How a Data Catalog Enables Data Democratization

For many organizations, data is a business asset that’s owned by the IT department. Based on this ‘data ownership’ model, there’s limited access to data across the organization, and no transparency around what’s available internally.

Now, an increasing number of organizations are rushing to mandate organization-wide data strategies. But with the isolated data ownership model in place, how can organizations ensure that everyone who needs data can find it and use it? 

Although data scientists and analysts are the ones closest to data, all business units now need data (and data know-how) in order to achieve digital transformation goals. That means data access and sharing needs to be a priority.

The 2021 New Vantage Partners Big Data and AI Executive Survey reports that more than three quarters of these executives said they haven’t created a data culture. Furthermore, only about 40% said they are treating data as a business asset and that their enterprises are competing on data and analytics.

It’s clear that data is top of mind for most companies, but it’s still a separate idea rather than the foundation of every decision. Across business units, and even within individual departments, there are still barriers, silos, and isolated workflows that slow progress. To drive a data-first culture, data has to be the central point that every department and decision can be built on – and that’s achievable through data democratization. 

What is data democratization?

Data democratization makes data accessible within your organization so that everyone, not just your IT department, has access to data. Bernard Marr, bestselling author of “Big Data in Practice,” puts it clearly: “It means that everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data. The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.”

Data democratization lays the roadways for people to find, understand, and use data within their organization to make data-driven decisions.

The internal resistance against data sharing

Data is a commodity that can unlock new opportunities, so it makes sense that better access means better results. However, there’s a lot of data that contains sensitive information, meaning that easier access to the data breeds security risks. This threat has led to all data being treated more or less the same way – closed by default, access being granted on a case-by-case, need-to-have basis.

This does make sense; data privacy should be a priority. But often, useful data that should be more available gets lumped in with sensitive information. In fact, Forrester reported that within enterprise companies, 73% of data still goes unused. Where else in your business would you be okay running at 27% capacity?

Another obstacle in the way of data democracy is the assumption that data isn’t used the same way, so it therefore can’t be shared. Every department uses data for different purposes, so it makes some sense to assume it can’t be shared among business units. That leads to the same data being purchased in several different places, or duplicative work in building connectors and data prep & cleansing. Either way, you’re probably spending money on the same problem more than once.

For example, we can assume that every division of a given bank tracks economic indicators. But without wider data asset visibility, every division manages that data separately. This means multiple purchases of the same data, redundant work, lower efficiency, and an erosion of your bottom line.

There are a lot of tools that will transform, warehouse, ingest, or present data – all of the things you need to do, right? But no matter the toolset, if there’s no cultural alignment among data initiatives, there’s no chance of reaping the full reward. Cost savings, new revenue, and improved decision-making truly take off when data is considered as part of the design and process, not a separate, black-box add-on. 

How a data catalog enables data democratization

The first step towards data democratization is to break down data silos. That sounds like a tall order, but the right tools can help.

A data catalog is a popular solution, as it allows organizations to centralize their data, then find, manage, and monitor data assets from one single point of access. Gartner reported that organizations with a curated catalog of internally and externally prepared data will realize double the business value coming from analytics investments this year.

Key advantages to data catalogs are that they allow users to discover all the data your organization has access to, no matter the source or which department acquired it, but under strict governance rules. Additionally, they can also offer data virtualization capabilities so that users can retrieve data without moving it from its existing warehouse. This is a major advantage for data privacy and residency requirements.

There’s a variety of data catalog solutions available in the market. Ensure the solution you choose provides a foundation for data governance and data sharing. By 2023, organizations promoting data sharing will outperform their peers on most business value metrics.

How ThinkData enables data democratization

ThinkData’s catalog solution offers visibility and governance of any data assets from any source. Once data is accessible from a centralized location, you need to think about how your organization will be able to share and connect to data with integrity.

Since the GDPR was launched in 2018, over €274M (over $350M) in fines have been passed down – and 92% of these fines were the result of insufficient legal basis for data processing and security. The ThinkData catalog is built to help you comply with GPDR, CCPA, and other regulatory requirements. What’s more, the platform is flexible enough to adapt to changing market conditions. 

We surveyed data science experts in our ThinkData State of Data 2021. What we found is that 86% of data scientists name role-based access control an important or very important feature when managing datasets. The ThinkData catalog enables effective data management through role-based access control and data forensics activities. Using Namara, users can grant or restrict data access; track datasets across updates; and audit data access, dataset health and more.

We also offer a seamless connection to the world’s largest repository of open, public, and partner data on our Data Marketplace. This provides access to trusted, product-ready datasets and increases speed of discovery, shortens time to insights, and bolsters data confidence (note: if you want to offer data on our Marketplace, send us a message).

Forging a data culture isn’t easy. The right training and executive support need to be in place across your business functions, not just your technical teams. That common goal and framework will foster cultural alignment in centring operations around a data catalog.

Data should be used by more and more people, not just data scientists, and most companies are working towards this goal. But if there’s a cultural mismatch between tools and training, it will be an uphill battle trying to meet your goals. We offer introductory data governance assessments that will help you discover the blind-spots in your current strategy. 

Democratizing data internally improves data access, reduces overhead costs, and promotes confidence and consistency. In an incredibly unpredictable environment, it’s no surprise that organizations are racing to implement a data-first approach to safeguard and optimize their operations. Promoting intra-organizational transparency will enrich your company’s understanding, capabilities, and resilience, and it’s never been more important.

Do you think your business has a need for a data catalog to find, understand, and use trusted data to drive business outcomes? Please reach out.

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Originally published at https://blog.thinkdataworks.com.

