spif:buildString(?reportStr,?bookTitle,?chapterCount) as ?report).

}

With output looking something like the following:

 

report

Book ‘Storm Crow’ has 7 chapters.

Book “The Scent of Rain” has 25 chapters.

Book “Raven Song” has 18 chapters.

 

This can be extended to HTML-generated content as well, illustrating how SPARQL can be used to drive a basic content management system.

Tip 6. SPARQL Analytics and Extensions

There is a tendency among programmers new to RDF to want to treat a triple store the same way that they would a SQL database – use it to retrieve content into a form like JSON and then do the processing elsewhere. However, SPARQL is versatile enough that it can be used to do basic (and not so basic) analytics all on its own.

For instance, consider the use case where you have items in a financial transaction, where the items may be subject to one of three different types of taxes, based upon specific item details. This can be modeled as follows:

# Turtle

 

item:_CanOfOil

    a class:_Item;

    item:hasPrice 5.95;

    item:hasTaxType taxType:_NonFoodGrocery;

    .

 

item:_BoxOfRice

    a class:_Item;

    item:hasPrice 3.95;

    item:hasTaxType taxType:_FoodGrocery;

    .

 

item:_BagOfApples

    a class:_Item;

    item:hasPrice 2.95;

    item:hasTaxType taxType:_FoodGrocery;

    .

 

item:_BottleOfBooze

    a class:_Item;

    item:hasPrice 8.95;

    item:hasTaxType taxType:_Alcohol;

    .

 

taxType:_NonFoodGrocery

    a class:_TaxType;

    taxType:hasRate 0.08;

    .

 

taxType:_FoodGrocery

    a class:_TaxType;

    taxType:hasRate 0.065;

    .

 

taxType:_Alcohol

    a class:_TaxType;

    taxType:hasRate 0.14;

    .

 

order:_ord123

    a class:_Order;

    order:hasItems (item:_CanOfOil item:_BoxofRice item:_BagOfApples item:_BottleOfBooze);

    .

This is a fairly common real world scenario, and the logic for handling this in a traditional language, while not complex, is still not trivial to determine a total price. In SPARQL, you can again make use of aggregate functions to do things like get the total cost:

#SPARQL

 

select ?order ?totalCost where {

     values ?order {order:_ord123}

     {{

         select ?order (sum(?itemTotalCost) as ?totalCost) where {

             ?order order:hasItems ?itemList.

             ?itemList rdf:rest*/rdf:first ?item.

             ?item item:hasPrice ?itemCost.

             ?item item:hasTaxType ?taxType.

             ?taxType taxType:hasRate ?taxRate.

             bind(?itemCost * (1 + ?taxRate) as ?itemTotalCost)

             }

         group by ?order

    }}

}

While this is a simple example, weighted cost sum equations tend to make up the bulk of all analytics operations. Extending this to incorporate other factors such as discounts is also easy to do in situ, with the following additions to the model:

# Turtle

 

discount:_MemorialDaySale

    a class:_Discount;

    discount:hasRate 0.20;

    discount:appliesToItem item:_CanOfOil item:_BottleOfBooze;

    discount:hasStartDate 2021-05-28;

    discount:hasEndDate 2021-05-31;

    .

This extends the SPARQL query out a bit, but not dramatically:

# SPARQL

 

select ?order ?totalCost where {

     values ?order {order:_ord123}

     {{

          select ?order(sum(?itemTotalCost) as ?totalCost) where {

              ?order order:hasItems ?itemList.

              ?itemList rdf:rest*/rdf:first ?item.

              ?item item:hasPrice ?itemCost.

              ?item item:hasTaxType ?taxType.

              ?taxType taxType:hasRate ?taxRate.

              optional {

                 ?discount discount:appliesToItem ?item.

                 ?discount discount:hasRate ?DiscountRate.

                 ?discount discount:hasStartDate ?discountStartDate.

                 ?discount discount:hasEndDate ?discountEndDate.

                 filter(now() >= ?discountStartDate and ?discountEndDate >= now())

              }

              bind(coalesce(?DiscountRate,0) as ?discountRate)

              bind(?itemCost*(1 – ?discountRate)*(1 + ?taxRate) as ?itemTotalCost)

              }

    }}

}

In this particular case, taxes are required, but discounts are optional. Also note that the discount price is only applicable around Memorial Day weekend, with the filter set up in such a way that ?DiscountRate would be null at any other time. The conditional logic required to support this externally would be getting pretty hairy at this point, but the SPARQL rules extend it with aplomb.

There is a lesson worth extracting here: use the data model to store contextual information, rather than relying upon outside algorithms. It’s straightforward to add another discount period (a sale, in essence) and with not much more work you can even have multiple overlapping sales apply on the same item.

