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Exam Code: C2090-913 Practice test 2022 by team
C2090-913 informix 4gl development

Exam Title : IBM Certified Solutions Expert - Informix 4GL Developer
Exam ID : C2090-913
Exam Duration : 90 mins
Questions in test : 90
Passing Score : 70 / 90
Exam Center : Pearson VUE
Real Questions : IBM Informix 4GL Development Real Questions
VCE practice questions : IBM C2090-913 Certification VCE Practice Test

Informix 4GL 18%
Statements 28%
Cursors and Memory 13%
Creating a Help File: The mkmessage Utility 1%
Creating a Report Driver 3%
Defining Program Variables 3%
Displaying Forms and Windows 4%
Forms that use Arrays 4%
Passing Values between Functions 6%
procedural Logic 1%
The REPORT Functions 3%
The SQLCA Record 6%

informix 4gl development
IBM development pdf
Killexams : IBM development pdf - BingNews Search results Killexams : IBM development pdf - BingNews Killexams : Birth Of IBM, Oracle, And Calculating Machines: This Week In Tech History

June 14, 1822

Charles Babbage reads to the Royal Astronomical Society a paper entitled "Note on the application of machinery to the computation of astronomical and mathematical tables" in which he proposes the construction of the Difference Engine. Considered to be a pre-curser of modern computers, it was designed to speed-up the production of error-free mathematical tables, just as early modern computers did. In 1991, London’s Science Museum completed the construction of the first-ever working model of the Difference Engine, under the direction of Doron Swade and Alan Bromley. Swade wrote in his 2000 book, The Difference Engine: Charles Babbage and the quest to build the first computer:

This book is a tale of two quests. The first is Charles Babbage’s quest to realise a vision—that the science of number could be mastered by mechanism. By simply turning the handle of his massive calculating engine Babbage planned to achieve results which up to that point in history could be achieve only by mental effort—thinking. But this was not all. Calculating engines offered a tantalizing new prospect. The ‘unerring certainty’ of mechanism would eliminate the risk of human error to which numerical calculation was so frustratingly prone. Infallible machines would compensate for the frailties of the human mind and extend its powers…

The second quest is the twentieth-century sequel: The quest at the Science Museum to build a working Babbage engine in time for the bicentenary of Babbage’s birth.

June 14, 1941

John Mauchly meets John Atanasoff at Iowa State University. During the next five days, Mauchly learned everything he could about what became to be known as the Atanasoff-Berry Computer (ABC) which he first heard about when Atanasoff visited Philadelphia in December 1940. The ABC was the first electronic digital computing device but was never put to real use because both Atanasoff and Berry left Iowa in 1942 to contribute to the war effort and did not resume the work after the war.

The significance of this meeting emerged years later when it became part of the evidence that led the judge in the case of Honeywell, Inc. v. Sperry Rand Corp., et al. to decide that the ENIAC patent was invalid, among other reasons, because “Eckert and Mauchly did not themselves invent the automatic electronic computer, but instead derived that subject matter from one Dr. John Vincent Atanasoff.” Campbell-Kelly and Aspray conclude in Computer: A History of the Information Machine:

The extent to which Mauchly drew on Atanasoff’s ideas remains unknown, and the evidence is massive and conflicting. The ABC was quite modest technology, and it was not fully implemented. At the very least we can infer that Mauchly saw the potential significance of the ABC and that this may have led him to propose a similar electronic solution to the Ballistic Research Laboratory’s [at the Moore School of Electrical Engineering at the University of Pennsylvania] computing needs.

They also note that in June 1941, Mauchly and Atanasoff “parted on very amicable terms.” Indeed, Mauchly wrote to Atanasoff on September 30th of that year:

A number of different ideas have come to me recently anent computing circuits—some of which are more or less hybrids, combining your methods with other things, and some of which are nothing like your machine. The question in my mind is this: is there any objection, from your point of view, to my building some sort of computer which incorporates some of the features of your machine? … Ultimately a second question might come up, of course, and that is, in the event that your present design were to hold the field against all challengers, and I got the Moore School interested in having something of the sort, would the way be open for us to build an ‘Atanasoff Calculator’ (a la Bush analyzer) here?

June 16, 1911

The Computing-Tabulating-Recording Company is incorporated. It changed its name to IBM  in 1924. In “Ideas make IBM 100 years young,” IBM’s Bernard Meyerson writes: “…if you really think about what keeps a company going, it’s that you have to keep reinventing yourself. You cannot reinvent yourself in the absence of great ideas. You have to have the great ideas, and you have to follow them through.” Meyerson equates the great ideas that sustain the life of a company with great innovations but in “1100100 and counting,” The Economist quotes Forrester Research’s George Colony: “IBM is not a technology company, but a company solving business problems using technology” and concludes:

Over time [the close relationships between IBM and its customers] became IBM’s most important platform—and the main reason for its longevity. Customers were happy to buy electric ‘calculating machines’, as Thomas Watson senior insisted on calling them, from the same firm that had sold them their electromechanical predecessors. They hoped that their trusted provider would survive in the early 1990s. And they are now willing to let IBM’s services division tell them how to organise their businesses better.

Kevin Maney lists five lessons he drew from his close study of IBM’s history, the first one being “At the start, convince the troops you’re a company of destiny, even if that seems crazy.” Thomas Watson Sr. did this and more. In a 1917 speech he said: “My duty is not the building of this business; it is rather, the building of the organization. … I [know] only one definition of good management; that is, good organization. So, as I see it, my work consists in trying to build a bigger and better organization. The organization, in its turn, will take care of the building of the business.”

So what was the Big IBM Idea? A trusted supplier? A focus on destiny and longevity? Building a bigger and better organization? All of the above?

In a 1994 Harvard Business Review article titled “The theory of the business,” Peter Drucker advanced the argument that great businesses revolve around a certain idea or “a theory of the business,” articulating the company’s assumptions about its environment, its mission, and its core competencies.

In response, I discussed in a letter-to-the-editor the similarities and dissimilarities between scientists and managers:

Managers [like scientists] must articulate their theories and how they can be refuted and then seek data that prove their theories wrong. That will prevent them from falling into the trap of discarding successful theories… the theory of the business may not just explain reality or past business success; it may also define it by communicating and convincing employees and customers that the company is unique. A business theory, then, unlike a scientific theory, can be true and false at the same time. That is how, as Drucker has illustrated, IBM and General Motors could both succeed and fail when they applied the same business theory to two different businesses.

In short, an idea or a set of ideas may explain past business success. But, business school education and management gurus notwithstanding, one cannot extract from history “management lessons,” prescriptions, and predictions about the future of this or any other business. Even if we had a perfect understanding of the reasons for IBM’s longevity, that would not tell us anything about the future of Apple or Google or Facebook. There is no one explanation or theory of business success and the same reasons for success in one case can be the very same reasons for failure in another.

I didn’t know it in 1994, but it turns out I was channeling Thomas Watson Sr. who said in another speech, this one in January 1915, shortly after he joined C-T-R:

We all know there have been numerous books written on scientific factory management, scientific sales management, the psychology of selling goods, etc. Many of us have read some of those books. Some of them are good; but we can’t accept any of them as a basis for us to work on. Neither can you afford to accept my ideas as whole and attempt to carry them out, because I do not believe in a fixed method–in any fixed way of selling goods, or of running a business.

June 16, 1977

Software Development Laboratories (SDL) is incorporated in Santa Clara, California, by Larry Ellison, Bob Miner and Ed Oates. It changed its name to Relational Software, Inc. (RSI) in 1979 and again to Oracle Systems Corporation in 1982. Since 1995, it has been known as Oracle Corporation.

June 18, 1908

Alan Archibald Campbell-Swinton publishes a letter in the journal Nature titled “Distant Electric Vision” in which he envisioned television as it was developed three decades later. He wrote: “Possibly no photoelectric phenomenon at present known will provide what is required in this respect, but should something suitable be discovered, distant electric vision will, I think, come within the region of possibility.”

In May 2013, Netflix stated its view on the future of television (PDF): “Over the coming decades and across the world, Internet TV will replace linear TV. Apps will replace channels, remote controls will disappear, and screens will proliferate. As Internet TV grows from millions to billions, Netflix, HBO, and ESPN are leading the way.”