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Finger On The PULSE: Using AI To Turn Blurry Images Into Hi-Res CGI Faces

For all the photographers out there who haven’t mastered the art of the steady hand: this one’s for you. Researchers at Duke University in North Carolina have applied an AI-based solution to touching up blurry photographs, creating a program capable of touching up blurry faces into an image sixty times sharper. It’s not going to turn you into an artist, but it could work wonder on your holiday snaps!

The Duke team’s system is called PULSE, standing for Photo Upsampling via Latent Space Exploration. The system creates entirely new high-resolution computer-generated images based on the blurry sample photograph, and then scours then scales the new images down, working backwards to finding the closest matches to the original low-res sample.

PULSE is actually made up of two neural networks, a machine learning tool called a GAN, or a generative adversarial network. When combined, these two networks produce lifelike high-resolution output images. The first AI generates new human faces, initially using its own programming but learning from feedback from the second network to improve its processes. The second network analyses the output images to “decide” if they’re convincing enough, feeding its analysis back into the first network to improve the process.

Traditional upscaling tools have assumed that there’s one true upscaled image based on any low-resolution (LR) input and have then worked linearly to add detail to an input image, slowly improving its resolution. But this assumption about a “true image” requires the program to smooth out any details that it can’t guess at. Typically, the image produced is around eight times sharper than its original, but details such as wrinkles, hairs and pores are absent, as the computer doesn’t want to guess at them. Output images suffer from a lack of texture, and look uncanny, like a too thoroughly airbrushed model – somewhere between human and CGI.

PULSE throws this assumption out the window, as any LR image could actually have hundreds of corresponding high-resolution outputs. By recognizing this, says Larry Gomez, a business writer at Revieweal and Assignment Help, “the team that worked on PULSE knew they could produce a vastly sharper image, where no details are smoothed out. The generative adversarial network works backwards, producing images and then shrinking them to see if they closely match the input image, all the while reappraising its process through machine learning to get better at both the image production process and the matching process.”

(PULSE Authors Example)

When the results from PULSE were tested against the images generated from other models, the PULSE images scored the best, approaching the scores given to real hi-res photographs. The algorithm can take an image of just 16×16 pixels and produce a realistic output image of 1024×1024 pixels. Even images where features are barely recognizable, eyes or mouths reduced to single pixels, can be used to produce life-like outputs.

The team at Duke focused on images of faces, but this was just a proof of concept for PULSE. “In theory, the process can be applied to blurry images of anything. This could have practical applications beyond your family photo album, in disciplines such as medicine and astronomy where blurry images are the norm. Sharp, realistic images could be generated to help us guess at what we’re looking at, whether that’s in the human body or deep space,” says Julia McAdams, a marketer at Coursework help service and State Of Writing.

One thing the team are keen to emphasize that PULSE can’t do is upgrade existing blurred images – say, blurred out photographs to protect individuals’ identities – and produce the original image. The output images produced by PULSE are computer generated – they look plausibly real, but aren’t actually images of anyone who exists. This dystopian model for unmasking protected identities doesn’t exist: indeed, the PULSE team say it’s impossible.

The power of AI and machine learning continues to be harnessed to create self-learning algorithms with impressive power to produce lifelike results. PULSE’s upscale tool is only one possible application of generative adversarial networks. It has been used in other areas to have algorithms produce their own video games. Smart algorithms will be infiltrating all walks of life in a few years, with impressive results.

Lauren Groff is a writer and editor at Lia Help and Big Assignments with a passion for AI and computer science. More of her writing can be found at Top essay writing services blog.

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A Guide to Amazon Web Services (AWS) – Scalable & Secure Cloud Services

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A Complete Guide to Amazon Web Services (AWS) – Scalable & Secure Cloud Services

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Unusual Opportunities for AI, Machine Learning, and Data Scientists

Here some off-the-beaten-path options to consider, when looking for a first job, a new job or extra income by leveraging your machine learning experience. Many were offers that came to my mailbox at some point in the last 10 years, mostly from people looking at my LinkedIn profile. Thus the importance of growing your network and visibility, write blogs,  and show to the world some of your portfolio and accomplishments (code that you posted on GitHub, etc.) If you do it right, after a while, you will never have to apply for a job ever again: hiring managers and other opportunities will come to you, rather than the other way around.

1. For beginners

Participating in Kaggle and other competitions. Being a teacher for one of the many online teaching companies or data camps, such as Coursera. Writing, self-publishing, and selling your own books: an example is Jason Brownlee (see here) who found his niche by selling tutorials explaining data science in simple words, to software engineers. I am moving in the same direction as well, see here. Another option is to develop an API, for instance to offer trading signals (buy / sell) to investors, who pay a fee to subscribe to your service – one thing I did in the past and it earned me a little bit of income, more than I had expected. I also created a website where recruiters can post data science job ads for a fee: it still exists (see here) thought it was acquired; you need to aggregate jobs from multiple websites, build a large mailing list of data scientists, and charge a fee only for featured jobs. Many of these ideas require that you promote your services for free, using social media: this is the hard part. A starting point is to create and grow your own groups on social networks. All this can be one while having a full-time job at the same time. 

You can also become a contributor/writer for various news outlets, though initially you may have to do it for free. But as you gain experience and notoriety, it can become a full time, lucrative job. And finally, raising money with a partner to start your own company. 

2. For mid-career and seasoned professionals

You can offer consulting services, especially to your former employers to begin with. Here are some unusual opportunities I was offered. I did not accept all of them, but I was still able to maintain a full time job while getting decent side income.

