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The primary object of this manual is to build an understanding of the principles of computer operations and the use of computers in the laboratory. While the development of applications for computers has been rapid since their introduction, the principles of computer operation and their use in sensing and control have remained stable. Those are the primary subjects of this book, throughout which a gradual understanding of what goes on inside a computer is developed. The laboratory provides a vital experience in linking theory with physical reality, and all of the computer work is done in the context of doing experiments. The IBM-PC design is used as the basis for the book. The internal design of this machine is slightly more complicated than earlier personal computers, but it is still simple enough to be quickly learned. The computer can be directly controlled by proper programming, and offers considerably more power than earlier designs. The IBM design also has expansion slots which make the addition of special hardware capabilities relatively simple, and provide a great flexibility in interfacing the machine to other equipment. The book, based on courses given at Cornell University, is designed as a tutorial to be used in conjunction with laboratory work. It will be a valuable guide and reference for students who are familiar with first-year university physics and have some computing experience.

Thu, 01 Apr 2021 22:32:00 -0500 en text/html https://www.cambridge.org/core/books/ibmpc-in-the-laboratory/4B5A26F781CB073199FEE85F19427AA8
Killexams : IBM Research Rolls Out A Comprehensive AI And ML Edge Research Strategy Anchored By Enterprise Partnerships And Use Cases

I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Improve future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Improve quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Improve the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

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Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html https://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/
Killexams : IBM Research uses advanced computing to accelerate therapeutic and biomarker discovery

Over the past decade, artificial intelligence (AI) has emerged as an engine of discovery by helping to unlock information from large repositories of previously inaccessible data. The cloud has expanded computer capacity exponentially by creating a global network of remote and distributed computing resources. And quantum computing has arrived on the scene as a game changer in processing power by harnessing quantum simulation to overcome the scaling and complexity limits of classical computing.

In parallel to these advances in computing, in which IBM is a world leader, the healthcare and life sciences have undergone their own information revolution. There has been an explosion in genomic, proteomic, metabolomic and a plethora of other foundational scientific data, as well as in diagnostic, treatment, outcome and other related clinical data. Paradoxically, however, this unprecedented increase in information volume has resulted in reduced accessibility and a diminished ability to use the knowledge embedded in that information. This reduction is caused by siloing of the data, limitations in existing computing capacity, and processing challenges associated with trying to model the inherent complexity of living systems.

IBM Research is now working on designing and implementing computational architectures that can convert the ever-increasing volume of healthcare and life-sciences data into information that can be used by scientists and industry experts the world over. Through an AI approach powered by high-performance computing (HPC)—a synergy of quantum and classical computing—and implemented in a hybrid cloud that takes advantage of both private and public environments, IBM is poised to lead the way in knowledge integration, AI-enriched simulation, and generative modeling in the healthcare and life sciences. Quantum computing, a rapidly developing technology, offers opportunities to explore and potentially address life-science challenges in entirely new ways.

“The convergence of advances in computation taking place to meet the growing challenges of an ever-shifting world can also be harnessed to help accelerate the rate of discovery in the healthcare and life sciences in unprecedented ways,” said Ajay Royyuru, IBM fellow and CSO for healthcare and life sciences at IBM Research. “At IBM, we are at the forefront of applying these new capabilities for advancing knowledge and solving complex problems to address the most pressing global health challenges.”

Improving the drug discovery value chain

Innovation in the healthcare and life sciences, while overall a linear process leading from identifying drug targets to therapies and outcomes, relies on a complex network of parallel layers of information and feedback loops, each bringing its own challenges (Fig. 1). Success with target identification and validation is highly dependent on factors such as optimized genotype–phenotype linking to enhance target identification, improved predictions of protein structure and function to sharpen target characterization, and refined drug design algorithms for identifying new molecular entities (NMEs). New insights into the nature of disease are further recalibrating the notions of disease staging and of therapeutic endpoints, and this creates new opportunities for improved clinical-trial design, patient selection and monitoring of disease progress that will result in more targeted and effective therapies.

Accelerated discovery at a glance

Fig. 1 | Accelerated discovery at a glance. IBM is developing a computing environment for the healthcare and life sciences that integrates the possibilities of next-generation technologies—artificial intelligence, the hybrid cloud, and quantum computing—to accelerate the rate of discovery along the drug discovery and development pipeline.