Summary

The secret to all of this: these aren’t really Ninja secrets. SPARQL, while not perfect, is nonetheless a powerful and expressive language that can work well when dealing with a number of different use cases. By introducing sequences, optional statements, coalesce, templates, aggregates and existential statements, a good SPARQL developer can dramatically reduce the amount of code that needs to be written outside of the database. Moreover, by taking advantage of the fact that in RDF everything can be a pointer, complex business rules can be applied within the database itself without a significant overhead (which is not true of SQL stored procedures).

So, get out the throwing stars and stealthy foot gloves: It’s SPARQL time!

Kurt Cagle is the community editor for Data Science Central, and the editor of The Cagle Report.

 

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More Machine Learning Tricks, Recipes, and Statistical Models

Source for picture: here

The first part of this list was published here. These are articles that I wrote in the last few years. The whole series will feature articles related to the following aspects of machine learning:

• Mathematics, simulations, benchmarking algorithms based on synthetic data (in short, experimental data science)
• Opinions, for instance about the value of a PhD in our field, or the use of some techniques
• Methods, principles, rules of thumb, recipes, tricks
• Business analytics 
• Core Techniques 

My articles are always written in simple English and accessible to professionals with typically one year of calculus or statistical training, at the undergraduate level. They are geared towards people who use data but are interesting in gaining more practical analytical experience. Managers and decision makers are part of my intended audience. The style is compact, geared towards people who do not have a lot of free time. 

Despite these restrictions, state-of-the-art, of-the-beaten-path results as well as machine learning trade secrets and research material are frequently shared. References to more advanced literature (from myself and other authors) is provided for those who want to dig deeper in the interested topics discussed. 

1. Machine Learning Tricks, Recipes and Statistical Models

These articles focus on techniques that have wide applications or that are otherwise fundamental or seminal in nature.

1. One Trillion Random Digits
2. New Perspective on the Central Limit Theorem and Statistical Testing
3. Simple Solution to Feature Selection Problems
4. Scale-Invariant Clustering and Regression
5. Deep Dive into Polynomial Regression and Overfitting
6. Stochastic Processes and New Tests of Randomness – Application to Cool Number Theory Problem
7. A Simple Introduction to Complex Stochastic Processes – Part 2
8. A Simple Introduction to Complex Stochastic Processes
9. High Precision Computing: Benchmark, Examples, and Tutorial
10. Logistic Map, Chaos, Randomness and Quantum Algorithms
11. Graph Theory: Six Degrees of Separation Problem
12. Interesting Problem for Serious Geeks: Self-correcting Random Walks
13. 9 Off-the-beaten-path Statistical Science Topics with Interesting Applications
14. Data Science Method to Discover Large Prime Numbers
15. Nice Generalization of the K-NN Clustering Algorithm –  Also Useful for Data Reduction
16. How to Detect if Numbers are Random or Not
17. How and Why: Decorrelate Time Series
18. Distribution of Arrival Times of Extreme Events
19. Why Zipf’s law explains so many big data and physics phenomenons

2. Free books

• Statistics: New Foundations, Toolbox, and Machine Learning Recipes

  Available here. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach.

  The material is accessible to practitioners with a one-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications with numerous illustrations, is aimed at practitioners, researchers, and executives in various quantitative fields.

• Applied Stochastic Processes

  Available here. Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems (104 pages, 16 chapters.) This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject.

  It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.

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). He recently opened Paris Restaurant, in Anacortes. You can access Vincent’s articles and books, here.

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This is the FIRST thing data science professionals should know about working with universities

If you wanted to recruit for “data science” talent at a university, where would go? Should you go to the College of Computing? Would it be in the College of Business? Is it in the Department of Mathematics? Statistics? Is there even a Department of Data Science? 

There is more variation in the housing of data science than any other academic discipline on a university campus. Why the variation? And why should you care?

The answer to the first question – Why the variation? – may not be straightforward.

As in any organization, not all academic programs are a function of long-term, well-considered strategic planning – many analytics programs evolved at the intersection of resources, needs, and opportunity (and some noisy passionate faculty). As universities began to formally introduce data science programs around 2006, there was little consistency regarding where this new discipline should be housed. Given the “academic ancestry” of analytics and data science it is not surprising that there is variation of placement of programs across the academic landscape:

Exacerbating this, we do not yet have a universal consensus as to what set of competencies should be common to a data science curriculum – again largely due to its transdisciplinary foundations. The fields of computer science, mathematics, statistics, and almost every applied field (business, health care, engineering) have professional organizations and long-standing models for what constitutes competency in those fields. Data science has no standardization, no accreditation, and no certifying body. As a result, the “data science” curriculum may look very different at different universities – all issues that have contributed to the misalignment of expectations for both students and for hiring managers. 

 The second question (why should you care?) might be more relevant –

 Generally, universities have approached the evolution of data science from one of two perspectives – as a discipline “spoke” (or series of electives) or as a discipline “hub” (as a major) as in the graphic above.