June 18, 1948

Columbia Records introduces the LP (long playing) record at a press conference in the Waldorf Astoria hotel in New York.

At the time the LP was introduced, nearly all phonograph records for home use were made of an abrasive (and therefore noisy) shellac compound, employed a much larger groove, and played at approximately 78 revolutions per minute (rpm), limiting the playing time of a 12-inch diameter record to less than five minutes per side. The new product was a 12- or 10-inch (30 or 25 cm) fine-grooved disc made of vinyl and played with a smaller-tipped "microgroove" stylus at a speed of 33 1⁄3 rpm. Each side of a 12-inch LP could play for more than 20 minutes.

The LP was developed—and became the standard for record industry for half a century—when Columbia's president Edward Wallerstein insisted on hearing an entire movement of a symphony on one side of an album. Ward Botsford in High Fidelity magazine: "He was no inventor—he was simply a man who seized an idea whose time was ripe and begged, ordered, and cajoled a thousand men into bringing into being the now accepted medium of the record business."

Mon, 18 Jul 2022 05:33:00 -0500 Gil Press en text/html
Killexams : IBM Uses Power10 CPU As An I/O Switch

Back in early July, we covered the launch of IBM’s entry and midrange Power10 systems and mused about how Big Blue could use these systems to reinvigorate an HPC business rather than just satisfy the needs of the enterprise customers who run transaction processing systems and are looking to add AI inference to their applications through matrix math units on the Power10 chip.

We are still gathering up information on how the midrange Power E1050 stacks up on SAP HANA and other workloads, but in poking around the architecture of the entry single-socket Power S1014 and the dual-socket S1022 and S1024 machines, we found something interesting that we thought we should share with you. We didn’t see it at first, and you will understand immediately why.

Here is the block diagram we got our hands on from IBM’s presentations to its resellers for the Power S1014 machine:

You can clearly see an I/O chip that adds some extra PCI-Express traffic lanes to the Power10 processor complex, right?

Same here with the block diagram of the Power S1022 (2U chassis) machines, which use the same system boards:

There are a pair of I/O switches in there, as you can see, which is not a big deal. Intel has co-packaged PCH chipsets in the same package as the Xeon CPUs with the Xeon D line for years, starting with the “Broadwell-DE” Xeon D processor in May 2015. IBM has used PCI-Express switches in the past to stretch the I/O inside a single machine beyond what comes off natively from the CPUs, such as with the Power IC922 inference engine Big Blue launched in January 2020, which you can see here:

The two PEX blocks in the center are PCI-Express switches, either from Broadcom or MicroChip if we had to guess.

But, that is not what is happening with the Power10 entry machines. Rather, IBM has created a single dual-chip module with two whole Power10 chips inside of it, and in the case of the low-end machines where AIX and IBM i customers don’t need a lot of compute but they do need a lot of I/O, the second Power10 chip has all of its cores turned off and it is acting like an I/O switch for the first Power10 chip that does have cores turned on.

You can see this clearly in this more detailed block diagram of the Power S1014 machine:

And in a more detailed block diagram of the two-socket Power S1022 motherboard:

This is the first time we can recall seeing something like this, but obviously any processor architecture could support the same functions.

In the two-socket Power S1024 and Power L1024 machines

What we find particularly interesting is the idea that those Power10 “switch” chips – the ones with no cores activated – could in theory also have eight OpenCAPI Memory Interface (OMI) ports turned on, doubling the memory capacity of the systems using skinnier and slightly faster 128 GB memory sticks, which run at 3.2 GHz, rather than having to move to denser 256 GB memory sticks that run at a slower 2.67 GHz when they are available next year. And in fact, you could take this all one step further and turn off all of the Power10 cores and turn on all of the 16 OMI memory slots across each DCM and create a fat 8 TB or 16 TB memory server that through the Power10 memory area network – what IBM calls memory inception – could serve as the main memory for a bunch of Power10 nodes with no memory of their own.

We wonder if IBM will do such a thing, and also ponder what such a cluster of memory-less server nodes talking to a centralized memory node might do with SAP HANA, Spark, data analytics, and other memory intensive work like genomics. The Power10 chip has a 2 PB upper memory limit, and that is the only cap on where this might go.

There is another neat thing IBM could do here, too. Imagine if the Power10 compute chip in a DCM had no I/O at all but just lots of memory attached to it and the secondary Power10 chip had only a few cores and all of the I/O of the complex. That would, in effect, make the second Power10 chip a DPU for the first one.

The engineers at IBM are clearly thinking outside of the box; it will be interesting to see if the product managers and marketeers do so.

Tue, 26 Jul 2022 05:34:00 -0500 Timothy Prickett Morgan en-US text/html
Killexams : Diabetes in the Digital Age

Illustration: Jordon Cheung

A day in the life of a diabetes patient is an exercise in micromanagement. Blood sugar metering. Insulin injections. Meal plans. Exercise diaries. Logs filled with heart rate, blood pressure, and even pain measurements. It’s a lot to keep up with, for doctors as well as patients, but it’s also absolutely crucial. Proper diabetes management is key to a patient’s quality of life and to keeping blood sugar levels in line.

For the 387 million people in the world living with diabetes, this is reality. But in many ways, their experience is not so different from that of  the rest of us, who struggle to stay on top of our health and to effectively communicate to our health providers what’s going on with our bodies. In America, about half of all adults suffer from chronic illnesses, and according to a 2013 Pew Research Center study, among those who do track their health, about half of them keep up with progress  “in their heads.”

Mobile app development, fueled by cloud technology, is going to change these habits dramatically. Already, wearable technologies, such as Fitbit, and food trackers, are helping us log our meals, movement, and weight. These seemingly simple devices have transformative powers. They have been proved to keep people motivated to exercise and lose weight. According to the Pew study, tracking also leads 40 percent of trackers to ask a doctor new questions or to get a second opinion.

But digital tools are pushing healthcare to the cusp of a much larger transformation than that. Apps designed to Strengthen our health are going far beyond simple one-feature tracking and peer support. The new apps are capable of sharing, analyzing, and visualizing real-time health data across different platforms and populations, inspiring a host of possibilities.

These “wellness 2.0” apps will provide ways for people to take control of their own health and promise to transform our healthcare system by giving doctors valuable new insights and researchers clues to the prevention and management of chronic illness.

“The opportunity to put powerful but simple tools in the hands of an they try to manage their condition is so compelling,” says Sean M. Hogan, VP and General Manager of IBM Healthcare. “It is absolutely going to change the face of medicine.”

Robin Hrassnigg was diagnosed with Type 1 diabetes in 1991. He filled out the usual logs and diaries until he created Diabetizer, a Web-based diabetes-management portal that will launch this fall as a mobile app for iOS and Android named myDIABETIZER. “In the past, you had the diary and, with pen and paper, you put the information in the book. That’s the normal, old way,” says Hrassnigg. “What we give the diabetic is a complete overview about his or her health status, and this information can also be used to discuss the condition with their doctor.”

Diabetizer works by pulling in information from a number of digital tools, including Fitbit, Runkeeper, and nutrition apps. It meshes that data with glucose readings and insulin information and presents an overall picture of health. Among its most useful features: A risk-index screen shows whether blood sugar, cholesterol, BMI, or any number of other indicators is in the danger zone. Graphs allow users to look back over the past week or even the past year to see how weight, heart rate, or blood sugar has fluctuated or to pick up on patterns—maybe blood sugar is spiking on a day after too little exercise, for example.

“The app makes it all easy to handle,” explains Hrassnigg. “You get more motivated in using it because it makes it fun and you get accurate information that you can look back over for yourself. You can look at your information on your mobile in your free time. That is a big advantage [if you want to discuss] your condition with your doctor and for getting a feel for your own health indicators.” Doctors are able to receive a PDF of a patient's latest health stats from the app, via email.

“The opportunity to put powerful, but simple tools in the hands of an individual and be able to maintain a connection with them as they try to manage their condition is so compelling. It is absolutely going to change the face of medicine. Diabetizer is just one example.”