Powering these advances are several core computing technologies that include AI, quantum computing, classical computing, HPC, and the hybrid cloud. Different combinations of these core technologies provide the foundation for deep knowledge integration, multimodal data fusion, AI-enriched simulations and generative modeling. These efforts are already resulting in rapid advances in the understanding of disease that are beginning to translate into the development of better biomarkers and new therapeutics (Fig. 2).

“Our goal is to maximize what can be achieved with advanced AI, simulation and modeling, powered by a combination of classical and quantum computing on the hybrid cloud,” said Royyuru. “We anticipate that by combining these technologies we will be able to accelerate the pace of discovery in the healthcare and life sciences by up to ten times and yield more successful therapeutics and biomarkers.”

Optimized modeling of NMEs

Developing new drugs hinges on both the identification of new disease targets and the development of NMEs to modulate those targets. Developing NMEs has typically been a one-sided process in which the in silico or in vitro activities of large arrays of ligands would be tested against one target at a time, limiting the number of novel targets explored and resulting in ‘crowding’ of clinical programs around a fraction of validated targets. recent developments in proteochemometric modeling—machine learning-driven methods to evaluate de novo protein interactions in silico—promise to turn the tide by enabling the simultaneous evaluation of arrays of both ligands and targets, and exponentially reducing the time required to identify potential NMEs.

Proteochemometric modeling relies on the application of deep machine learning tools to determine the combined effect of target and ligand parameter changes on the target–ligand interaction. This bimodal approach is especially powerful for large classes of targets in which active-site similarities and lack of activity data for some of the proteins make the conventional discovery process extremely challenging.

Protein kinases are ubiquitous components of many cellular processes, and their modulation using inhibitors has greatly expanded the toolbox of treatment options for cancer, as well as neurodegenerative and viral diseases. Historically, however, only a small fraction of the kinome has been investigated for its therapeutic potential owing to biological and structural challenges.

Using deep machine learning algorithms, IBM researchers have developed a generative modeling approach to access large target–ligand interaction datasets and leverage the information to simultaneously predict activities for novel kinase–ligand combinations1. Importantly, their approach allowed the researchers to determine that reducing the kinase representation from the full protein sequence to just the active-site residues was sufficient to reliably drive their algorithm, introducing an additional time-saving, data-use optimization step.

Machine learning methods capable of handling multimodal datasets and of optimizing information use provide the tools for substantially accelerating NME discovery and harnessing the therapeutic potential of large and sometimes only minimally explored molecular target spaces.

Focusing on therapeutics and biomarkers

Fig. 2 | Focusing on therapeutics and biomarkers. The identification of new molecular entities or the repurposing potential of existing drugs2, together with improved clinical and digital biomarker discovery, as well as disease staging approaches3, will substantially accelerate the pace of drug discovery over the next decade. AI, artificial intelligence.

Drug repurposing from real-world data

Electronic health records (EHRs) and insurance claims contain a treasure trove of real-world data about the healthcare history, including medications, of millions of individuals. Such longitudinal datasets hold potential for identifying drugs that could be safely repurposed to treat certain progressive diseases not easily explored with conventional clinical-trial designs because of their long time horizons.

Turning observational medical databases into drug-repurposing engines requires the use of several enabling technologies, including machine learning-driven data extraction from unstructured sources and sophisticated causal inference modeling frameworks.

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders in the world, affecting 1% of the population above 60 years of age. Within ten years of disease onset, an estimated 30–80% of PD patients develop dementia, a debilitating comorbidity that has made developing disease-modifying treatments to slow or stop its progression a high priority.

IBM researchers have now developed an AI-driven, causal inference framework designed to emulate phase 2 clinical trials to identify candidate drugs for repurposing, using real-world data from two PD patient cohorts totaling more than 195,000 individuals2. Extracting relevant data from EHRs and claims data, and using dementia onset as a proxy for evaluating PD progression, the team identified two drugs that significantly delayed progression: rasagiline, a drug already in use to treat motor symptoms in PD, and zolpidem, a known psycholeptic used to treat insomnia. Applying advanced causal inference algorithms, the IBM team was able to show that the drugs exert their effects through distinct mechanisms.