University programs that are “hubs” – reflecting the model above on the left – have likely been established as a “major” field of study. These programs are likely to be housed in a more computational college (e.g., Computing, Science, Statistics) or in a research unit (like a Center or Institute) and will focus on the “science of the data”. They tend to be less focused on the nuances of any individual area of application. Hub programs will (generally) allow (encourage) their students to take a series of electives in some application domain (i.e., students coming out of a hub program may go into Fintech, but they may also go into Healthcare – their major is “data”). Alternatively, programs that are “spokes” – reflecting the model above on the right – are more likely to be called “analytics” and are more frequently housed in colleges of business, medicine, and the humanities.  Programs that are “spokes” are (generally) less focused on the computational requirements and are more aligned with applied domain-specific analytics. Students coming out of these programs will have stronger domain expertise and will better understand how to integrate results into the original business problem but may lack deep computational skills.  Neither is “wrong” or “better” – the philosophical approaches are different. 

Understanding more about where an analytics program is housed and whether analytics is treated as a “hub” or a “spoke” should inform and improve analytics professionals collaborative experiences with universities.

The book “Closing the Analytics Talent Gap” is available through Amazon and directly from the publisher CRC Press.

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Digital Onboarding Trends: 2021

Due to the response to the COVID-19 pandemic, banks, and financial services companies have faced unexpected difficulties over the past year. By accelerated adaptation initiatives and a greater emphasis on their digital presence as consumers go online to access essential services, the industry is rethinking the future of digital banking. During these troubling times, business leaders are increasingly realizing the enormous potential of digitization and the need to integrate it into onboarding to maintain a foothold in the sector.

Digitization is a Necessity Not an Option

The pre-COVID-19 financial industry still had to update the digital experience by incorporating consumer preferences into business strategies, despite the demand for digital experiences among customers of all ages. In recent months, there has been a dramatic acceleration in the digital race. The ‘nice-to-have or ‘Plan B’ is no longer applicable for digital onboarding and real-time communication tools, but rather it’s a mandatory requirement. Companies need to look at video conferencing, digital document management, digital signature, video identification and biometrics, and cloud services and use smart automation to reduce operational burdens and ensure security compliance.

Faster Implementation is Key

The response to COVID-19 has put the spotlight on two major categories of disadvantages in traditional onboarding namely slow processes and poor customer experience. Traditional processes are generally slow, repetitive, and complex. Low speed of progress can lead to a 40% abandonment rate in onboarding, and nearly 7 out of 10 millennials demand a seamlessly integrated experience for digital services across all channels. There is a demand for faster and easier onboarding processes.

Aggregate and Integrate

The need to address the disaggregation of client data, processes, and stakeholders was a core problem in financial services at the beginning of 2020. It is vital to support a remote onboarding model with repeated cycles of government-imposed social distancing by implementing intelligent orchestration within the IT structure of the organization, which leads to greater organizational agility and resilience. This strategy enables businesses to incorporate third-party technologies such as biometrics, cloud, real-time messaging services, artificial intelligence (AI), NBA (next-best-action) seamlessly while streamlining interactions with digital consumers.67% of customers prefer self-service instead of speaking to a company representative. AI can help integrate this. Smart analytics and artificial intelligence (AI) are being used by businesses to figure out what really matters to their customers. This enables them on the platforms of their choosing to give consumers what they care about.

eKYC for Secure Onboarding

Digital onboarding provides companies with the opportunity to react at the pace of information to changes. Organizations using these solutions can, within a few minutes, sign up their clients, open their bank accounts, complete a loan procedure, and their KYC – a process that had previously taken days to complete. This is exactly why organizations, in order to prepare for the future, must act now. Organizations will have to work alongside a recovery strategy as business resumes, where both downtime and inefficiency of employees will have an undesirable impact. Early eKYC adopters are now writing their success stories and redefining the consumer needs baseline. We also live in a world where consumers are busier than ever, which means that it is important to eliminate friction across the entire customer journey across both digital and physical channels and allow hybrid experiences. Customers are pleased not to deal with time-consuming, non-essential documents and have the ability to concentrate on more important and meaningful data. IDcentral’s suite of onboarding services provides end-to-end eKYC solution that helps onboarding the customers with ease without compromising the security. Additionally, IDcentral also helps reduce customer drop-out rates by transcending beyond the lengthy processes and inefficiencies of current solutions. IDcentral’s highly accurate OCR is able to extract exact information even if the document uploaded is blurred or skewed by 360 degrees which improves customer experience by eliminating iterations while uploading the ID documents. IDcentral’s liveness solution includes 3D Mapping, Skin Texture Analysis, Micro Movement Detection capabilities which eliminate the slightest possibility of morphing and spoof attacks. Companies can pick and choose the services they need as per their vertical and risk appetite and access these services free for the first six months which can save a lot of your cost. It is a completely self-serve platform that you can integrate into your system within a few hours and start using it.