• Expert witness – get paid by big law firms to show up in court and help them win big money for their clients (and for themselves, and you along the way.) Or you can work for a company specializing in statistical litigation, such as this one.
• Become a part-time, independent recruiter. Some machine learning recruiters are former machine learning experts. You can still keep your full-time job.
• Get involved in patent reviews (pertaining to machine learning problems that you know very well.)
• Help Venture Capital companies do their due diligence on startups they could potentially fund, or help them find new startups worthy to invest in. The last VC firm that contacted me offered $1,000 per report, each requiring 2-3 hours of work. 
• I was once contacted to be the data scientist for an Indian Tribe. Other unusual job offers came from the adult industry (fighting advertising fraud on their websites, they needed an expert) and even working for the casino industry. I eventually created my own very unique lottery system, see here.  I plan to either sell the intellectual property or work with some existing lottery companies (governments or casinos) to make it happen and monetize it. If you own some IP (intellectual property) think about monetizing it if you can. 

There are of course plenty of other opportunities, such as working for a consulting firm or governments to uncover tax fraudsters via data mining techniques, just to give an example. Another idea is to obtain a realtor certification if you own properties, to save a lot of money by selling yourself without using a third party. And use your analytic acumen to buy cheap and sell high at the right times. And working from home in (say) Nevada, for an employer in the Bay Area, can also save you a lot of money. 

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.

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The Megatrends Driving Demand for Datacenter Compute

The field of information technology loves to prove that the only constant is change. What intensifies the truth of this axiom is that we are living in the greatest period of technological change that humans have experienced so far. Any one of the several megatrends that are occurring simultaneously could be viewed independently as one of the biggest changes in history. 

Together, they are radically changing the way people communicate – and the way networks are built to support them. These trends include 5G, cloud and edge computing, and the IoT (Internet of Things.) While each of these is unique in its own way, one thing they share is the need for a new class of network for the communications infrastructure that underpins the vision.  

 

The rise of 5G 

 

5G is widely considered the fastest–growing mobile technology in history. According to Omdia, telecom operators added 225 million 5G subscribers between Q3 2019 and Q3 2020. As of December 2020, there were 229 million 5G subscriptions globally – a 66% increase over the prior quarter.  

With the goal of unlocking unimaginable new services based on greater bandwidth and lower latency, 5G telecommunications operators are building new datacenters and expanding the number of access points to a massive scale.   

 

Cloud and edge computing 

 

Industry experts expect increased investments in edge capacity to reduce latency and support personalized content delivery and custom security policies. Cloud and edge computing datacenter operators continue to expand to hyper scale, delivering more intelligent, localized, and autonomous computing resources for an increasing set of users, and applications.

   

IoT  

The IoT continues to add more “things” as innovators discover its possibilities. This includes the invention of devices and sensors spanning personal use, entertainment and quality of life, to factory automation, safety, connected cities, smart power grids, new applications of machine learning and artificial intelligence, and so much more.  

New network requirements 

The possibilities that these trends engender necessitate new network designs – and there’s too much at stake for failure to be an option. Datacenter designers and operators have emerged with a clear set of networking requirements that cannot be compromised:  

 

Security: A new distributed architecture is essential in light of so many users, devices and applications able to enter the network in many configurations. 

 

Performance: Line-rate networking with ultra-low latency that scales from 25 gigabit Ethernet in the servers to 100 gigabit Ethernet and beyond in the network links that connect them is paramount. 

 

Agility: In a world of software-defined-everything, the network must provide hardware performance at the speed of software innovation. This equates to programmability that allows the hardware to evolve as fast as new networking protocols and standards do, as well as the threat landscape driven by bad actors.  

 

Orchestration: New methods for automation, configuration and control are needed to orchestrate and manage so many elements at this scale. 

 

Economics: A fundamental requirement for minimal costs shifts the design of networks towards open, standard and commodity off-the-shelf products. Cost at this incredible scale cannot be forgotten. 

 

Efficiency: The central processing units (CPUs), the most valuable and costly resources within the datacenter severs, spend most of their time supporting the applications, services and revenue they were intended for, offloading all burdensome workloads such as networking, data and security processing. 

 

Sustainability: This last piece ensures that the network operates as required, but in an environmentally friendly size and power configuration. 

 

How programmable SmartNICs fit in 

 

Due to this new set of non-negotiable requirements, networks are moving away from large, proprietary, expensive, monolithic, vertically integrated systems – and a new method of software-defined networking has emerged.  

To address the problems created by the decline of Moore’s Law, a new heterogeneous processing architecture exists where expensive and burdensome workloads are offloaded from the CPUs. This model has proven to be successful in the past, with GPUs offloading video and graphics processing from the CPU. This same model is now being applied for data, network, and security processing. However, the applications underpinned by 5G, cloud, edge computing and IoT often fail on standard servers with basic NICs.  

As Moore’s Law ends and other megatrends skyrocket, organizations prefer SmartNICs as the method to avoid these problems and help advance datacenter computing. A SmartNIC’s primary function is to operate as a co-processor inside of the server, offloading the CPU from the burdensome tasks of network and security processing, while simultaneously accelerating applications on multiple dimensions. It uniquely meets the network design requirements for  performance, agility, efficiency, security, economics, orchestration and sustainability.  

 

Equipped for the future 

 

5G, cloud and edge computing, and the IoT are radically changing the way people communicate. And that means that these megatrends are changing the way networks are built to support them. Organizations need a new class of network with stringent requirements for their communications infrastructure. SmartNICs enable this network architecture, resulting in high performance at a reduced cost – along with meeting all the other needed requirements. They form a critical element of the future-forward infrastructure organizations need to capitalize on today’s massive technological shifts. 

 

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Why many financial services professionals use alternative data every day (and the rest wish they could)

It’s no secret that when you seek trustworthy information on which to base your business and investment strategy, traditional and internal data sources, such as quarterly earnings reports, are no longer enough. This is especially the case when it comes to the financial services world. These stalwarts of a previous era can’t keep up with the current 24/7 business cycle that demands real-time actionable insights. This is where alternative or external data sources come into play. They allow companies to utilize online data from a variety of sources that are only limited by one single factor: the organization’s creativity to integrate them.