Using observational healthcare data to emulate otherwise costly, large and lengthy clinical trials to identify repurposing candidates highlights the potential for applying AI-based approaches to accelerate potential drug leads into prospective registration trials, especially in the context of late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

Enhanced clinical-trial design

One of the main bottlenecks in drug discovery is the high failure rate of clinical trials. Among the leading causes for this are shortcomings in identifying relevant patient populations and therapeutic endpoints owing to a fragmented understanding of disease progression.

Using unbiased machine-learning approaches to model large clinical datasets can advance the understanding of disease onset and progression, and help identify biomarkers for enhanced disease monitoring, prognosis, and trial enrichment that could lead to higher rates of trial success.

Huntington’s disease (HD) is an inherited neurodegenerative disease that results in severe motor, cognitive and psychiatric disorders and occurs in about 3 per 100,000 inhabitants worldwide. HD is a fatal condition, and no disease-modifying treatments have been developed to date.

An IBM team has now used a machine-learning approach to build a continuous dynamic probabilistic disease-progression model of HD from data aggregated from multiple disease registries3. Based on longitudinal motor, cognitive and functional measures, the researchers were able to identify nine disease states of clinical relevance, including some in the early stages of HD. Retrospective validation of the results with data from past and ongoing clinical studies showed the ability of the new disease-progression model of HD to provide clinically meaningful insights that are likely to markedly Improve patient stratification and endpoint definition.

Model-based determination of disease stages and relevant clinical and digital biomarkers that lead to better monitoring of disease progression in individual participants is key to optimizing trial design and boosting trial efficiency and success rates.

A collaborative effort

IBM has established its mission to advance the pace of discovery in healthcare and life sciences through the application of a versatile and configurable collection of accelerator and foundation technologies supported by a backbone of core technologies (Fig. 1). It recognizes that a successful campaign to accelerate discovery for therapeutics and biomarkers to address well-known pain points in the development pipeline requires external, domain-specific partners to co-develop, practice, and scale the concept of technology-based acceleration. The company has already established long-term commitments with strategic collaborators worldwide, including the recently launched joint Cleveland Clinic–IBM Discovery Accelerator, which will house the first private-sector, on-premises IBM Quantum System One in the United States. The program is designed to actively engage with universities, government, industry, startups and other relevant organizations, cultivating, supporting and empowering this community with open-source tools, datasets, technologies and educational resources to help break through long-standing bottlenecks in scientific discovery. IBM is engaging with biopharmaceutical enterprises that share this vision of accelerated discovery.

“Through partnerships with leaders in healthcare and life sciences worldwide, IBM intends to boost the potential of its next-generation technologies to make scientific discovery faster, and the scope of the discoveries larger than ever,” said Royyuru. “We ultimately see accelerated discovery as the core of our contribution to supercharging the scientific method.”

Mon, 11 Apr 2022 04:28:00 -0500 en text/html https://www.nature.com/articles/d43747-022-00128-z
Killexams : IoT In Smart Cities Market size worth $ 735 Billion, Globally, by 2030 at 23.04% CAGR: Tested Market Research

Various factors such as an increase in adoption of IoT technology for infrastructure management and city monitoring and exponential rise in urban population are expected to drive the adoption of IoT in smart cities solutions and services.

JERSEY CITY, N.J., Aug. 8, 2022 /PRNewswire/ -- Tested Market Research recently published a report, "IoT In Smart Cities Market" By Offering (Solution, Services), By Application (Smart Transportation, Smart Building), and By Geography. According to Tested Market Research, the IoT In Smart Cities Market size was valued at USD 112 Billion in 2021 and is projected to reach USD 735 Billion by 2030, growing at a CAGR of 23.04% from 2022 to 2030.

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Browse in-depth TOC on "IoT In Smart Cities Market"

202 - Pages

126 - Tables

37 - Figures

Global IoT In Smart Cities Market Overview

Globally the demand for IoT services has accumulated at a speedy rate within the past few years. The introduction of the latest technologies supported by IoT platforms and the rising integration of IoT services have boosted the demand in this market. Moreover, rising government programs for smart cities are taken into account as a key sector for the expansion of this market.

The introduction of advanced technologies and their inclusion in most sectors have been big at a big rate within the past few years. Customers today like the victimization of advanced technologies, that are acting as a vital growth driver in this market. Governments in each developed and developing region are currently taking bear in mind to build smart cities, therefore, growing the demand considerably during this market. Moreover, the implementation of IoT services helps in providing higher and advanced security solutions and monitoring assets from a remote location with no physical presence. Considering the above-named factors, there are remunerative growth opportunities in this market which will accelerate the market's growth within the coming years.