Better Communication for Better Customer Satisfaction

For businesses, data is becoming the most coveted commodity, which is why integration becomes all the more important. To ensure that meaningful and appropriate information is still available, connect the various recording systems and keep the data synchronized and consistent. Employees can benefit from the availability of data and boost communication between teams, work effortlessly together and create more value. Innovation is simple because it does not understand how each variable relates to the other. Businesses have to bear in mind that the more connected everything is, the better for the best long-term result.

Expand Existing Connections

Instead of an immense opportunity to establish relationships, client onboarding is often perceived as a repetitive process. For the customer, the lack of concrete details on their onboarding status becomes frustrating. In addition, the absence of a customer’s centralized view can lead to possible fallouts. Make onboarding interactions, even in more complicated situations, as simple as possible. Onboard an entire family in a single process. Include multiple goods and facilities that do not share the same processes (like checking the eligibility of a more complicated service in detail for an individual person). Therefore, to attract the customers of today and convert them to loyal clients, a strategy centered on smoother digital onboarding is more than necessary.

The article is originally published at IDcentral.

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Solving Modern Data Protection Challenges

As more and more data resides in online repositories, data backup and protection have taken on critical importance – not just for huge corporations but for organizations of all sizes. In fact, these capabilities may even now be determining an organization’s future. Veeam commissioned an independent survey of 1,550 larger companies (with over 1,000 users) across 18 countries to examine how data backup and restoration are currently being handled.

The survey reveals that as IT practices improve in the area of data protection and backup, a continually changing digital transformation is taking place. Surveyed organizations use a diverse mix of physical servers, virtual machines and cloud-hosted virtual machines (VMs), while around 10% of on-premises data systems will shift to the cloud over the next two years.

The surveyed companies expressed interest in ensuring that the cloud and data are more available to help improve customer experience and the impact of their brands. “However, the research infers that by modernizing data protection with easy-to-use and flexible solutions,” the survey states, “businesses can greatly increase the protection and usability of their data while also freeing a lot of resources to focus further on their IT modernization and management efforts.”

Backup challenges

Backing up and restoring data is a major concern because the data provided by IT is the “heart and soul” of modern companies. Downtime is another issue, with 95% of the surveyed organizations experiencing unexpected outages; at least 10% of servers have at least one outage of two hours on average, once per year. The researchers note, therefore, how important it is to modernize data protection for those inevitable outages. Doing so can help better manage operations, impact customer service, reduce costs and lessen employee task time.

Any time there is a change, and especially a large change like modernizing current systems, there will be challenges. Some include a lack of IT skills in a company’s workforce, a dependency on legacy systems and a lack of staff and/or budget that ultimately prevents them from engaging in this digital transformation.

The surveyed companies indicated that they want to be able to move workloads from on-premises to the cloud, and they want cloud-based disaster recovery. Flexibility of solutions, the researchers conclude, is a big factor in the adoption of new systems and technologies. Data protection, therefore, must be simple, have no delays, and present an immediate return on investment (ROI). It must also be flexible enough to allow for data access from anywhere and at any time. It must continue to be reliable, as well, even as the IT environment evolves.

When planning to improve their current backup systems, companies are looking for reductions in costs and complexity, improved recovery time and reliability. Modernizing your backup into cloud data management can cut the cost of data backup and protection by 50%. That can lead to a 55% increase, says Veeam, in efficiency as well.

Most current mission-critical systems are still tied to legacy solutions, most often located on-site. It’s implausible, then, to expect that organizations will jump directly to a fully modernized backup system. But by starting with a hybrid solution, where data is stored both on-premises and in the cloud, managed by a unified toolset, companies are seeing a 49% savings on costs, according to the survey.

Compliance and security

Another factor is that the cost of compliance is rising as governmental regulation continues to increase across the globe. Moving from ad-hoc or legacy systems to protect and audit data, as companies tend to do now, can result in what the researchers call “isolated pockets of visibility.” And these “pockets” can be targeted by cyber-attackers.

A primary challenge for organizations today is to make sure data is reliably backed up and instantly recovered when needed. As organizations continue to create more and more data, so must data protection and backup rise to the challenge. Modern systems must be more intelligent, anticipate user needs and meet user demands.

Building a new approach

Change is not without its hurdles, but the research demonstrates that organizations cannot afford to ignore the changing IT landscape. Data protection and backup have become mission-critical issues as data volumes continue to explode as data gets distributed across the cloud. Simple, flexible solutions are a must – and they must also be affordable. A robust data management system helps organizations remain compliant and gain greater visibility to defend against attack. As IT leaders consider a new approach to data protection and backup, they should take into account the significant benefits of automated, cloud-friendly solutions.

 

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Electrical Flexibility: what is it?

• increase in system costs;
• impossibility using non-programmable renewable sources;
• need to keep power plants from non-renewable sources in operation;
• creation of new power plants to coverage changes in demand;
• limited operation in the #Flexibility market, mainly focused on capacity incentives;
• increase in the carbon footprint.