Sean M. Hogan, VP and General Manager of IBM Healthcare

“It’s very important that you have that information with you and not only in the ten minutes that you are discussing with the doctor.” -- the doctor may be with you for ten minutes. “You’ve got more concrete information with you and from all over the world you can get access to your data. And it’s also very really easy for a patient to see what to change to Strengthen my health.”

Robin Hrassnigg, founder of Diabetizer

“The structural shift that we’re wrestling with is the existing systems of care that are all built around acute-care models, but a societal need that is chronic and to be really effective needs to be supported with an ongoing basis outside the hospital environment.”

Sean M. Hogan, VP and General Manager of IBM Healthcare

“What we give the diabetic is a complete overview about his health status with a risk index. And this information can then be used to discuss his condition with his or her doctor or they can send it via PDF to the doctor.”

Robin Hrassnigg, founder of Diabetizer

“If you have information that might come from a fitness tracker to show your physical activity level, plus information from your glucose monitor, and you’re connected to your insulin pump, it's now possible that an app on your phone could tie all this information together to make an updated calculation in real time.”

Sean M. Hogan, VP and General Manager of IBM Healthcare

Share This

A day in the life of doctors who treat diabetes can be tedious. They are tasked with evaluating months of glucose readings and many other health indicators, sometimes in as little as 10 minutes while the patient is in the examining room. As for the patients, we all know what it's like to have a few minutes to explain symptoms during a doctor's appointment.

Wellness apps back up these experiences with data that we can easily share with our physicians, which leads to better recommendations and better outcomes. “The doctor is doing the same job,” says Hrassnigg, “but the patient has more concrete and more detailed information, which can now be easily provided to the doctor for an updated review of a case. That makes it easier to discuss with a doctor.”

What the world could use is more effective digital healthcare tools like Diabetizer, and fast. This is where advances in technology and cloud computing are finally cutting development times and making the process more democratic. Start-ups like Diabetizer, which moved its development over to IBM’s Bluemix cloud-based development platform earlier this year, can access a variety of cloud services instead of investing in expensive architecture. “As a start-up, it was very good for us because the costs were low,” says Hrassnigg. For one thing, he adds, “we didn’t have to invest in a big server infrastructure.”

Developers are also able to use the latest IBM Cloud services, such as IBM Bluemix, which offers a multitude of features and capabilities, to quickly add new functions into their apps. For example, Diabetizer used Bluemix to integrate fitness trackers as well as push notifications, email alerts, and photo storage into the app so that users can upload profile pics or even photos of a physical symptom they may want to share with their doctor.

“What’s so exciting about an application built on IBM Cloud’s development platform is you can put it out to a community and rapidly understand if it’s working,” says Hogan. “That’s vastly preferred to having to create an app and put it out on the market, wait multiple months, and go back into the lab.”

Beyond drastically shorter development times, it also helps make apps effective right out of the gate. Developers are ultimately looking to create apps that people use and return to again and again, which in the case of wellness apps is how the real benefit occurs. In addition to rapid application development and response, IBM Cloud also provides analytics and data-insight services that help developers understand how they can continue to refine their apps to meet patient needs.

Chronic diseases like diabetes are by definition daily and ongoing health issues. They’re caused, treated, and may ultimately be prevented by healthier choices and environmental factors—a fact that’s backed up by hard statistics. “Studies show that only about 10% to 25% of your health status is correlated to the clinical care that you receive and up to 30% associated with your unique genetic makeup,” says Hogan. “The predominant influence, up to 60%, is associated with your health behaviors, social and economic factors, and physical environments.”

Yet our healthcare system is currently set up in opposition to these facts. It deals with our well-being intermittently, through occasional doctor’s visits—or worse, when our health spirals out of control and we are hospitalized. This method of care drives up healthcare costs for everyone, and beyond that, it fails miserably at keeping us well.

“The structural shift that we’re wrestling with is the existing systems of care that are all built around acute-care models,” says Hogan. “But a societal need that is chronic and to be really effective needs to be supported with an ongoing basis outside the hospital environment.” Hogan adds that there's a real need to better understand risk factors for disease so we can Strengthen intervention and prevent disease altogether.

It’s not that we need to cut out doctors while building a new health paradigm. Doctors benefit as much as patients do when we fill in the gaps and keep ourselves well between visits. But digital tools and the data that they collect are proving to be very effective drivers toward 24/7, engaged, empowered, and consumer-driven healthcare.

And these tools are making doctors' lives easier too. As Hogan puts it, “they provide a connection back to the physician without it being overwhelming.”

Mon, 09 May 2022 15:53:00 -0500 text/html
Killexams : IoT Analytics Market is expected to Grow USD 92.46 Billion by 2030 | Sap, Oracle, IBM

Market Overview

The IoT analytics market has been esteemed at USD 9.1 billion in 2018 and required to develop at a CAGR of 24.63% by 2030, to arrive at USD 92.46 Billion by 2030.

The market is being driven by the growing development of bury-related devices and the sharing of data across a variety of industries. The IoT Analytics market is rapidly expanding due to the growing need to have data from numerous endeavors cautiously accessible. Continuous observation and sharing of knowledge are critical and should be prioritized. It has become easier to share data as a result of recent mechanical advancements and improvements. IoT analytics are used in a variety of businesses. The IoT analytics sector is used by the medical services business to Strengthen the nature of therapy. It’s also used in web-based business, retail, and assembly to refresh existing patterns and customer behavior that can be used to develop new products and services.

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The flexibility of the IoT analytics market forecast merchants to set restrictions or provide more highlights for similar pricing is one silver lining to the COVID-19 emergency. Most IoT analytics market implementers are optimistic about the potential of IoT innovation expenditure plans during the COVID times. COVID-19 drove spending increases at the same time. In terms of IoT analytics market spending adjustments, half of the respondents said COVID-19 increased the demand for computerized activities, including IoT.

Market Segmentation

Based on the Type, the market has been segmented into Predictive Analytics, Descriptive Analytics, and Prescriptive Analytics.

Based on the Application, the market has been segmented into energy the executives, building mechanization, prescient, stock administration, deals and client the board and security, and resource the board, and crisis the executives. To identify, filter, investigate, address, and quickly recover from major events, the organizations use advanced logical devices.

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  1. 5G market
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Regional Classification

North America continues to hold the largest share of the market, with revenue expected to reach approximately USD 50,000 Million during the forecast period and is expected to grow at the fastest rate in the global IoT investigation market. In addition, Europe is expected to account for 10% of the entire industry, as well as other IoT analytics market demands, allowing it to rank second in the global IoT investigation market by the end of the forecast period. Despite this, the Middle East and Africa (MEA) region would have a relatively low CAGR throughout the forecast period. Medical services will continue to be the most important driving vertical for the global IoT examination market, as the impact of retail is required to see the fastest growth for IoT investigation. During the forecasted time frame, medical care alone will be required to account for more than 70% of the IoT analytics industry. Transportation and coordination are expected to have the second-highest CAGR in the industry. Similarly, the Energy and Utilities vertical in the IoT analysis would have a low CAGR over the forecasted time range.

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Industry News

The major key players in the market are Amazon Web Services, Inc., Google, Inc., Microsoft Corporation, SAP SE, Oracle Corporation, IBM Corporation, Dell Technologies, Inc., Cisco Systems, Inc., HP Enterprise Company, and PTC, Inc. The market is receiving a boost as executives place a greater emphasis on cost and time, reducing the demand for continuous information, growing severe competition, increasing the use of robotization in businesses, and the introduction of trendsetting technologies.

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MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions.

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Mon, 25 Jul 2022 20:33:00 -0500 Market Research Future en-US text/html
Killexams : IBM Research Open-Sources Deep Search Tools


IBM Research’s Deep Search product uses natural language processing (NLP) to “ingest and analyze massive amounts of data—structured and unstructured.” Over the years, Deep Search has seen a wide range of scientific uses, from Covid-19 research to molecular synthesis. Now, IBM Research is streamlining the scientific applications of Deep Search by open-sourcing part of the product through the release of Deep Search for Scientific Discovery (DS4SD).