Key Developments

  • June 2020, Siemens AG, a German multi-industry company, joined hands with Salesforce Inc. to develop a new workplace technology. The solution will help companies to offer a safer experience to their employees after reopening worldwide.

Key Players

The major players in the market are Cisco, Intel, IBM, Huawei, Tech Mahindra, Microsoft, Honeywell, Bosch Software Innovations, Siemens, PTC, ARM, Schneider Electric, and Quantela.

Verified Market Research has segmented the Global IoT In Smart Cities Market On the basis of Offering, Application, and Geography.

  • IoT In Smart Cities Market, By Offering
  • IoT In Smart Cities Market, By Application
    • Smart Transportation
    • Smart Building
    • Smart Citizen
    • Others
  • IoT In Smart Cities Market, by Geography
    • North America
    • Europe
      • Germany
      • France
      • U.K
      • Rest of Europe
    • Asia Pacific
      • China
      • Japan
      • India
      • Rest of Asia Pacific
    • ROW
      • Middle East & Africa
      • Latin America

Browse Related Reports:

Smart Offices Market Size By Office Type (Retrofit Offices and New Construction Offices), By Product (Smart Lighting/Lighting Controls, Security Systems), By Communication Technology (Wireless Technologies, Wired Technologies), By Geography, Forecast, 2021-2028

Smart Cities Market By Focus Area (Smart Transportation, Smart Buildings, Smart Utilities, and Smart Citizen Services), By Smart Transportation (Solutions and Services), By Smart Citizen Service (Public safety, Smart healthcare, Smart education, Smart Street Lighting, and e-Governance), By Geography, Forecast, 2021-2028

Smart City ICT Infrastructure Market By Type (Smart Grid, Smart Logistics, Smart Transport, Smart Water Network, Smart Building, Smart Education, Others), By Geography, Forecast, 2021-2028

Smart City & Connected City Solutions Market By Product (Smart Grid, Smart Building, Smart Water Network, Smart Healthcare, Smart Education, Smart Security, Smart Transport), By Application (Passenger Car, Commercial Vehicle), By Geography, Forecast, 2021-2028

Top 7 Biosensor Companies developing best biosensors technology

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Killexams : Quantum Computing Software Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2026

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

Jul 25, 2022 (AB Digital via COMTEX) -- The Quantum Computing Software Market size is projected to grow from USD 0.11 billion in 2021 to 0.43 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 30.5% during the forecast period. The major factors driving the growth of the Quantum Computing Software market include the growing adoption of quantum computing software in the BFSI vertical, government support for the development and deployment of the technology, and the increasing number of strategic alliances for research and development.

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Based on Component, the service segment to grow at a higher CAGR during the forecast period

Among the component segment, the services segment is leading the quantum computing software market in 2021. The growth of the services segment can be attributed to the increasing investments by start-ups in research and development related to quantum computing technology. Quantum computing software and services are used in optimization, simulation, and machine learning applications, thereby leading to optimum utilization costs and highly efficient operations in various industries.

Based on application, the optimization segment is expected to hold the highest market size during the forecast period

The optimization segment is expected to lead the global quantum computing software market in terms of market share. Optimization problems exist across all industries and business functions. Some of these problems take too long to be solved optimally with traditional computers, where the usage of quantum computing technology is expected to be an optimum solution. Several optimization problems require a global minimal point solution. By using quantum annealing, the optimization problems can be solved earlier as compared to supercomputers.

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Major Quantum Computing Software vendors include IBM Corporation (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US), D-Wave Systems Inc (Canada), Rigetti Computing (US), Google LLC (US), Honeywell International Inc. (US), QC Ware (US), 1QBit (US), Huawei Technologies Co., Ltd. (China), Accenture plc (Ireland), Cambridge Quantum Computing (England), Fujitsu Limited (Japan), Riverlane (UK), Zapata Computing (US), Quantum Circuits, Inc. (US), Quantica Computacao (India), XANADU Quantum Technologies (Canada), VeriQloud (France), Quantastica (Finland), AVANETIX (Germany), Kuano (England), Rahko (UK), Ketita Labs (Estonia), and Aliro Quantum (US). These market players have adopted various growth strategies, such as partnerships, collaborations, and new product launches, to expand have been the most adopted strategies by major players from 2019 to 2021, which helped companies innovate their offerings and broaden their customer base.