 

Alternative data is not quite living up to its name anymore – it has hit the mainstream. New survey data we’ve commissioned with Vanson Bourne supports this, with 24 percent of financial services professionals using alternative data every day. These organizations have recognized that external information sources are too vital to neglect when building an agile, comprehensive, and winning business or investment strategy.  The certainty that they provide is just too undeniable to ignore.

 

It’s not all smooth sailing

 

Using alternative data sources is always going to be a subjective endeavor tailored to the specific needs of that organization or business; there is no right or wrong path. Whether it’s looking at a prospective investment, measuring growth by monitoring employee counts on LinkedIn, or tracking trade shipments to predict which way revenue is heading, relying on online data or any external data is no longer a nice-to-have element but a business or market necessity.

 

These data sources can help lead you to many potential investment routes or to verify whether your current routes will actually produce the desired results. The past year has made this growing realization a well-known fact. The aforementioned study illustrates it with 80 percent of those surveyed expressing that they require more competitive insights from alternative data, and 79 percent hoping to get information on customer behavior from the data.

 

When the need is so obviously stated, what are the challenges stopping it from being widely incorporated into all organizations? Let’s start with the fact that there’s no single playbook on how to gain actionable insights from these data sources in the most effective way. With 61 percent of financial services professionals citing data analysis as a major bottleneck for integrating the data into operations, this is clearly indicating an operational challenge to overcome.

 

This challenge is also felt across related industries. Three-quarters of banking professionals surveyed felt that data analysis was the biggest challenge they faced when it came to alternative data. Meanwhile, hedge funds and insurance company employees found it less challenging on average and are gaining the required insights on the go.

 

What generates this gap in the need for alternative data consumption and the actual integration and utilization?  The reasons lie within the organization’s readiness as well as assigned professional resources to look after the entire data sourcing process. In addition, the source of the challenge often goes back to a strict compliance process and demands that inhibit the inclusion of alternative data.

 

When it comes to insurance companies and hedge funds, the business cases are clearly defined. In the insurance industry, this could mean integrating alternative data – such as crime reports in a target area – into the calculations used to set rates. For hedge funds, it could be using the real-time data to make on-the-spot trading decisions on a frequent basis.

 

Banks and banking services are known for their more traditional approach and security concerns. However, there are multiple ways alternative data can directly and successfully serve banks’ growing business cases.  For example, banks could use alternative data to determine how to market products in different regions or to decide what products to develop to begin with. However, these require a bit more ingenuity when it comes to seeking out data sources and gaining meaningful insights from them.

 

Traditional banks also tend to be much slower at adopting new technology than some of their financial services counterparts; they require a more “sensitive touch” to their overall compliance needs – needs that can be met with the right kind of ethical data provider. It’s therefore likely that their process for integrating the technology and talent needed to get the best insights is a more intensive and less agile process than it would be at a hedge fund.

 

What all of this means is that there’s still a massive opportunity for financial services firms to leverage alternative data more effectively. And those that put the resources behind doing it well, will undoubtedly gain a more profound competitive advantage.

 

It all comes down to strategy and sensitive understanding

 

Alternative data purely works for the benefit of all financial markets – this fact has been established. It often removes the guesswork from immediate decision-making, which should be the goal of any data initiative. Relying on outdated data or wrong data is frankly a recipe for disaster. This is what has led 64 percent of organizations that use alternative data to leverage it for investing decisions and 59 percent to use it to inform customer service strategies.

 

In the end, it all boils down to whether you have the following: the right, trustworthy operation that is sensitive to your security requirements, the professional talent to support your competitive strategy with data-driven creativity, and, last but not least, the ability to translate that huge amount of data into winning results! Whether you are a small organization starting out on your data-driven path or a large global bank, your data must set you apart from – and ahead of – all others.

I

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The Pros and Cons of RDF-Star and Sparql-Star

For regular readers of the (lately somewhat irregularly published) The Cagle Report, I’ve finally managed to get my feet underneath me at Data Science Central, and am gearing up with a number of new initiatives, including a video interview program that I’m getting underway as soon as I can get the last of the physical infrastructure (primarily some lighting and a decent green screen) in place. If you are interested in being interviewed for The Cagle Report on DSC, please let me know by dropping a line to editor@techtarget.com.

I recently purchased a new laptop one with enough speed and space to let me do any number of projects that my nearly four-year-old workhorse was just not equipped to handle. One of those projects was to start going through the dominant triple stores and explore them in greater depth as part of a general evaluation I hope to complete later in the year. The latest Ontotext GraphDB (9.7.0) had been on my list for a while, and I was generally surprised and pleased by what I found there, especially as I’d worked with older versions of GraphDB and found it useful but not quite there.

There were four features in particular that I was overjoyed to see:

1. the use of RDF* and SPARQL* (also known as RDF-STAR, etc. for easier searchability),
2. the implementation of the SHACL specification for validation
3. Implementation of the SERVICE protocol for federation
4. and the adoption of a GraphQL interface.

These four items have become what I consider essential technologies for a W3C stack triple store to fully implement. I’d additionally like to see some consensus around a property path-based equivalent to Gremlin (RDF* and SPARQL* is a starting point), but right now the industry is still divided with respect to that.

There are a couple of other features that I find quite useful in GraphDB. One of the first is something I’ve been advocating for a long time – if you load in triples in Turtle, you are also providing the prefix to namespace associations to the system, but all too often these get thrown out or mapped to some other, less useful term (typically, p1: to pN). Namespaces do have some intrinsic meaning, and prefixes can, within controlled circumstances, do a lot of the heavy lifting of that meaning. When I make a SPARQL query, I don’t want to have to re-declare these prefixes. OntoText automatically does that for you, transparently.