DS4SD includes specific segments of Deep Search aimed at document conversion and processing. First is the Deep Search Experience, a document conversion service that includes a drag-and-drop interface and interactive conversion to allow for quality checks. The second element of DS4SD is the Deep Search Toolkit, a Python package that allows users to “programmatically upload and convert documents in bulk” by pointing the toolkit to a folder whose contents will then be uploaded and converted from PDFs into “easily decipherable” JSON files. The toolkit integrates with existing services, and IBM Research is welcoming contributions to the open-source toolkit from the developer community.

IBM Research paints DS4SD as a boon for handling unstructured data (data not contained in a structured database). This data, IBM Research said, holds a “lot of value” for scientific research; by way of example, they cited IBM’s own Project Photoresist, which in 2020 used Deep Search to comb through more than 6,000 patents, documents, and material data sheets in the hunt for a new molecule. IBM Research says that Deep Search offers up to a 1,000× data ingestion speedup and up to a 100× data screening speedup compared to manual alternatives.

The launch of DS4SD follows the launch of GT4SD—IBM Research’s Generative Toolkit for Scientific Discovery—in March of this year. GT4SD is an open-source library to accelerate hypothesis generation for scientific discovery. Together, DS4SD and GT4SD constitute the first steps in what IBM Research is calling its Open Science Hub for Accelerated Discovery. IBM Research says more is yet to come, with “new capabilities, such as AI models and high quality data sources” to be made available through DS4SD in the future. Deep Search has also added “over 364 million” public documents (like patents and research papers) for users to leverage in their research—a big change from the previous “bring your own data” nature of the tool.

The Deep Search Toolkit is accessible here.

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Mon, 18 Jul 2022 02:37:00 -0500 text/html
Killexams : Amazon, IBM Move Swiftly on Post-Quantum Cryptographic Algorithms Selected by NIST

A month after the National Institute of Standards and Technology (NIST) revealed the first quantum-safe algorithms, Amazon Web Services (AWS) and IBM have swiftly moved forward. Google was also quick to outline an aggressive implementation plan for its cloud service that it started a decade ago.

It helps that IBM researchers contributed to three of the four algorithms, while AWS had a hand in two. Google contributed to one of the submitted algorithms, SPHINCS+.

A long process that started in 2016 with 69 original candidates ends with the selection of four algorithms that will become NIST standards, which will play a critical role in protecting encrypted data from the vast power of quantum computers.

NIST's four choices include CRYSTALS-Kyber, a public-private key-encapsulation mechanism (KEM) for general asymmetric encryption, such as when connecting websites. For digital signatures, NIST selected CRYSTALS-Dilithium, FALCON, and SPHINCS+. NIST will add a few more algorithms to the mix in two years.

Vadim Lyubashevsky, a cryptographer who works in IBM's Zurich Research Laboratories, contributed to the development of CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon. Lyubashevsky was predictably pleased by the algorithms selected, but he had only anticipated NIST would pick two digital signature candidates rather than three.

Ideally, NIST would have chosen a second key establishment algorithm, according to Lyubashevsky. "They could have chosen one more right away just to be safe," he told Dark Reading. "I think some people expected McEliece to be chosen, but maybe NIST decided to hold off for two years to see what the backup should be to Kyber."

IBM's New Mainframe Supports NIST-Selected Algorithms

After NIST identified the algorithms, IBM moved forward by specifying them into its recently launched z16 mainframe. IBM introduced the z16 in April, calling it the "first quantum-safe system," enabled by its new Crypto Express 8S card and APIs that provide access to the NIST APIs.

IBM was championing three of the algorithms that NIST selected, so IBM had already included them in the z16. Since IBM had unveiled the z16 before the NIST decision, the company implemented the algorithms into the new system. IBM last week made it official that the z16 supports the algorithms.

Anne Dames, an IBM distinguished engineer who works on the company's z Systems team, explained that the Crypto Express 8S card could implement various cryptographic algorithms. Nevertheless, IBM was betting on CRYSTAL-Kyber and Dilithium, according to Dames.

"We are very fortunate in that it went in the direction we hoped it would go," she told Dark Reading. "And because we chose to implement CRYSTALS-Kyber and CRYSTALS-Dilithium in the hardware security module, which allows clients to get access to it, the firmware in that hardware security module can be updated. So, if other algorithms were selected, then we would add them to our roadmap for inclusion of those algorithms for the future."

A software library on the system allows application and infrastructure developers to incorporate APIs so that clients can generate quantum-safe digital signatures for both classic computing systems and quantum computers.

"We also have a CRYSTALS-Kyber interface in place so that we can generate a key and provide it wrapped by a Kyber key so that could be used in a potential key exchange scheme," Dames said. "And we've also incorporated some APIs that allow clients to have a key exchange scheme between two parties."

Dames noted that clients might use Kyber to generate digital signatures on documents. "Think about code signing servers, things like that, or documents signing services, where people would like to actually use the digital signature capability to ensure the authenticity of the document or of the code that's being used," she said.

AWS Engineers Algorithms Into Services

During Amazon's AWS re:Inforce security conference last week in Boston, the cloud provider emphasized its post-quantum cryptography (PQC) efforts. According to Margaret Salter, director of applied cryptography at AWS, Amazon is already engineering the NIST standards into its services.

During a breakout session on AWS' cryptography efforts at the conference, Salter said AWS had implemented an open source, hybrid post-quantum key exchange based on a specification called s2n-tls, which implements the Transport Layer Security (TLS) protocol across different AWS services. AWS has contributed it as a draft standard to the Internet Engineering Task Force (IETF).

Salter explained that the hybrid key exchange brings together its traditional key exchanges while enabling post-quantum security. "We have regular key exchanges that we've been using for years and years to protect data," she said. "We don't want to get rid of those; we're just going to enhance them by adding a public key exchange on top of it. And using both of those, you have traditional security, plus post quantum security."

Last week, Amazon announced that it deployed s2n-tls, the hybrid post-quantum TLS with CRYSTALS-Kyber, which connects to the AWS Key Management Service (AWS KMS) and AWS Certificate Manager (ACM). In an update this week, Amazon documented its stated support for AWS Secrets Manager, a service for managing, rotating, and retrieving database credentials and API keys.

Google's Decade-Long PQC Migration

While Google didn't make implementation announcements like AWS in the immediate aftermath of NIST's selection, VP and CISO Phil Venables said Google has been focused on PQC algorithms "beyond theoretical implementations" for over a decade. Venables was among several prominent researchers who co-authored a technical paper outlining the urgency of adopting PQC strategies. The peer-reviewed paper was published in May by Nature, a respected journal for the science and technology communities.

"At Google, we're well into a multi-year effort to migrate to post-quantum cryptography that is designed to address both immediate and long-term risks to protect sensitive information," Venables wrote in a blog post published following the NIST announcement. "We have one goal: ensure that Google is PQC ready."

Venables recalled an experiment in 2016 with Chrome where a minimal number of connections from the Web browser to Google servers used a post-quantum key-exchange algorithm alongside the existing elliptic-curve key-exchange algorithm. "By adding a post-quantum algorithm in a hybrid mode with the existing key exchange, we were able to test its implementation without affecting user security," Venables noted.

Google and Cloudflare announced a "wide-scale post-quantum experiment" in 2019 implementing two post-quantum key exchanges, "integrated into Cloudflare's TLS stack, and deployed the implementation on edge servers and in Chrome Canary clients." The experiment helped Google understand the implications of deploying two post-quantum key agreements with TLS.

Venables noted that last year Google tested post-quantum confidentiality in TLS and found that various network products were not compatible with post-quantum TLS. "We were able to work with the vendor so that the issue was fixed in future firmware updates," he said. "By experimenting early, we resolved this issue for future deployments."

Other Standards Efforts

The four algorithms NIST announced are an important milestone in advancing PQC, but there's other work to be done besides quantum-safe encryption. The AWS TLS submission to the IETF is one example; others include such efforts as Hybrid PQ VPN.

"What you will see happening is those organizations that work on TLS protocols, or SSH, or VPN type protocols, will now come together and put together proposals which they will evaluate in their communities to determine what's best and which protocols should be updated, how the certificates should be defined, and things like things like that," IBM's Dames said.

Dustin Moody, a mathematician at NIST who leads its PQC project, shared a similar view during a panel discussion at the RSA Conference in June. "There's been a lot of global cooperation with our NIST process, rather than fracturing of the effort and coming up with a lot of different algorithms," Moody said. "We've seen most countries and standards organizations waiting to see what comes out of our nice progress on this process, as well as participating in that. And we see that as a very good sign."