• “What strategy to adopt?“
• “How to overcome the current problems in the electricity sector managed so far in a monolithic way and using a privileged position (incentives)?“
• “What solutions to adopt to unlock the new values of the #Flexibility in the panorama of #EnergyTransition?“

• valorisation of #Renewable according to priorities as to economic, physical and environmental;
• grid inbalancing avoided through the implementation of dynamic corrections to consumption programs, without impacting operational processes;
• active participation of the #Demand in the #Flexibility processes.

• Real-time acquisition of the energy consumption data of each load;
•  Building of the *Characteristic Energy Profile*;
• Definition of the energy consumption program associated with the operational activity in compliance with the predetermined performance indices.

• Dynamic implementation of energy consumption programs (`#Baseline`) through ordinary modulation of set points;
• Periodic verification of actual compliance with the predetermined performance indices.

• Dynamic correction of the consumption program through an extraordinary modulation of the set points, drawing as needed from a predetermined list of possible interventions;
• Punctual activation in the event of operational criticalities or in the face of remuneration opportunities.

• Identification of the actual availability (`#Flexibility`)of modulated energy in compliance with the predetermined performance indices;
• Periodic communication of the consumption program and availability to modulation towards the aggregator;
• Implementation of the modulation requested by the aggregator when the network is actually required.

• awareness of the impact of energy consumption on its operating activities.
• reduction of costs associated with energy consumption;
• improvement of corporate image positioning through Corporate Social Responsibility.

• Supply clean, affordable and secure energy;
• Building and renovating in an energy and resource efficient way;
• Accelerating the switch to sustainable and smart mobility.

• Decarbonisation;
• #Flexibility;
• Active participation of consumer/prosumers.

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Benefits of improving data quality

As the digital world continues to become more competitive, everyone is trying to understand their customers better and make finance, development, and marketing decisions based on real data for a better ROI.

Bad data is misleading and would be even more detrimental to your business than a lack of data at all. Organizations may also be forced to abide by data quality guidelines because of compliance issues. If your business’s data is not properly maintained or organized, you may struggle to demonstrate compliance. They find themselves in possession of sensitive personal and financial data such as banks may particularly face more stringent data management prerequisites, if not complied.

Good data quality enables:

Effective decision making: Good quality data leads to accurate and realistic decision-making and also boosts your confidence as you make the decisions. It takes away the need to guesstimate and saves you the unnecessary costs of trials and errors.

More focused: As part of the value chain proposition, it’s critical you know who your prospects are – something that you can only manage analyzing and understanding data. Using high-quality data from your current customer base, you can create user personas and anticipate the needs of the new opportunities and target markets.

Efficient marketing: There are many forms of digital marketing out there, and each one of them works differently for different products in various niches. Good data quality will help you identify what’s working and what’s not.

Better customer relationships: You cannot succeed in any industry if you have poor customer relations. Most people only want to do business with brands they can trust. Creating that bond with your customers starts with understanding what they want.

Competitive Advantage: Being in possession of good quality data gives you a clearer picture of your industry and its dynamics. Your marketing messages will be more specific, and your projections in market changes will bear more accuracy. It will also be easier for you to anticipate the needs of your customers, which will help you beat your rivals to sales.

An AI-augmented data platform, such as DQLabs, would help you detect and address poor data quality issues without the need for much human effort. Since it is AI-based, it will discover patterns and, if possible, tune itself to curb data quality issues of the type it has come across before.

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7 Key Advantage of Using Blockchain for Banking Software Development

Did you know!

• Worldwide spending on blockchain solutions is expected to cross 15.9 billion dollars by 2023.
• 90% of U.S. and European banks are exploring blockchain solutions to stay ahead of the game.
• To date, financial institutions alone have spent $552+ million on blockchain-based development projects.

And we can go on and on with these insightful stats relating blockchain with banking and financial institutions, as per Fortunately.

Since getting conceptualized by Satoshi Nakamoto in 2008 in bitcoin cryptocurrency form, blockchain has witnessed remarkable new and innovative applications in software development.

The fintech industry always looks for technology tools that enhance security, and blockchain has emerged as a viable solution. The technology is getting used in diverse ways by banks and other financial institutions to ensure the highest level of privacy and protection.

The infographic below illustrates the blockchain investors’ industry focus for the year 2019.

Source: Statista 

                                                               

With the passing years, the number of deals has increased significantly as diverse industry verticals are exploring the technology. Investment from the banking sector is also rising owing to multiple benefits from the technology.

Blockchain offers a lot more than high-security standards to the financial institution. Have a quick look at the infographic below that lists the top benefits of blockchain for banks and financial institutions.

Before you proceed to learn them in detail, let’s recollect some basic concepts.

So, do you know what exactly defines blockchain?