The other aspect of OntoText that I’ve always liked is that they realized early on that visualizations were important – they communicate information about seemingly complex spaces that tables simply can’t, they make it easier to visually spot anomalies, and when you’re talking to a client you don’t want to spend a lot of time building up yet another visualization layer. OntoText has worked closely with Linkurious to develop Ogma.js, a visualization library that is used extensively within their own product but is also available as an open-source javascript library. I still rely fairly heavily on vizjs.org and d3.js) as well as working with the DOT and related libraries out of GraphViz, but having something that’s available “out of the box” is a big advantage when trying to explain complex graph math to non-ontologists.

I’ll be doing a series exploring these points as part of this newsletter, with Ontotext being my testbed for putting together examples. In this particular issue, I’d like to talk about RDF* and SPARQL*, and why these will help bridge the gap between knowledge graphs and property graphs.

Is an Edge a Node?

This seemingly simple question is what differentiates a property graph from an RDF graph. In RDF, an edge is considered to be an abstract connection between two concepts. For instance, consider the marriage between two people: person:_Jane and person:_Mark. In a (very) simple ontology, this distinction can be summarized in one statement:

# Turtle

person:_Jane   person:isMarriedTo   person:_Mark.

If you are describing the state of the world as it currently exists, this is perfectly sufficient. The edge `person:isMarriedTo` is abstract, and can be thought of as a function. Indeed, Manchester notation, used in early OWL documentation, made this very clear by turning the edge into a function:

person:isMarriedTo(person:_Jane, person:_Mark)

What’s most interesting about this is that while `person:isMarried` itself is abstract (you can think of it as a function with two parameters), the resulting object is NOT in fact a person, but a marriage instance:

# Here’s the function template

marriage: <= person:isMarriedTo(?person1,?person2)

# Here’s an example: 

marriage:_JaneToMark <= person:isMarriedTo(person:_Jane,person:_Mark)

 

 

If at some time Jane divorces Mark and marries Harry, then you end up with another marriage that uses the same template:

marriage:_JaneToHarry <= person:isMarriedTo(person:_Jane,person:_Harry)

These are two separate marriages, two separate instances, and they are clearly not the same marriage.

In an RDF graph, this kind of association typically needed to be described by what amounted to a third form normalization:

marriage:_JaneToMark

     a class:_Marriage;

     marriage:hasSpouse person:_Jane;

     marriage:hasSpouse person:_Mark;

     .

There are advantages to this approach: first, you have a clear declaration of type (Marriage), and you essentially invert the marriage function. Additionally, you can add more assertions that help qualify this node:

marriage:_JaneToMark

    marriage:hasStartDate “2012-05-15″^^xsd:date;

    marriage:hasEndDate “2018-03-02″^^xsd:date;

    marriage:officiatedBy person:_Deacon_Deacon; 

    .

You can also describe other relationships by referencing the created object:

person:hasParents marriage:_JaneToMark;

which makes it possible to ask questions like Who were the children of Jane?

# Sparql

select ?child where {

    ?child person:hasParents ?marriage.

    ?marriage marriage:hasSpouse ?parent.

    values ?parent {person:_Jane}

    }

If you are building a genealogy database, this level of complexity and abstraction is useful, but it does require that you explicitly model these entities and that you then expect that there will be a large number (this is where combinatorics and factorials begin to kick in creating geometric growth in databases).

A property graph short-circuits this by specifying that an edge is a concrete node that can take attributes but not relationships. In effect, the property edge has both the abstract relationship description (`person:isMarriedTo`) and the relationships that exist between the nodes that are bound to this assertion or function. However, this benefit comes from the fact that property graphs essentially have nodes that are inaccessible to other nodes. This tends to make property graphs more optimized for performance, at the cost of making them less versatile for modeling or inferencing.

In some cases it makes sense to explicitly label these edge relational models, but sometimes you just don’t have that luxury: for instance, what happens when you get data in the form

person:_Jane   person:isMarriedTo   person:_Mark.

You can take a shortcut here by creating a reification, a very fancy word that means creating a reference to a triple as a single assertion. Reifications actually were in place why back in the first RDF proposal, and look something like this:

marriage:_JaneToMark

     rdf:subject person:_Jane;

     rdf:predicate person:isMarriedTo;

     rdf:object prerson:_Mark;

     .

Introducing RDF* Statements

The problem with this is that it is fairly unwieldy. RDF* and SPARQL* were proposals that would provide a syntax that would do much the same thing in Turtle and SPARQL respectively but without the verbosity. The syntax was to wrap double angle brackets around a given triple to indicate that it should be treated as a reification:

<<person:_Jane  person:isMarriedTo  person:_Mark>>

    a class:_Marriage;

    marriage:hasStartDate “2012-05-15″^^xsd:date;

    marriage:hasEndDate “2018-03-02″^^xsd:date;

    marriage:officiatedBy person:_Deacon_Deacon;

    .   

In effect, this creates a uniquely identifiable URI that represents a triple. It’s worth noting that this doesn’t affect any of the components of that triple. For instance, I can still say:

person:_Jane

    a class:_Person;

    person:hasFullName “Jane Elizabeth Doe”^^xsd:string;

    person:hasBirthDate “1989-11-21″^^xsd:date;

    .

Reification in the RDF (in either guise) can somewhat simplify the generation of RDF, but the more powerful benefits come when you provide support in SPARQL for the same concept.

For instance, SPARQL* supports three functions rdf:subject,rdf:predicate and rdf:object. This comes in handy for doing things like finding out whom Jane Doe was married to on January 1, 2015.

select ?spouse where {

    VALUES (?personOfInterest ?targetDate)  {

        (person:_Jane   “2015-01-01″^^xsd:date)

        }

    ?marriage a class:_Marriage.

    bind(rdf:subject(?marriage) as ?spouse1)

    bind(rdf:object(?marriage) as ?spouse2)

    filter(?personOfInterest IN (?spouse1,?spouse2)}

    ?marriage marriage:hasStartDate ?startDate.