Thu, 04 Aug 2022 10:39:00 -0500 en text/html
Killexams : Workload Scheduling Software Market Size and Growth 2022 Analysis Report by Development Plans, Manufactures, Latest Innovations and Forecast to 2028

The MarketWatch News Department was not involved in the creation of this content.

Aug 03, 2022 (The Expresswire) -- "Final Report will add the analysis of the impact of COVID-19 on this industry."

Global “Workload Scheduling Software Market” 2022 report presents a comprehensive study of the entire Global market including market size, share trends, market dynamics, and overview by segmentation by types, applications, manufactures and geographical regions. The report offers the most up-to-date industry data on the real market situation and future outlook for the Workload Scheduling Software market. The report also provides up-to-date historical market size data for the period and an illustrative forecast to 2028 covering key market aspects like market value and volume for Workload Scheduling Software industry.

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Market Analysis and Insights: Global Workload Scheduling Software Market

System management software is an application that manages all applications of an enterprise such as scheduling and automation, event management, workload scheduling, and performance management. Workload scheduling software is also known as batch scheduling software. It automates, monitors, and controls jobs or workflows in an organization. It allows the execution of background jobs that are unattended by the system administrator, aligning IT with business objectives to Strengthen an organization's performance and reduce the total cost of ownership. This process is known as batch processing. Workload scheduling software provides a centralized view of operations to the system administrator at various levels: project, organizational, and enterprise.
The global Workload Scheduling Software market size is projected to reach USD million by 2028, from USD million in 2021, at a CAGR of during 2022-2028.
According to the report, workload scheduling involves automation of jobs, in which tasks are executed without human intervention. Solutions like ERP and customer relationship management (CRM) are used in organizations across the globe. ERP, which is a business management software, is a suite of integrated applications that is being used by organizations in various sectors for data collection and interpretation related to business activities such as sales and inventory management. CRM software is used to manage customer data and access business information.

The major players covered in the Workload Scheduling Software market report are:

● BMC Software ● Broadcom ● IBM ● VMWare ● Adaptive Computing ● ASG Technologies ● Cisco ● Microsoft ● Stonebranch ● Wrike ● ServiceNow ● Symantec ● Sanicon Services ● Cloudify

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Global Workload Scheduling Software Market: Drivers and Restrains

The research report has incorporated the analysis of different factors that augment the market’s growth. It constitutes trends, restraints, and drivers that transform the market in either a positive or negative manner. This section also provides the scope of different segments and applications that can potentially influence the market in the future. The detailed information is based on current trends and historic milestones. This section also provides an analysis of the volume of production about the global market and about each type from 2017 to 2028. This section mentions the volume of production by region from 2017 to 2028. Pricing analysis is included in the report according to each type from the year 2017 to 2028, manufacturer from 2017 to 2022, region from 2017 to 2022, and global price from 2017 to 2028.

A thorough evaluation of the restrains included in the report portrays the contrast to drivers and gives room for strategic planning. Factors that overshadow the market growth are pivotal as they can be understood to devise different bends for getting hold of the lucrative opportunities that are present in the ever-growing market. Additionally, insights into market expert’s opinions have been taken to understand the market better.

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Global Workload Scheduling Software Market: Segment Analysis

The research report includes specific segments by region (country), by manufacturers, by Type and by Application. Each type provides information about the production during the forecast period of 2017 to 2028. By Application segment also provides consumption during the forecast period of 2017 to 2028. Understanding the segments helps in identifying the importance of different factors that aid the market growth.

Segment by Type

● On-Premises ● Cloud-Based

Segment by Application

● Large Enterprises ● Small And Medium-Sized Enterprises (SMEs) ● Government Organizations

Workload Scheduling Software Market Key Points:

● Characterize, portray and Forecast Workload Scheduling Software item market by product type, application, manufactures and geographical regions. ● give venture outside climate investigation. ● give systems to organization to manage the effect of COVID-19. ● give market dynamic examination, including market driving variables, market improvement requirements. ● give market passage system examination to new players or players who are prepared to enter the market, including market section definition, client investigation, conveyance model, item informing and situating, and cost procedure investigation. ● Stay aware of worldwide market drifts and give examination of the effect of the COVID-19 scourge on significant locales of the world. ● Break down the market chances of partners and furnish market pioneers with subtleties of the cutthroat scene.

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Geographical Segmentation:

Geographically, this report is segmented into several key regions, with sales, revenue, market share, and Workload Scheduling Software market growth rate in these regions, from 2015 to 2028, covering

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia, and Vietnam) ● South America (Brazil etc.) ● Middle East and Africa (Egypt and GCC Countries)

Some of the key questions answered in this report:

● Who are the worldwide key Players of the Workload Scheduling Software Industry? ● How the opposition goes in what was in store connected with Workload Scheduling Software? ● Which is the most driving country in the Workload Scheduling Software industry? ● What are the Workload Scheduling Software market valuable open doors and dangers looked by the manufactures in the worldwide Workload Scheduling Software Industry? ● Which application/end-client or item type might look for gradual development possibilities? What is the portion of the overall industry of each kind and application? ● What centered approach and imperatives are holding the Workload Scheduling Software market? ● What are the various deals, promoting, and dissemination diverts in the worldwide business? ● What are the key market patterns influencing the development of the Workload Scheduling Software market? ● Financial effect on the Workload Scheduling Software business and improvement pattern of the Workload Scheduling Software business?

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Detailed TOC of Global Workload Scheduling Software Market Research Report 2022

1 Workload Scheduling Software Market Overview

1.1 Product Overview and Scope

1.2 Segment by Type

1.2.1 Global Market Size Growth Rate Analysis by Type 2022 VS 2028

1.3 Workload Scheduling Software Segment by Application

1.3.1 Global Consumption Comparison by Application: 2022 VS 2028

1.4 Global Market Growth Prospects

1.4.1 Global Revenue Estimates and Forecasts (2017-2028)

1.4.2 Global Production Capacity Estimates and Forecasts (2017-2028)

1.4.3 Global Production Estimates and Forecasts (2017-2028)

1.5 Global Market Size by Region

1.5.1 Global Market Size Estimates and Forecasts by Region: 2017 VS 2021 VS 2028

1.5.2 North America Workload Scheduling Software Estimates and Forecasts (2017-2028)

1.5.3 Europe Estimates and Forecasts (2017-2028)

1.5.4 China Estimates and Forecasts (2017-2028)

1.5.5 Japan Estimates and Forecasts (2017-2028)

2 Workload Scheduling Software Market Competition by Manufacturers

2.1 Global Production Capacity Market Share by Manufacturers (2017-2022)

2.2 Global Revenue Market Share by Manufacturers (2017-2022)

2.3 Market Share by Company Type (Tier 1, Tier 2 and Tier 3)

2.4 Global Average Price by Manufacturers (2017-2022)

2.5 Manufacturers Production Sites, Area Served, Product Types

2.6 Market Competitive Situation and Trends

2.6.1 Market Concentration Rate

2.6.2 Global 5 and 10 Largest Workload Scheduling Software Players Market Share by Revenue

2.6.3 Mergers and Acquisitions, Expansion

3 Workload Scheduling Software Production Capacity by Region

3.1 Global Production Capacity of Workload Scheduling Software Market Share by Region (2017-2022)

3.2 Global Revenue Market Share by Region (2017-2022)

3.3 Global Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.4 North America Production

3.4.1 North America Production Growth Rate (2017-2022)

3.4.2 North America Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.5 Europe Production

3.5.1 Europe Production Growth Rate (2017-2022)

3.5.2 Europe Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.6 China Production

3.6.1 China Production Growth Rate (2017-2022)

3.6.2 China Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.7 Japan Production

3.7.1 Japan Production Growth Rate (2017-2022)

3.7.2 Japan Production Capacity, Revenue, Price and Gross Margin (2017-2022)

4 Global Workload Scheduling Software Market Consumption by Region

4.1 Global Consumption by Region

4.1.1 Global Consumption by Region

4.1.2 Global Consumption Market Share by Region

4.2 North America

4.2.1 North America Consumption by Country

4.2.2 United States

4.2.3 Canada

4.3 Europe

4.3.1 Europe Consumption by Country

4.3.2 Germany

4.3.3 France

4.3.4 U.K.

4.3.5 Italy

4.3.6 Russia

4.4 Asia Pacific

4.4.1 Asia Pacific Consumption by Region

4.4.2 China

4.4.3 Japan

4.4.4 South Korea

4.4.5 China Taiwan

4.4.6 Southeast Asia

4.4.7 India

4.4.8 Australia

4.5 Latin America

4.5.1 Latin America Consumption by Country

4.5.2 Mexico

4.5.3 Brazil

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5 Workload Scheduling Software Market Segment by Type