IBM was founded in 1911 and is headquartered in New York, US. It is a multinational technology and consulting corporation that offers infrastructure, hosting, and consulting services. The company operates through five major business segments: Cloud and Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing. IBM Cloud has emerged as a platform of choice for all business applications, as it is AI compatible. It is a unifying platform that integrates IBM’s capabilities with a single architecture and spans over public and private cloud platforms. With this powerful cloud platform, the company can cater to the requirements of different businesses across the globe. IBM caters to various verticals, including aerospace & defense, education, healthcare, oil & gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemical, government, manufacturing, travel & transportation, construction, and metals & mining. The company has a strong presence in the Americas, Europe, MEA, and APAC and clients in more than 175 countries. IBM is one of the major players in the quantum computing ecosystem. The company in 2016 made a quantum computer available to the public by connecting it to the cloud. In September 2019, it opened a Quantum Computation Center. The Quantum Computation Center offers about 100 IBM clients, academic institutions, and more than 200,000 registered users access to this cutting-edge technology through a collaborative effort called the IBM Q Network and Qiskit, IBM’s open-source development platform for quantum computing. Through these efforts, IBM is exploring the ways quantum computing can address the most complicated problems faced while training the workforce to use this technology.

Rigetti Computing was founded in 2013 and is headquartered in California, US. Rigetti Computing designs and manufactures superconducting quantum-integrated circuits. It develops quantum computers, as well as superconducting quantum processors that power them. The machines of the company can be integrated with any public, private, or hybrid cloud through the quantum cloud services (QCS) platform. It is a full-stack quantum computing company that provides an integrated computing environment. Rigetti Computing develops algorithms for quantum computing that focus on application areas such as machine learning, logistics, healthcare and pharmaceuticals, and chemicals. The company also delivers a set of tools, such as Quil, pyQuil, and Quilc, which help solve optimization problems.

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Killexams : Serious Games Market To Surpass $32.72 Billion By 2030 | Emerging Growth Factors, Business Development

(MENAFN- EIN Presswire)

Serious Games Market

The report segments the global serious games market on the basis of analysis gaming platform, applications, industry vertical, and region.

PORTLAND, PORTLAND, OR , UNITED STATES , July 27, 2022 /EINPresswire.com / -- Advent of social networks and inclination toward interactive advertisements coupled with large-scale digitization would unlock new opportunities in the future. Rise in demand for better user engagement platforms across organizations, improvement in learning outcomes, adoption of virtual reality in training and development activities, and rise in use of mobile-based educational games have boosted the growth of the global serious games market .

Key market players such as - BreakAway, Ltd., DIGINEXT, Designing Digitally, Inc., Intuition, IBM Corporation, Nintendo Co., Ltd., Learning Nexus Ltd, Revelian, and Tata Interactive Systems.

The global serious games market was pegged at $5.94 billion in 2020, and is expected to reach $32.72 billion by 2030, growing at a CAGR of 18.4% from 2021 to 2030.

The global serious games industry is analyzed across several regions such as North America, Europe, Asia-Pacific, and LAMEA. The market across Asia-Pacific held the lion's share in 2020, accounting for more than two-fifths of the market. However, the market across LAMEA region is expected to portray the highest CAGR of 19.6% from 2021 to 2030.

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On the basis of gaming platform, the smartphone segment dominated the market in 2020, contributing to more than one-third of the market. In addition, the segment is projected to manifest the highest CAGR of 19.0% during the forecast period. The report also analyzes the segments including console, PC, and others.

Based on application, the simulation and training segment held the largest share in 2020, accounting for more than one-third of the market. However, the research and planning segment is estimated to register the highest CAGR of 20.1% during the forecast period.

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• The advent of Covid-19 pandemic and lockdown across several countries boosted the demand for serious games.
• The shutdown of schools increased the adoption of e-learning, which positively affected the market.