 

    bind(if(?sameTerm(spouse1,?personOfInterest),?spouse2,?spouse1) as ?spouse)

 

   OPTIONAL {?marriage hasEndDate ?endDate}

   filter(?targetDate >= ?startDate AND ?endDate > ?targetDate)

   }

 

This example is more complicated than it should be only because person:isMarriedTo is symmetric. What’s worth noting, however, is that, from SPARQL’s standpoint, the reified value in ?marriage is a node just like any other node. If the predicate hadn’t been symmetric, the expression:

    bind(rdf:subject(?marriage) as ?spouse1)

    bind(rdf:object(?marriage) as ?spouse2)

    filter(?personOfInterest IN (?spouse1,?spouse2)}

    ?marriage marriage:hasStartDate ?startDate.   

 

could have been replaced with

    ?marriage rdf:subject ?personOfInterest.

    ?marriage rdf:object ?spouse.

 

There are two additional functions that SPARQL* supports: rdf:isTriple() and rdf:Statement. The first takes a node and tests whether it has the rdf:subject,rdf:predicate and rdf:object predicates defined for the entity, while the subject creates a triple statement from the corresponding node URIs:

select ?isTriple where {

      values ?tripleCandidate {

           rdf:Statement(person:_Jane,person:isMarriedTo,person:_Mark)}

      bind(rdf:isTriple(?tripleCandidate) as ?isTriple)

  }

# Same as: 

select ?isTriple where {

      values ?tripleCandidate        

           {rdf:Statement(person:_Jane,person:isMarriedTo,person:_Mark)}

      bind(if(rdf:subject(?tripleCandidate) and

             rdf:predicate(?tripleCandidate) and

             rdf:object(?tripleCandidate),true(),false())

             as ?isTriple) 

  }

 

 

It’s also worth noting that if any of the three components are null values, then the rdf:Statement() function will also return a null value and the isTriple() function will return false().

RDF* Duplications

So when would you use reification? Surprisingly, not quite as many as you’d think. One seeming obvious use case comes when you want to annotate a relationship without creating a formal class to support the annotation. For instance let’s say that you have a particular price associated with a given product, and that price changes regularly even if the rest of the product does not. You can annotate that price change over time as follows:

#Turtle

<<product:myLittleGraphSet product:hasPrice “21.95”^^currency:_USD>>

     annot:hasLastUpdate “2021-05-01T05:15:00Z”^^xsd:date;

     annot:hasApprovalBy  jane@bigCo.com;

     annot:isActive “true”^^xsd:Boolean;

     annot:hasPreviousPriceStatement

        <<product:myLittleGraphSet product:hasPrice “25.25”^^currency:_USD>>

     .

 

 

This annotational approach lets you track the price evolution of a given product over time, and also provides a way of indicating whether the current assertion is active. Of course, if the price changes away from “21.95” then changes back, you suddenly end up with multiple hasLastUpdate, hasApprovalBy and isActive assertions – unless the new reification has a different URI than the old one does.

This can lead to some unexpected (though consistent) results. For instance, if you create a sequence in SPARQL, such as:

select ?lastUpdated where {

    <<product:myLittleGraphSet product:hasPrice “21.95”^^currency:_USD>>

          annot:hasLastUpdate ?lastUpdated

    }

 

it’s entirely possible that you will get multiple values returned.

This actually gets to a deeper issue in RDF, which is the fact that reifications can create a large number of seemingly duplicate triples that aren’t actually duplicates, especially if reification was an automatic capability of creating triples in the first place. In effect, it requires another field in what is already becoming a crowded tuple, as “triples” now have one additional slot for graphs, a second slot for types in objects, and now a third slot (for a total of six) for managing a unique URI that acts as the reified URI for the entry, when you’re talking about potentially billions of triples, each of these slots has a cumulative effect in terms of both performance and memory requirements.

RDF Graphs Are Property Graphs Event Without RDF*

This raises a question – do you need RDF*? From the annotational standpoint, quite possibly, as it provides a means of tracking volatile property and relationship value changes over time. From a modeling perspective, however, perhaps not. For instance, the marriage example given earlier in this article can actually be resolved quite handily by simply creating a marriage class that points to both (or potentially more than both) spouses with a `marriage:hasSpouse` property, rather than attempting to create a poorly considered `person:isMarriedTo` relationship.

Put another way – RDF* should not be used to solve modeling deficiencies. The marriage example without RDF* is actually easier to articulate and understand as just one instance of this.

select ?spouse where {

    VALUES (?personOfInterest ?targetDate)  {

        (person:_Jane   “2015-01-01″^^xsd:date)

        }

    ?marriage a class:_Marriage.

    ?marriage marriage:hasSpouse ?spouse.

    filter(?personOfInterest IN ?spouse and not(sameTerm(?personOfInterest,?spouse)))

    ?marriage marriage:hasStartDate ?startDate.

   OPTIONAL {?marriage hasEndDate ?endDate}

   filter(?targetDate >= ?startDate AND ?endDate > ?targetDate)

   }

The primary illustration of this comes in the area of paths. Property graphs are used frequently in path analysis, where the goal is to either minimize or maximize the aggregate values of a particular property that crosses a path. A good example of this would be airline maps, where an airline flies certain routes, and the goal is to minimize the distance travelled to get to a particular airport from another airport. Again, this is where modeling can actual prove more effective than trying to emulate property-graph-like behavior.

For instance, you can define an airline route as the path taken between airports to get to a final destination. While you COULD use RDF* for this, you’re probably better off putting the time into modeling this correctly:

flightSegment:_SEA_DEN

      a class:_FlightSegment;

      flightSegment:hasStartAirport airport:_SEA;

      flightSegment:hasEndAirport airport:_DEN;

      flightSegment:hasDistance “1648”^^length:_kilometer;

      .