5.1 Global Production Market Share by Type (2017-2022)

5.2 Global Revenue Market Share by Type (2017-2022)

5.3 Global Price by Type (2017-2022)

6 Workload Scheduling Software Market Segment by Application

6.1 Global Production Market Share by Application (2017-2022)

6.2 Global Revenue Market Share by Application (2017-2022)

6.3 Global Price by Application (2017-2022)

7 Workload Scheduling Software Market Key Companies Profiled

7.1 Manufacture 1

7.1.1 Manufacture 1 Corporation Information

7.1.2 Manufacture 1 Product Portfolio

7.1.3 Manufacture 1 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.1.4 Manufacture 1 Main Business and Markets Served

7.1.5 Manufacture 1 recent Developments/Updates

7.2 Manufacture 2

7.2.1 Manufacture 2 Corporation Information

7.2.2 Manufacture 2 Product Portfolio

7.2.3 Manufacture 2 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.2.4 Manufacture 2 Main Business and Markets Served

7.2.5 Manufacture 2 recent Developments/Updates

7.3 Manufacture 3

7.3.1 Manufacture 3 Corporation Information

7.3.2 Manufacture 3 Product Portfolio

7.3.3 Manufacture 3 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.3.4 Manufacture 3 Main Business and Markets Served

7.3.5 Manufacture 3 recent Developments/Updates

8 Workload Scheduling Software Manufacturing Cost Analysis

8.1 Key Raw Materials Analysis

8.1.1 Key Raw Materials

8.1.2 Key Suppliers of Raw Materials

8.2 Proportion of Manufacturing Cost Structure

8.3 Manufacturing Process Analysis of Workload Scheduling Software

8.4 Workload Scheduling Software Industrial Chain Analysis

9 Marketing Channel, Distributors and Customers

9.1 Marketing Channel

9.2 Workload Scheduling Software Distributors List

9.3 Workload Scheduling Software Customers

10 Market Dynamics

10.1 Workload Scheduling Software Industry Trends

10.2 Workload Scheduling Software Market Drivers

10.3 Workload Scheduling Software Market Challenges

10.4 Workload Scheduling Software Market Restraints

11 Production and Supply Forecast

11.1 Global Forecasted Production of Workload Scheduling Software by Region (2023-2028)

11.2 North America Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.3 Europe Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.4 China Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.5 Japan Workload Scheduling Software Production, Revenue Forecast (2023-2028)

12 Consumption and Demand Forecast

12.1 Global Forecasted Demand Analysis of Workload Scheduling Software

12.2 North America Forecasted Consumption of Workload Scheduling Software by Country

12.3 Europe Market Forecasted Consumption of Workload Scheduling Software by Country

12.4 Asia Pacific Market Forecasted Consumption of Workload Scheduling Software by Region

12.5 Latin America Forecasted Consumption of Workload Scheduling Software by Country

13 Forecast by Type and by Application (2023-2028)

13.1 Global Production, Revenue and Price Forecast by Type (2023-2028)

13.1.1 Global Forecasted Production of Workload Scheduling Software by Type (2023-2028)

13.1.2 Global Forecasted Revenue of Workload Scheduling Software by Type (2023-2028)

13.1.3 Global Forecasted Price of Workload Scheduling Software by Type (2023-2028)

13.2 Global Forecasted Consumption of Workload Scheduling Software by Application (2023-2028)

13.2.1 Global Forecasted Production of Workload Scheduling Software by Application (2023-2028)

13.2.2 Global Forecasted Revenue of Workload Scheduling Software by Application (2023-2028)

13.2.3 Global Forecasted Price of Workload Scheduling Software by Application (2023-2028)

14 Research Finding and Conclusion

15 Methodology and Data Source

15.1 Methodology/Research Approach

15.1.1 Research Programs/Design

15.1.2 Market Size Estimation

15.1.3 Market Breakdown and Data Triangulation

15.2 Data Source

15.2.1 Secondary Sources

15.2.2 Primary Sources

15.3 Author List

15.4 Disclaimer

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Tue, 02 Aug 2022 18:15:00 -0500 en-US text/html
Killexams : Healthcare Analytics Market Top Key Players Analysis | IBM, Elsevier, McKesson Corporation, Oracle and Medeanalytics, Inc.

Market Data Centre

Healthcare Analytics Market 2022 - 2030 - Vendor Assessment (Company Profiles, Market Positioning, Strategies, recent Developments, Capabilities & Product Offerings / Mapping), Technology Assessment (Developments & Economic Impact), Partner & Customer Ecosystem (Product Services, Proposition & Key Features) Competitive Index & Regional FootPrint by MDC Research

Pune, Aug. 05, 2022 (GLOBE NEWSWIRE) -- Healthcare Analytics Market by Vendor Assessment, Technology Assessment, Partner & Customer Ecosystem, type/solution, service, organization size, end-use verticals, and Region – Global Healthcare Analytics Market Forecast to 2030, published by Market Data Centre, The Healthcare Analytics Market is projected to grow at a solid pace during the forecast period. The presence of key players in the ecosystem has led to a compsetitive and diverse market. The advancement of digital transformation initiatives across multiple industries is expected to drive the worldwide Healthcare Analytics Market during the study period.

This COVID-19 analysis of the report includes COVID-19 IMPACT on the production and, demand, supply chain. This report provides a detailed historical analysis of the global Healthcare Analytics Market from 2017-to 2021 and provides extensive market forecasts from 2022-to 2030 by region/country and subsectors. The report covers the revenue, sales volume, price, historical growth, and future perspectives in the Healthcare Analytics Market.

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Regional Analysis:

On the basis of Geography, the Global Healthcare Analytics Market is segmented into North America, Europe, Asia-Pacific, and the Rest of the World (RoW). North America is expected to hold a considerable share in the global Healthcare Analytics Market. Due to increasing investment for research and development process and adoption of solutions in the region whereas Asia-Pacific is expected to grow at a faster pace during the forecasted period.

The growing number of Healthcare Analytics Market players across regions is expected to drive market growth further. Moreover, increasing investments by prominent vendors in product capabilities and business expansion is expected to fuel the market during the study period. Many market players are finding lucrative opportunities in emerging economies like China and India, where the large populations are coupled with new innovations in numerous industries.

List of Companies Covered in this Report are:

  • Verisk Analytics, Inc.

  • Elsevier

  • Medeanalytics, Inc.

  • Truven Health Analytics, Inc.

  • Allscripts Healthcare Solutions, Inc.

  • Cerner Corporation

  • McKesson Corporation

  • Optum, Inc.

  • IBM

  • Oracle

  • SAS Institute, Inc.


  • Among others.