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Killexams : Global BPM-platform-based Case Management Software Market Emerging Growth Analysis, Future Demand and Business Opportunities 2027

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

Aug 01, 2022 (Heraldkeepers) -- Pune, India-Global BPM-platform-based Case Management Software Market Industry oversaw different associations of the business from various geologies or locales. The Report study comprises of subjective and quantitative data featuring key market improvements challenges that industry and rivalry are looking alongside hole investigation, new open doors accessible and pattern additionally incorporate COVID-19 effect Analysis in Global BPM-platform-based Case Management Software Market and effect different elements bringing about boosting Global BPM-platform-based Case Management Software Market at worldwide just as territorial level. There are colossal rivalries that happen worldwide and should require the investigation of MARKET Shares ANALYSIS quite a Top Competitors/Top Players are: Pegasystems, Bizagi, IBM, Hyland, K2, Appian, AgilePoint, Microsoft, Newgen Software, PMG, Isis Papyrus, MicroPact, OpenText. Watchman’s Five Forces Analysis, sway examination of Coronavirus, and SWOT Analysis are additionally referenced to comprehend the elements affecting shopper and provider conduct.

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NOTE: Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post the COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.

We are here to implement a PDF trial Report copy as per your Research Requirement, also including impact analysis of COVID-19 on Global BPM-platform-based Case Management Software Market Size.

Global BPM-platform-based Case Management Software Market Regional and Country-wise Analysis:

North America (U.S., Canada, Mexico)

Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS)

Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)

Latin America (Brazil, Rest of Latin America)

The Middle East and Africa (Turkey, GCC, Rest of the Middle East and Africa)

Rest of the World....

Major factors covered in the report:

Global BPM-platform-based Case Management Software Market summary

Market Competition in terms of Manufacturers

Economic Impact on the Industry

Market Analysis by Application

Production, Revenue (Value), Price Trend by Type

Cost Investigation

Industrial Chain, Raw material sourcing strategy and Downstream Buyers

Production, Revenue (Value) by geographical segmentation

Marketing Strategy comprehension, Distributors and Traders

Global BPM-platform-based Case Management Software Market Forecast

Study on Market Research Factors

Key Highlights of the TOC provided by Syndicate Market Research:

Global BPM-platform-based Case Management Software Market Executive synopsis: This segment underscores the key investigations, market development rate, serious scene, market drivers, patterns, and issues notwithstanding the plainly visible pointers.

Global BPM-platform-based Case Management Software Market Study Coverage: It incorporates key market portions, key makers covered, the extent of items offered in the years considered, worldwide Global BPM-platform-based Case Management Software Market and study destinations. Also, it contacts the division study gave in the report based on the sort of item and applications.

Global BPM-platform-based Case Management Software Market Production by Region: The report conveys information identified with import and fare, income, creation, and central participants of all provincial business sectors contemplated are canvassed in this segment.

Global BPM-platform-based Case Management Software Market Profile of Manufacturers: Analysis of each market player profiled is itemized in this segment. This portion likewise gives SWOT examination, items, creation, worth, limit, and other crucial elements of the individual player.

The analysis objectives of the report are:

To know the Global BPM-platform-based Case Management Software Market size by pinpointing its sub-fragments.

To investigate the sum and estimation of the Global BPM-platform-based Case Management Software MarketMarket, contingent upon key districts

To contemplate the significant players and investigate their development plans.

To investigate the Global BPM-platform-based Case Management Software Market concerning development patterns, possibilities, and furthermore their cooperation in the whole area.

To inspect the Global BPM-platform-based Case Management Software Market size (volume and worth) from the organization, fundamental locales/nations, items and application, foundation data.

Essential overall Global BPM-platform-based Case Management Software Market fabricating organizations, to determine, explain, and break down the item deals sum, worth and piece of the pie, market competition scene, SWOT examination, and improvement plans for future.

To inspect serious advancement, for example, developments, courses of action, new item dispatches, and acquisitions available.

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Killexams : Managed Services Market Growing at a CAGR 7.9% | Key Player IBM, Fujitsu, Accenture, Atos, Cisco
Managed Services Market Growing at a CAGR 7.9% | Key Player IBM, Fujitsu, Accenture, Atos, Cisco

“IBM (US), Fujitsu (Japan), Accenture (Ireland), Atos (France), Cisco (US), DXC (US), TCS (India), Rackspace (US), AT&T (US), Verizon (US), Dimension Data (South Africa), Infosys (India), HCL (India), Ericssion (Sweden), GTT Communications (US), NTT Data (Japan), Happiest Minds (India), Huawei (China), Nokia Networks (Finland), CenturyLink (US), Wipro (India).”