In simple as ABC, blockchain is a kind of distributed ledger technology that records data in a secured manner with zero possibility of data altercation. Being a distributed technology, it has the following extraordinary features:

 

• Each node of the network keeps the ledger account.
• The data stored is immutable, which means it cannot get modified by a user.
• Every transaction bears a time stamp.
• The data record is encrypted.
• It is a programmable technology.

Main types of blockchain used by the banking industry

The blockchain can be classified into many kinds, but for the sake of lucidity, we will stick to four main types of blockchain:

 

Public Blockchain: A non-restrictive, permission-less distributed ledger system is referred to as the public blockchain. A public blockchain is open to all, and anyone can become an authorized user for accessing past and current records. 

Best examples of Public Blockchain include the digital currency of bitcoin, Litecoin, etc.

 

Private Blockchain: A restrictive and permission-based blockchain is called a private blockchain. It is meant for personal use in enterprises, and the level of scalability, accessibility, gets controlled by the administrative department.

Best examples of Private Blockchain includes Fabric, Corda, etc. 

 

Consortium Blockchain: A blockchain similar to private blockchain but gets utilized by multiple enterprises is called a consortium blockchain. It allows users from multiple enterprises thus actively used by banks, government organizations, etc.

Best examples of Consortium Blockchain includes R3, Energy web foundation, etc.

 

Hybrid Blockchain: As the name suggests, a hybrid blockchain is a mix between Private Blockchain and Public Blockchain. It allows users to go for both permission-based and permission-less features. The organization can control whether to let a particular transaction go for public or private use.

The best example of Hybrid Blockchain is Dragonchain.

 

Here are the top 7 benefits of blockchain solutions in banking software development

1. Reduces Running Cost

Blockchain effectively builds trust between the bank and its trading partner (whether a client or another bank). The high trust between the partners conducting financial transactions removes the necessity of mediators and third-party software otherwise required in the absence of blockchain.

The immutable version of the transactions minimizes the corruption level, thus boosting the confidence level among the users.

 

2. Lightning Speed Transactions

The technology is responsible for effectively reducing the transaction time. It has been possible because it cuts and eliminates multiple intermediaries out of the process.

The result is a simplified transaction with little to zero intermediaries. Also, the trades are conducted with ledger entries which facilitate banks to instantly authorize and permit processes with the least time gap.

3. High-Security Standards

Lightning-speed transactions of blockchain significantly reduce the time span for hackers to divert or hack them. It also allows zero modification powers to the concerned parties, enhancing the transparency level for the users.

It has become feasible because blockchain stores data in a decentralized and encrypted manner over the entire network. It means as soon as the data gets stored over the network, a hacker cannot conduct any alteration to it. 

Any data altercation invalidates the signature, which enhances the security level.

4. Smart contracts improving data handling

Banks and other financial institutions hire app developers for developing smart contracts using blockchain. The technology allows the development team to ensure automatic data verification and quick execution of commands and processes.

It improves the data handling capacity of the developed software with high security and minimum human interference.

 

5. Offers High Accountability

Every transaction conducted online gets duplicated over the entire network. It automatically eliminates the risk of losing transaction details or loss of data. The user can conveniently trace any executed transaction.

So, banks find it very easy to trace and deal with any issues occurring with transactions. Finding the culprit becomes a matter of clicks in such a scenario.

 

6. Regarded as the future of banking software

As per AI development stats, 20+ nations worldwide have researched for developing a national cryptocurrency. With multiple countries formally and informally approving bitcoin trade, digital currencies are leading significant impacts in trade and commerce.

Blockchain is regarded among the most disruptive technologies and has become an integral part of banking software development worldwide. 

 

 7. Improves Efficiency 

As per CEO of IBM, Ginni Rometty, “Anything that can conceive of as a supply chain, blockchain can vastly improve its efficiency- it doesn’t matter if its people, numbers, data, money.”

Convenient tracking of fraudulence, quicker transactions, heavy security, etc., all together aids in developing a positive work culture environment for the bank employees. 

Blockchain improves the efficiency and reliability of the developed software and acts as a positive energy booster for the bank employees.

 

Final Words

That was about the top 7 benefits of using blockchain for banking software. For developing advanced banking apps, hire blockchain developers from India at an affordable hourly rate.

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Workplace Flexibility is the New Norm

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Web development trends for 2021 and the latest web technology stack

            

Standards in web development can change faster than they can implement. To stay one step ahead, it is essential to keep an eye on the prevailing trends, techniques, and approaches.

We’ve analyzed trends across the industry and put together a definitive list of web development trends for 2021. As a bonus, you’ll also read about the best web development stacks to watch out for next year. Whether your current interest is market development, startup innovation, or IoT invention, these are the trends you need to know about.

The hottest web technology trends to adopt in 2021

“Knowing what the next big trends are will help you know what you need to focus on.”

Mark Zuckerberg

When a billionaire with decades of experience in an industry tells us to do this, we can only agree. Here we’ve put together a list of the top trends to watch out for when growing your web business, so they’re easy to find, save you time, and help you grow your business in the new decade.