 

flightSegment:_DEN_ATL

      a class:_FlightSegment;

      flightSegment:hasStartAirport airport:_DEN;

      flightSegment:hasEndAirport airport:_ATL;

      flightSegment:hasDistance “1947”^^length:_kilometer;

      .

 

flightSegment:_DEN_STL

      a class:_FlightSegment;

      flightSegment:hasStartAirport airport:_DEN;

      flightSegment:hasEndAirport airport:_STL;

      flightSegment:hasDistance “1507”^^length:_kilometer;

      .

 

flightSegment:_ATL_BOS

      a class:_FlightSegment;

      flightSegment:hasStartAirport airport:_ATL;

      flightSegment:hasEndAirport airport:_BOS;

      flightSegment:hasDistance “1947”^^length:_kilometer;

      .

 

flightSegment:_STL_BOS

      a class:_FlightSegment;

      flightSegment:hasStartAirport airport:_DEN;

      flightSegment:hasEndAirport airport:_STL;

      flightSegment:hasDistance “1667”^^length:_kilometer;

      .

 

 

route:_SEA_DEN_ATL_BOS route:hasSequence (

                                  flightSegment:_SEA_DEN,

                                  flightSegment:_DEN_ATL,

                                  flightSegment:_ATL_BOS).

 

 

route:_SEA_DEN_STL_BOS route:hasSequence (

                                  flightSegment:_SEA_DEN,

                                  flightSegment:_DEN_STL,

                                  flightSegment:_STL_BOS).

 

In this particular case, calculating flight distances for different routes, while not trivial, is doable. The trick is to understand that sequences in RDF are represented by rdf:first/rdf:rest chains, where rdf:first points to a given item in the sequence, and rdf:rest points to the next pointer in the chain:

select ?route (sum(?flightDistance) as ?totalDistance) where {

     values (?startAirport ?endAirport) {(airport:_SEA airport:_BOS)}

     # Determine which flight segments originate from the start airport

     # or ends with the last airport

     ?flightSegment1 flightSegment:hasStartAirport ?startAirport.

     ?flightSegment2 flightSegment:hasEndAirport ?endAirport.

     # Identify those routes that start with the first segment and end with the

     # last.

     ?route route:hasSequence ?sequence.

     ?sequence rdf:first ?flightSegment1.

     ?sequence rdf:rest*/rdf:first ?flightSegment2.

     # Check to make sure that the last flight segment is the final segment in the

     # sequence.

     ?flightSegment2 ^rdf:first/rdf:rest rdf:nil.

     # retrieve all of the flight segments in the sequence and get the flight

     #  distance.

     ?sequence rdf:rest*/rdf:first ?flightSegment.

     ?flightSegment flightSegment:hasDistance ?flightDistance.

}

It’s always worth remembering that RDF graphs generally work best by capturing derived information. Thus, once such route distances are calculated once, they can always be stored back into the graph to minimize the overall computational costs. In other words, with knowledge graphs, the more information that you can index, the more intelligent the overall system becomes:

# Sparql

insert {?route route:hasTotalDistance sum(?flightDistance) where {

 

 

     ?flightSegment1 flightSegment:hasStartAirport ?startAirport.

     ?flightSegment2 flightSegment:hasEndAirport ?endAirport.

     ?route route:hasSequence ?sequence.

     ?sequence rdf:first ?flightSegment1.

     ?sequence rdf:rest*/rdf:first ?flightSegment2.

     ?flightSegment2 ^rdf:first/rdf:rest rdf:nil.

     ?sequence rdf:rest*/rdf:first ?flightSegment.

     ?flightSegment flightSegment:hasDistance ?flightDistance.

}

Summary

Reification plays a big part in managing annotations, and a lesser role in operational logic such as per property permissions, and for that, RDF* and SPARQL* provide some powerful tools. However, in general, intelligent model design is all that is really needed to make RDF graphs both as efficient and as flexible as labeled property graphs. That RDF is not used as much tends to come down to the fact that most developers prefer to model their domain as little as possible, even though the long-term benefits of intelligent modeling make such solutions useful for far longer than plug-and-play labeled property graphs.

Another area worth exploring is the ability to extend SPARQL through various tools. I’ll explore this in more detail in my next post.

 

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The Future of Medical Software Development.

2020 demonstrated how helpless humankind could be when facing an epidemic. It is clear now that healthcare systems in many countries need to be transformed. And this transformation is impossible without leading-edge technology as the challenges are enormous. Like many other spheres, healthcare is undergoing a digital revolution. Data Science, Machine Learning, and Artificial Intelligence will be dominating the healthcare software development sphere in the decades to come. They will help create unprecedented products and systems that will save and improve the lives of people globally: software for hospitals, such as digital workplaces for healthcare professionals, drug prescription assistance, medical staff training, coordinating care and health information exchange; for patients – healthcare chatbots, virtual medicine apps; for device manufactures – medical apps for users, cloud solutions for data storage and management; for pharmaceutical companies – systems for drug testing and medication guidance, etc. Overall, healthcare technology pursues the following ambitious goals:

Let’s have a closer look at technology trends in the medical industry, which promise a healthier future for us all, with some looking truly mind-blowing even in the hi-tech age.

Healthcare technology – industry trends and solutions

1. Telemedicine and personal medical devices

The Covid-19 pandemic created a pressing need to reduce contact between patients and healthcare workers and caused an upsurge in telehealth services’ popularity. Smart wearables are a crucial component for telemedicine as they enable access to real-time patient data, such as blood pressure and oxygen saturation, heart rate, etc., for physicians. Several companies are working to create a multifunctional device that will measure all key vital parameters. The doctor can conduct an exam remotely, track changes in the patient’s condition, and adjust the treatment. One of them is MedWand, which offers cloud-based systems for smooth virtual healthcare sessions.