Managed Services Market by Service Type (Managed Security, Managed Network, and Managed Data Center and IT Infrastructure), Vertical, Organization Size, Deployment Type, and Region (2022 – 2026)

The global Managed Services Market size is expected to grow at a Compound Annual Growth Rate (CAGR) of 7.9% during the forecast period, to reach USD 354.8 billion by 2026 from USD 242.9 billion in 2021. Major factors that are expected to drive the growth of the managed services market include lack of skilled IT professionals, rise in demand for secure IT infrastructure during the COVID-19 pandemic, cost and risk reduction, and requirements for regulatory compliance and security.

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Managed service vendors around the globe have increased their offerings in the managed services segment. The emergence of new technologies such as blockchain, AI, ML, and data analytics is helping MSPs to enhance their offerings and empower organizations. Enterprises require experts to guide them with their complex IT infrastructure. MSPs around the globe are helping organizations with different managed services such as managed security and managed networks. The objective of these managed services is to enhance and bolster different business verticals so that productivity can be improved and organizations can focus on their core businesses.

Managed Services Market Report Metrics

Report Metrics

Details

Market size available for years

2016-2026

Base year considered

2020

Forecast period

2021-2026

Forecast units

Value (USD)

Estimated Year Market Size

USD 242.9 billion in 2021

Forecast Year Market Size

USD 354.8 billion by 2026

Segments covered

Service Type, Deployment Model, Organization Size, Verticals, and Region

Estimated Year Market Size

USD 242.9 billion in 2021

Forecast Year Market Size

USD 354.8 billion by 2026

Regions covered

North America, Europe, APAC, MEA, and Latin America

Highest Growing Region

APAC (Asia Pacific)

Companies covered

IBM (US), Fujitsu (Japan), Accenture (Ireland), Atos (France), Cisco (US), DXC (US), TCS (India), Rackspace (US), AT&T (US), Verizon (US), Dimension Data (South Africa), Infosys (India), HCL (India), Ericssion (Sweden), GTT Communications (US), NTT Data (Japan), Happiest Minds (India), Huawei (China), Nokia Networks (Finland), CenturyLink (US), Wipro (India), Cognizant (US), Capgemini (France), BT (UK), Deloitte (UK), Secureworks (US), Alert Logic(US), BAE Systems (UK), Trustwave (US), Hughes (US), MeTtel (US), Microland (India), Optanix (US), Essintial (US), Intact Tech (US), 1-Net (Singapore), Ascend technologies (US), SecureKloud (India), Aunalytics (US), AC3 (Australia), Cloud certified (Australia), Corsica Technologies (US), and Empist (US).

Lack of IT skilled professionals, cost reduction and IT budget constraints, need for cloud-based managed services, high security monitoring to avoid high data loss and downtime cost, and enhanced business productivity are the major factors expected to drive the growth of the managed services market. The lack of sales and marketing staff, training, and cybersecurity could create challenges in front of MSPs during the forecast period. The major factor that may restrain the growth of the managed services market is increasing pressure from statutory regulations across the globe. However, high cloud adoption, the need for automation, and a continuous increase in the demand from SMEs are creating opportunities for MSPs

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The managed services market report includes major vendors, such as are IBM (US), Fujitsu (Japan), Accenture (Ireland), Atos (France), Cisco (US), DXC (US), TCS (India), Rackspace (US), AT&T (US), Verizon (US), Dimension Data (South Africa), Infosys (India), HCL (India), Ericssion (Sweden), GTT Communications (US), NTT Data (Japan), Happiest Minds (India), Huawei (China), Nokia Networks (Finland), CenturyLink (US), Wipro (India), Cognizant (US), Capgemini (France), BT (UK), Deloitte (UK), Secureworks (US), Alert Logic(US), BAE Systems (UK), Trustwave (US), Hughes (US), MeTtel (US), Microland (India), Optanix (US), Essintial (US), Intact Tech (US), 1-Net (Singapore), Ascend technologies (US), SecureKloud (India), Aunalytics (US), AC3 (Australia), Cloud certified (Australia), Corsica Technologies (US), and Empist (US). The major players in the managed services market have implemented various growth strategies to expand their global presence and increase their market shares. Key players such as Accenture, IBM, Fujitsu, Atos, and Cisco have majorly adopted many growth strategies, such as new services and product launches, acquisitions, and partnerships, to expand their service portfolios and grow further in the managed services market.