Market Assessment

Technology Assessment

Vendor Assessment

Market Dynamics

Key Innovations

Product Breadth and Capabilities

Trends and Challenges

Adoption Trends and Challenges

Technology Architecture

Drivers and Restrains

Deployment Trends

Competitive Differentiation

Regional and Industry Dynamics

Industry Applications

Price/Performance Analysis

Regulations and Compliance

Latest Upgrardation

Strategy and Vision

In deep ToC includes

233 – Tables

45  – Figures

300 – Pages

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Table of Contents                                                                           

1.1.   Market Definition
1.2.   Market Segmentation
1.3.   Geographic Scope
1.4.   Years Considered: Historical Years – 2017 & 2020; Base Year – 2021; Forecast Years – 2022 to 2030
1.5.   Currency Used
2.1.   Research Framework
2.2.   Data Collection Technique
2.3.   Data Sources
2.3.1.      Secondary Sources
2.3.2.      Primary Sources
2.4.   Market Estimation Methodology
2.4.1.      Bottom-Up Approach
2.4.2.      Top-Down Approach
2.5.   Data Validation and Triangulation
2.5.1.      Market Forecast Model
2.5.2.      Limitations/Assumptions of the Study
4.1.   Overview
4.2.   Drivers
4.3.   Barriers/Challenges
4.4.   Opportunities
8.1.   Global - Healthcare Analytics Market Analysis & Forecast, By Region
8.2.   Global - Healthcare Analytics Market Analysis & Forecast, By Segment
8.2.1.      North America Healthcare Analytics Market, By Segment
8.2.2.      North America Healthcare Analytics Market, By Country            US            Canada
8.2.3.      Europe Healthcare Analytics Market, By Segment
8.2.4.      Europe Healthcare Analytics Market, By Country            Germany            UK            France            Rest of Europe (ROE)
8.2.5.      Asia Pacific Healthcare Analytics Market, By Segment
8.2.6.      Asia Pacific Healthcare Analytics Market, By Country            China            Japan            India            Rest of Asia Pacific (RoAPAC)
8.2.7.      Rest of the World (ROW) Healthcare Analytics Market, By Segment
8.2.8.      Rest of the World (ROW) Healthcare Analytics Market, By Country            Latin America            Middle East & Africa

ToC can be modified as per clients' business requirements*

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Key Questions Answered in This Report:

  • How does our product and services portfolio compare to leading competitors?

  • What are the key developments in customer demand given the changing economy?

  • What are the new pricing and consumption models in the marketplace and how should we align our portfolio?

  • What are the key decision drivers for services buyers?

  • How can we accelerate our bidding process?

  • What is the potential of the Healthcare Analytics Market?

  • What is the impact of COVID-19 on the global Healthcare Analytics Market?

  • What are the top strategies that companies adopting in Healthcare Analytics Market?

  • What are the challenges faced by SME’s and prominent vendors in Healthcare Analytics Market?

  • Which region has the highest investments in Healthcare Analytics Market?

  • What are the latest research and activities in Healthcare Analytics Market?

  • Who are the prominent players in Healthcare Analytics Market?

  • What is the potential of the Healthcare Analytics Market?

Vendor Assessment

Vendor assessment includes a deep analysis of how vendors are addressing  the demand in the Healthcare Analytics Market. The MDC CompetetiveScape model was used to assess qualitative and quantitative insights in this assessment. MDC's CompetitiveScape is a structured method for identifying key players and outlining their strengths, relevant characteristics, and outreach strategy. MDC's CompetitiveScape allows organizations to analyze the environmental factors that influence their business, set goals, and identify new marketing strategies. MDC Research analysts conduct a thorough investigation of vendors' solutions, services, programs, marketing, organization size, geographic focus, type of organization and strategies.

Technology Assessment

Technology dramatically impacts business productivity, growth and efficiency.Technologies can help companies develop competitive advantages, but choosing them can be one of the most demanding decisions for businesses. Technology assessment helps organizations to understand their current situation with respect to technology and offer a roadmap where they might want to go and scale their business. A well-defined process to assess and select technology solutions can help organizations reduce risk, achieve objectives, identify the problem, and solve it in the right way. Technology assessment can help businesses identify which technologies to invest in, meet industry standards, compete against competitors.

Business Ecosystem Analysis

Advancements in technology and digitalization have changed the way companies do business; the concept of a business ecosystem helps businesses understand how to thrive in this changing environment. Business ecosystems provide organizations with opportunities to integrate technology in their daily business operations and Strengthen research and business competency. The business ecosystem includes a network of interlinked companies that compete  and cooperate to increase sales, Strengthen profitability, and succeed in their markets. An ecosystem analysis is a business network analysis that includes the relationships amongst suppliers, distributors, and end-users in delivering a product or service.

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Regions and Countries Covered

North America (US, Canada), Europe (Germany, UK, France, Spain, Italy, and Rest of Europe), Asia-Pacific (Japan, China, Australia, India, Rest of Asia-Pacific), and Rest of the World (RoW).

Report Coverage

Healthcare Analytics Market Dynamics, Covid-19 Impact on the Healthcare Analytics Market, Vendor Profiles, Vendor Assessment, Strategies, Technology Assessment, Product Mapping, Industry Outlook, Economic Analysis, Segmental Analysis, Healthcare Analytics Market Sizing, Analysis Tables.

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Market Data Centre offers complete solutions for market research reports in miscellaneous businesses.These decisions making process depend on wider and systematic extremely important information created through extensive study as well as the most recent trends going on in the industry.The company also attempts to offer much better customer-friendly services and appropriate business information to achieve our clients’ ideas.

CONTACT: Market Data Centre (Subsidiary of Yellow Bricks Global Services Private Limited) Office 808, Amar Business Park, S.No. 105, Baner Road, Pune 411045, India Email: Phone: +1-916-848-6986 (US) Website:
Fri, 05 Aug 2022 01:19:00 -0500 en-US text/html
Killexams : Cloud Augmented Intelligence Market – Major Technology Giants in Buzz Again | MicroStrategy, SAP, IBM, SAS, CognitiveScale

Advance Market Analytics published a new research publication on “Cloud Augmented Intelligence Market Insights, to 2027” with 232 pages and enriched with self-explained Tables and charts in presentable format. In the Study you will find new evolving Trends, Drivers, Restraints, Opportunities generated by targeting market associated stakeholders. The growth of the Cloud Augmented Intelligence market was mainly driven by the increasing R&D spending across the world.

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Some of the key players profiled in the study are: AWS (United States), Microsoft (United States), Salesforce (United States), SAP (Germany), IBM (United States), SAS (United States), CognitiveScale (United States), QlikTech International (United States), TIBCO (United States), Google (United States), MicroStrategy (United States) and Sisense (United States).

Scope of the Report of Cloud Augmented Intelligence
The global market for cloud augmented intelligence is growing as organisations increasingly leverage cutting-edge technologies like big data, block chain, artificial intelligence, and the internet of things to meet customer expectations. Additionally, the market’s expansion is positively impacted by the spike in demand for business intelligence products. However, factors including software implementation challenges and a shortage of cloud augmented intelligence specialists are anticipated to restrain market expansion. In contrast, it is anticipated that throughout the forecast period, significant companies would advance their use of augmented intelligence solutions and the volume and variety of data will expand within an automated process, providing lucrative chances for the market’s growth.

The titled segments and sub-section of the market are illuminated below:

by Technology (Machine Learning, Natural Language Processing, Computer Vision, Others), Industry Vertical (IT & Telecom, Retail & E-Commerce, BFSI, Healthcare, Manufacturing, Automotive, Others), Component (Software, Service), Organisation Size (Small & Medium, Large) Players and Region – Global Market Outlook to 2027

Solutions for Cloud Augmented Intelligence Are Widely Used By SMES
Increased Use of Technology for Machine Learning, Artificial Intelligence, and Natural Language Processing

Market Drivers:
A Growing Amount of Sophisticated Corporate Data
Expanding Use of Cutting-Edge Cloud Augmented Intelligence and Analytics Tools

Have Any Questions Regarding Global Cloud Augmented Intelligence Market Report, Ask Our [email protected]

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa

Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Latest Market Insights:

In January 2022, Microsoft Corp. announced its plans to acquire Activision Blizzard Inc., a leader in game development and interactive entertainment content publisher. This acquisition will accelerate the growth in Microsoft’s gaming business across mobile, PC, console and cloud and will provide building blocks for the met averse.

In March 2022, Schlumberger partnered with Dataiku to provide customers with a single, centralized platform for designing, deploying, governing, and managing AI and analytics applications, allowing everyday users to create low-code no-code AI solutions. and In April 2021, Oracle made its GoldenGate technology available as a highly automated, fully managed cloud service that clients can use to help ensure that their valuable data is always available and analyzable in real-time, wherever they need it.