1| Voice search

We are currently experiencing the beginning of the era of voice search. Every smartphone equips with a digital voice assistant (Siri for iPhones, Google Assistant for Android devices). Intelligent speakers with artificial intelligence are also becoming more and more popular.

What are the reasons for the move to voice interfaces?

| Ease of use

Communication doesn’t have learners, which means that even children and the elderly can get to grips with voice interfaces without training.

| Accessibility

Digital voice assistants are already a common feature of smartphones. Smartphones are still not very popular, but pricing from $50 is a prerequisite for expansion.

The report states that “the use of voice assistants is reaching a critical mass. And by 2021, some 123 million U.S. citizens, or 37% of the total population, are expected to be using voice assistants.

| It’s good for your business.

Voice search is a big trend in ecommerce. But it applies to all businesses on the web too. If you want people to find your web app, optimize it for voice search as soon as possible.

Consider developing your smart speaker app and help you build a loyal audience, and give you another channel to generate sales.

2|  WebAssembly

When building web applications, performance inevitably suffers: heavy computation slows down due to the limitations of JavaScript, which has a significant impact on the user experience. For this reason, most popular games and powerful applications are only available as native desktop applications.

WebAssembly has emerged to change the game. This new format aims for native performance across web applications; WebAssembly can compile code from any programming language into bytecode that runs in the browser.

3| Content personalization through machine learning

Artificial intelligence, including machine learning, influences our daily online activities without us even realizing it, which is the essence of ML and the experience it provides in our native language.

Machine learning is the ability of software to improve its performance without the direct involvement of the developer. Essentially, the software analyses the input data, discover patterns, makes decisions and improves performance.

For example, Airbnb hired a machine to personalize guest search results to increase the likelihood that hosts would accept a request. A machine learning algorithm analyses each host’s decision to make a bid. In our A/B testing, we saw a 3.75% increase in conversions. A/B testing showed a 3.75% increase in conversions, and as a result, customer satisfaction and revenue increased as the algorithm applied to all Airbnb users.

Netflix’s engineers wanted to go even further. They used a more advanced ML-based algorithm to personalize content to meet the needs of users better predictively. Rather than targeting entire segments of users, each user identifies individually. The algorithm provides content and search results based on the user’s intent, not on previous queries.

A great example, but there are many more. You can improve the user experience by incorporating natural language processing and pattern recognition—machine perception, where a computer interprets data and makes decisions.

4|  Data security

The more data a web application processes, the more attractive it is to cybercriminals. They aim to ruin your services and steal your users’ data and your company’s internal information. Such actions can seriously damage a company’s reputation and cause significant losses.

The security of web services should be a top priority. So here are four things you can do to keep your user data safe in 2021.

| Don’t neglect security testing

Security testing should be carried out during the development phase to prevent data breaches. Should test each change to your web app should be tested explicitly.

| Use a website monitoring tool.

Website monitoring tools allow you to monitor all requests and detect and identify suspicious activity constantly. Timely notifications enable your team to react immediately and protect your web app.

| Choose third-party services carefully.

SaaS software is becoming increasingly popular as it makes app development more accessible and quicker. However, it would be best to make sure that the service provider you work with is reliable.

| Encryption of sensitive data

Even if criminals have access to your database, they won’t be able to extract any use-value from the sensitive data stored there.

Along with these tips, here are the latest trends for the web in 2021 to help you keep your apps and data safe. Must address two essential elements must be addressed here.

| A.I. for cybersecurity

Machines are becoming more intelligent. There are both good and bad sides to this fact, but we will focus on the benefits A.I. brings in this report.

In 2021, we expected A.I. technology to become even more helpful in terms of data security. We have already had the opportunity to look after some of the latest improvements. AI-powered biometric logins that scan fingerprints and retinas are more than just an element of science fiction. The web systems of many powerful companies demonstrate these capabilities.

80% of telecom company founders say they rely on A.I. for cybersecurity.

Threats and malicious activity can be easily detected by AI-powered security software. The more types of malware there are, the more powerful and dangerous they become. That’s why large companies are now training A.I. systems to analyze behavioral patterns in their networks and respond immediately to any suspicious activity.

“Artificial intelligence is an advisor. It’s like a trusted advisor that you can ask questions to. Artificial intelligence learns as it goes. The cognitive system starts learning as we teach it, but it’s there and it doesn’t forget what it’s been taught, so it can use that knowledge to make more accurate decisions when it’s looking at what’s happening from a (security) threat perspective,” said IBM Security Vice President, Kevin Skapinetz, explains how A.I. can influence security systems and save companies from potential threats.

| Blockchain for cybersecurity

Over the last few years, Bitcoin and other blockchain-related topics have dominated tech blogs and reports, and in 2021 we recommend taking a closer look at this tool for the security of web solutions.