The new generation of telemedicine software will ensure a remarkably high security level for electronic health records thanks to Blockchain and cloud data storage. WebRTC is among the key technologies that underpin the success of telehealth apps. Google’s open-source project enables API-based real-time interaction between mobile apps and web browsers with different data types, such as audio and video. App integration with smartphone health trackers, like Apple HealthKit, is also a must. Smartphones will turn into a min-lab, equipped with microscopes and sensors to analyze swab samples and detect abnormalities.

Despite fraud concerns, telemedicine popularity is forecasted to grow after the pandemic ends as virtual appointments are less stressful and time-consuming than conventional hospital visits. From the healthcare practitioner’s perspective, they allow providing service to more patients daily.

2. Artificial Intelligence

Artificial Intelligence is omnipresent and disrupts medicine like many other domains. Here are just a few medical objectives that can be reached with the help of AI:

3. Virtual and Augmented Reality

Virtual and Augmented Reality tools are in wide use for educational and entertainment purposes across numerous industries. In medicine, the range of applications includes simulations for health professionals training, planning complex surgeries, diagnostics, anxiety and pain therapy, and rehabilitation (for instance, dealing with motor deficiencies, memory loss). Companies like ImmersiveTouch and Osso VR provide virtual platforms for surgeons and hospital staff. Interestingly, VR headsets have proven effective for alleviating pain through sound and color therapy. Augmented Reality screens help surgeons make better decisions during emergencies. AR also streamlines robotic surgeries.

4. Nanotechnology 

Nanomedicine is only emerging and will probably have a slow adoption by patients. Although having invisible robots performing surgery or delivering medicine to the exact organs or cells is not for the weak-hearted, there is a vast range of no-daunting applications. Miniature devices like PillCam serve non-invasive diagnostics purposes. Another direction for nanotechnology is smart patches with biosensors. The Medical Futurist Journal features a fascinating overview of the latest products and solutions presented at the Consumer Electronics Show (CES) in 2020. Among the wow gadgets, there is a patch for continuous wound monitoring from a French company Grapheal. In October 2021, NanoMedicine International Conference and Exhibition will take place in Milan, Italy. The list of themes for discussion shows the immense potential of nanotechnology.

5. Robotics

Robotics is booming and has a wide range of healthcare applications. Exoskeletons with wireless brain-machine interface, robotic limbs, surgical robots, robotic assistants, and companions for disabled people – the future has come! Importantly, disinfectant and sanitary robots may play an essential role in preventing epidemics as they cannot contract a virus while taking care of infected patients. There is only one downside – robotic medicine is expensive. For instance, the famous da Vinci Surgical System costs over 1million US dollars

6. 3D printing

Now virtually anything can be printed – human tissues and organs, models and prosthetics, medical devices and pills. This field requires highly sophisticated software solutions for processing medical images, segmentation, mesh editing, and 3 D modeling.

7. In silico medicine trials

The creation of virtual organs (organs-on-a-chip) for simulated clinical trials is taking off. Computer mathematical models of human anatomy and physiology will allow testing new drugs on thousands of virtual patients in very short timeframes. The ethical aspect is also essential – this futuristic technology will help decrease or eliminate tests conducted on animals and human volunteers.

Conclusion

With all the amazing medical technology trends in mind, we can expect many breakthroughs in the upcoming decades. However, what already exists is not available or affordable for the developing countries’ population, who are often deprived even of basic healthcare. Therefore, like in education and finance, accessibility is among the main challenges technology faces today.

***

Originally published.

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Types of Saas Solutions: Categories and Examples

What is software as a service (SaaS)?

SaaS is web-based software accessible through the internet. Since SaaS adopts cloud computing technology, there’s no need for installing desktop applications — users simply subscribe to a service hosted on a remote server. For example, Netflix is a B2C SaaS platform that offers licensed videos on-demand and follows a subscription model.

The global SaaS market is expanding rapidly and will hit $436.9 billion in 2025. While COVID-19 is causing dramatic shifts in business due to telecommuting and social distancing, more companies rely on the SaaS model. Let’s take a closer look at the benefits of SaaS for business.

Benefits of B2B SaaS solutions

The SaaS model benefits software providers and their customers. For developers, SaaS allows a recurring revenue stream and faster deployment compared to traditional on-premise software. For companies, SaaS offers the chance to reach a wider audience and save software development and maintenance costs.

Here are the top advantages of choosing SaaS for business:

Cost-effective. A software vendor holds all maintenance and infrastructure costs.
Accessible. SaaS products can be accessed from anywhere via a web browser.
Scalable. Customers can change their usage plans anytime without hassle.
Easy to integrate. SaaS solutions support multiple integrations with other platforms.
Secure. The decentralized nature of cloud-based technology protects user data from breaches and loss.
Come with free service. SaaS provides automated backups, free updates, and swift customer support.
Easy to use. A friendly interface and simple user flow make SaaS easy to use for everyone, regardless of their technical skills.
Offer free trials. Most SaaS vendors allow you to try it before you buy it.

The best part is that SaaS fits all industries and company sizes, so both large and small businesses can benefit from it equally. In the next chapter, we will explore different categories along with the most prominent examples of SaaS software.

The most common types of SaaS for business

As we mentioned earlier, the SaaS market is huge and highly segmented. The cloud service model has reached all business niches and generates billions of dollars in revenue for SaaS companies. For example, Salesforce — the world’s largest SaaS provider — made $17,1 billion in 2020 alone.

While the complete list of SaaS categories can be extensive, we will cover the most widely used ones.

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Discover more

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