Strategic Points Covered in Table of Content of Global Cloud Augmented Intelligence Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Augmented Intelligence market

Chapter 2: Exclusive Summary – the basic information of the Cloud Augmented Intelligence Market.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Cloud Augmented Intelligence

Chapter 4: Presenting the Cloud Augmented Intelligence Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

Chapter 5: Displaying the by Type, End User and Region/Country 2015-2020

Chapter 6: Evaluating the leading manufacturers of the Cloud Augmented Intelligence market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile

Chapter 7: To evaluate the market by segments, by countries and by Manufacturers/Company with revenue share and sales by key countries in these various regions (2021-2027)

Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

finally, Cloud Augmented Intelligence Market is a valuable source of guidance for individuals and companies.

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Thanks for memorizing this article; you can also get individual chapter wise section or region wise report version like North America, Middle East, Africa, Europe or LATAM, Southeast Asia.

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Mon, 01 Aug 2022 19:24:00 -0500 Newsmantraa en-US text/html
Killexams : NLP in Healthcare and Life Sciences Market Analysis by Size, Share, Key Players, Growth, Trends & Forecast 2026

"Microsoft (US), Google (US), IBM (US), Cerner (US), 3M (US), AWS (US), Inovalon (US), Dolbey (US), Averbis (Germany), Linguamatics (an IQVIA Company) (UK), Apixio (US), Clinithink (US), Lexalytics (US), Apixio (US), Clinithink (US), Lexalytics (US), Health Fidelity (US), Wave Health Technologies (US), Corti (US), CloudMedx (US), Oncora Medical (US)."

NLP in Healthcare and Life Sciences Market by Component (Solutions and Services), NLP Type (Rule-based, Statistical, and Hybrid), Application (IVR, Predictive Risk Analytics), Organization Size, End User, and Region - Global Forecast to 2026

The global NLP in Healthcare and Life Sciences Market size to grow from USD 1.8 billion in 2021 to USD 4.3 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 19.0% during the forecast period. Factors such as growing need to analyze and extract insights from narrative text and huge amount of clinical data, increasing demand for improving EHR data usability to Strengthen healthcare delivery and outcomes and the rising urge of predictive analytics technology to reduce risks and Strengthen significant health concerns are driving the adoption of the NLP in healthcare and life sciences market across the globe.

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In the constant fight against COVID-19, pharmaceutical and healthcare organizations, government bodies, and the broader scientific communities worldwide are working to assess the impact of the COVID-19 virus and quickly develop accurate solutions. Few vendors in the market have observed that NLP provides faster, more systematic, and more comprehensive insight generation from unstructured text. Gaining key information from sources such as scientific literature, clinical trial records, pre-prints, internal sources, medical records, and social media and news reports, and synthesizing it into one evidence hub deepens the understanding for users, help accelerate outcomes. NLP is extensively used in different organizations to categorize sentiments, provide recommendations, and summarize information and topic. With the spread of COVID-19, communities, patients, and clinicians across the globe have all witnessed major disruptions in the way they work and how they engage with stakeholders across the ecosystem. Pharmaceutical and life science companies face immense pressure to provide essential medical products to support needy patients and ensure the development of new therapeutics and vaccines for COVID-19.

Scope of the Report

Report Metrics


Market size available for years


Base year considered


Forecast period


Forecast units

USD Million

Segments covered

Component, Deployment Mode, Organization Size, NLP Type, Application, End User, And Region

Geographies covered

North America, Europe, APAC, Latin America and MEA

Companies covered

Microsoft (US), Google (US), IBM (US), Cerner (US), 3M (US), AWS (US), Inovalon (US), Dolbey (US), Averbis (Germany), Linguamatics (an IQVIA Company) (UK), Apixio (US), Clinithink (US), Lexalytics (US), Apixio (US), Clinithink (US), Lexalytics (US), Health Fidelty (US), Wave Health Technologies (US), Corti (US), CloudMedx (US), Oncora Medical (US), Caption Health (US), ForeSee Medical (US), Press Ganey (US), (India), Notable (US), Biofourmis (US), Babylon (UK), Flatiron (US), and Suki (US)

The services segment to hold higher CAGR during the forecast period

Based on components, the NLP in healthcare and life sciences market is segmented into solutions and services. The services segment has been further divided into professional and managed services. These services play a vital role in the functioning of NLP in healthcare and life sciences, as well as ensure faster and smoother implementation that maximizes the value of the enterprise investments. The growing adoption of NLP technology is expected to boost the adoption of professional and managed services. Professional service providers have deep knowledge related to the products and enable customers to focus on the core business, while MSPs help healthcare firms to Strengthen their business operations and reduce overall expenses.

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According to ForeSee Medical, NLP is the ability of computers to understand the latest human speech terms and text. It is used in current technology to support spam email privacy, personal voice assistants, and language translation applications. The adoption of NLP in healthcare and life sciences is rising because of its recognized potential to search, analyze, and interpret a mammoth amount of patient datasets. Using advanced NLP-based algorithms, healthcare and life sciences firms harness the relevant insights and concepts from the clinical data that was previously considered buried in the text form.

Some of the key players operating in the NLP in healthcare and life sciences market include Microsoft (US), Google (US), IBM (US), Cerner (US), 3M (US), AWS (US), Inovalon (US), Dolbey (US), Averbis (Germany), Linguamatics (an IQVIA Company) (UK), Apixio (US), Clinithink (US), Lexalytics (US), Apixio (US), Clinithink (US), Lexalytics (US), Health Fidelty (US), Wave Health Technologies (US), Corti (US), CloudMedx (US), Oncora Medical (US), Caption Health (US), ForeSee Medical (US), Press Ganey (US), (India), Notable (US), Biofourmis (US), Babylon (UK), Flatiron (US), and Suki (US). These NLP in healthcare and life sciences vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global NLP in healthcare and life sciences market.

Microsoft (US) develops software, services, devices, and solutions to compete around intelligent cloud and intelligent edge. With continuous investments in the cloud, Microsoft enables its customers to digitalize their business processes. The company’s offerings include cloud-based solutions that provide customers with software, platforms, content and deliver solution support and consulting services. Its product offerings include Operating Systems (OS), cross-device productivity applications, server applications, business solution applications, desktop and server management tools, software development tools, and video games. Microsoft operates its business using three segments: Productivity and Business Processes, Intelligent Cloud, and More Personal Computing. The company’s platforms and tools help drive the productivity of small businesses, the competitiveness of large businesses, and the efficiency of the public sector. The company’s platform accelerates innovation across the spectrum of intelligent edge devices, from IoT sensors to gateway devices and edge hardware to build, manage, and secure edge workloads. Microsoft will invest USD 1 billion over the next four years in new technologies and innovative climate solutions. It has a geographical presence in more than 190 countries across North America, APAC, Latin America, MEA, and Europe. In response to the COVID-19 pandemic, Microsoft partnered with the Allen Institute for AI and leading research groups to prepare the COVID-19 Open Research Dataset. It is a free resource containing over 47,000 scholarly articles for use by the global research community. With Cognitive Search and Text Analytics, Microsoft developed the COVID-19 search engine, enabling researchers to more quickly evaluate and gain insights from the overwhelming amount of information about the COVID-19 pandemic.

3M (US) company was formerly known as Minnesota Mining and Manufacturing Company. 3M is a diversified global manufacturer, technology innovator, and marketer of a wide variety of products and services. 3M is a well-known provider of products, such as adhesives, laminates, dental products, orthodontic products, abrasives, and medical appliances. 3M is a diversified technology company with a worldwide presence and operates in segments including Safety and Industrial; Transportation and Electronics; Health Care, and Consumer. The company operates worldwide and caters to more than 65 nations. It delivers products through retailing and distributing partners in more than 200 nations. The company offers its products to verticals, such as healthcare, consumer and office; transportation and industry, safety, display and graphics, security and protection services, and electronics and communication. The company develops, manufactures, and markets its innovative products for the global market. 3M developed its NLP platform for email spam detection, personal assistants, and language translation apps. It further developed various healthcare-specific applications based on this platform for text processing and documentation. The company’s NLP platform automates the process of mining clinical concepts from unstructured data. 3M Health Information System (HIS) uses NLP to autosuggest codes, which helps coders turn clinical documentation into rich data sources, thus helping coders save time. 3M has also come up with an NLP software platform named CodeRyte CodeAssist. The platform helps capture the physician’s report and record diseases. All of this results in improved productivity, performance, and efficiency.

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