NASA has implemented blockchain technology to protect its data and prevent cyber attacks on its services. The starting point is that if influential leaders are using this trend to protect their entities, why can they ignore this principle?

| Database security

Storing all your data in one place makes it perfectly convenient for hackers to steal it. Blockchain is a decentralized database, which means no single authority or location for storing data. All users are responsible for validating the data and can make no changes of any kind without everyone’s approval.

| Protected DNS

DDoS attacks plague large companies. However, there is a cure: a fully decentralized DNS. When content distributes across many nodes, it becomes almost impossible for an attacker to find and use sensitive points or attack a domain.

Security trends in web development

| Making it work for your business

No matter what kind of web app you plan to launch, its security is the number one thing you need to focus on. Look at the most effective approaches and make sure your development team is proficient in security questions and has the skills to keep your critical data safe.

5|  Progressive Web Apps (PWAs) and Accelerated Mobile Pages (AMPs)

Google prioritizes web apps that load quickly on mobile devices. For this reason, you should consider implementing PWAs and AMPs, which are proprietary technologies that reduce the loading time of web pages.

Progressive web apps (PWAs) are web pages that replicate the native mobile experience. PWAs are fast, work online and even with poor internet connections, and are relatively inexpensive. PWAs support interaction, so users are not aware that they are using a browser. E-commerce web applications are an everyday use case for this technology.

AMP (Accelerated Mobile Page) only supports static content, but it loads faster than regular HTML. AMP omits all decorative elements and displays only the necessary information, such as text and images. This approach is ideal for blogs and news publishers.

Whether you should use a PWA or AMP depends on your case. However, it would be best if you started considering these technologies now. You have the opportunity to dramatically improve your search result rankings while providing a high-end experience.

We use so many great PWAs that we don’t need to mention them: some, like Twitter, have over 330 million active users every month, while others are on the verge of spectacular success. If you’re planning to build a simple web game, a lifestyle or sports app, an entertainment app, or a news site, you should consider a PWA approach.

AMP is an excellent idea if

• Most of the users of your web app are accessing it on a mobile device.
• The page loads slowly, and users leave the site quickly.
• You are putting a lot of effort into SEO and app promotion.

6| . Multi-experience

The story of app development started with smartphones, tablets, and laptops. Today, they are so prevalent that it’s hard to imagine a day without a smartphone. More recently, other intelligent devices, cars, smartwatches, and components of IoT systems have gained remarkable popularity. Mobile-friendly apps are a must. However, a fresh and vivid trend is emerging in app development. Multiple experiences are welcome. The idea is to allow users to use your app wherever they want – on their tablet, smartwatch, car, etc. The point is to create apps that look good, work well, and bring value in an equally engaging and helpful way on all devices.

Multi-experience is the new trend in web development.

The trend for 2021 is to make web applications compatible with all screens. According to Gartner, it’s one of the top trends in technology. The traditional idea of using laptops and smartphones to interact with software applications is drifting towards a multi-sensory, multi-touch, multi-screen, multi-device experience. Customers expect apps with great intelligent chatbots, voice assistants, AR/VR modules, etc., to be available on all devices. For web businesses that want to be successful in 2021, this multi-channel approach to human-machine interaction in web apps will be critical.

“This ability to communicate with users across the many human senses provides a richer environment for delivering nuanced information,” said Brian Burke, Research Vice President at Gartner.

5G technology and edge computing will stimulate the development of multi-experience trends. A new generation of wireless technologies will increase transmission speeds and open up opportunities to deliver superior AR/VR experiences. I.T. companies worldwide are poised to provide seamless experiences on mobile and web extensions, wearables, and conversational devices.

7| Motion U.I.

Motion design is one of the key trends in web design for the coming year. Minimalist design combined with sophisticated interaction looks tremendous and grabs the user’s attention.

Think page header transitions, nice hovers, animated charts, animated backgrounds, and modular scrolling. These and many other elements will display your unique style, entertain users, improve behavioral factors and help your web app rank higher in search results.

Use for business

To increase user engagement and provide a better UI/UX for your web app, upgrade it with Motion U.I. technology.

Guide users through your app with animations that show the next steps.

React to the user’s gestures with catchy animations.

Show the relationship between the different components of your app, for example.

Conclusion

Fashions change so quickly that it can be challenging to keep up with them. But why not give it a go?

By keeping up with the latest trends in web development, you can delight your users with a world-class experience, improve your web app’s ranking and open up new markets for your services.

Over the next few years, voice search will strengthen its position, and service providers will adapt to the new reality. By approaching it smartly, you can be the first company to win customers with voice search. Sounds good, right?

The security of user data has been an issue for quite some time now. If you want to become a market leader, you can’t ignore this issue.

By offering a multi-faceted experience to your web app users, you increase your chances of being chosen first by them. So be in touch with top web development companies in India to have a world-class experience.

Source Prolead brokers usa