Atos is a global leader in secure and decarbonized digital with a range of market-leading digital solutions along with consultancy services, digital security and decarbonization offerings; an end-to-end partnership approach. It offers managed services and BPO, cloud operations, big data and cybersecurity solutions, and transactional services. It is the worldwide IT partner for the Olympic & Paralympic Games. SAP, Microsoft, Cisco, Oracle, and AWS are some of the leading partners of Atos. It offers various managed services which includes security services, public cloud, workplace services, infrastructure services, and digital video surveillance. The company offers its solutions and services through three business segments: infrastructure and data management, business and platform solutions, and big data and cybersecurity. Products and solutions offered by Atos include enterprise servers, data centers, and integrated systems, application development, big data, and analytics and consulting.

Atos has a strong presence around the globe with more than 105,000 employees and a presence in more than 70 countries. The company caters to various verticals such as manufacturing, BFSI, healthcare and life sciences, public sector and defense, telecommunications and media, and resources and services.

Cisco invests in R&D activities to offer its customers new and technologically advanced products and solutions, which would deliver maximum results with minimum spending. This tradition of innovation continues with industry-leading products and solutions in the company’s core development areas of routing and switching, as well as in advanced technologies such as home networking, IP telephony, optical networking, security, storage area networking, and wireless technology. In addition to its products, Cisco provides a broad range of service offerings, including technical support and advanced services. The company offers various technological assistance in the blockchain, security, and cloud. It also offers services, such as advisory, implementation, training, optimization, management, and technical, to enable customers to efficiently manage their businesses. These services are categorized into technical support services and advanced services. Technical support services aim at ensuring the operating efficiency of the products by keeping the system up to date with the latest application software. Advance services are offered for cloud, security, and analytics, which provide responsive, preventive, and consultative support to technologies related to networking. Cisco offers various managed services such as managed security services, managed data center services, managed network services, managed collaboration services, and managed workplace services.

Cisco has a worldwide presence with more than 77,000 employees and caters to clients operating in government, financial services, health, utility, communication, oil & gas, manufacturing, retail & consumer service, and transportation & logistics sectors.

IBM is a leading cloud platform and cognitive solutions company. It. The company’s major operating sector consists of five business segments, namely, cognitive solutions (AI), global business services, technology services and cloud platforms, systems, and global financing. IBM focuses on strengthening its product portfolio by launching new and advanced solutions in these sectors. It helps customers streamline business processes and enhance data-driven decision-making capabilities. It offers a broad product portfolio that includes Analytics, Intelligent Automation, Cloud Computing, Blockchain, Business Operations, IT Infrastructure, Mobile Technology, Security, Software Development, and Supply Chain Management. As of December 2020, the company has 345,000 employees.

IBM offers an array of services, including infrastructure services, outsourcing, application management services, Global Process Services (GPS), maintenance and support, consulting, and other managed services. It offers managed services such as enterprise application services, managed mobility services, workplace services, backup and recovery services, network services, security services, and storage services. IBM has expanded its managed services in the cloud services such as Infrastructure-as-a-service (IaaS), Platform-as-a-service (PaaS), and Software-as-a-service (SaaS).

IBM caters to various verticals, including automotive, telecommunications, financial services, health, aerospace & defense, insurance, life sciences, and retail. It nurtures an ecosystem of global business partners operating in more than 170 countries. IBM research constitutes the largest industrial research organization globally, with 12 labs across six continents spread across the Americas, Europe, MEA, and APAC.

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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 Improve 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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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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Killexams : Data Fabric Industry 2026 | Growth Drivers And Future Outlook | IBM, Netapp, Oracle, Denodo Technologies

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Data Fabric Industry

The data fabric market report focuses on the growth prospects, restraints, and market analysis.

PORTLAND , PORTLAND, OR, UNITED STATE, July 20, 2022 /EINPresswire.com / -- Rise in volume & variety of business data, increase in need for business agility & data accessibility, and growing demand for real-time streaming analytics drive the growth of the global data fabric industry .

On the other hand, lack of awareness associated to data fabric impedes the growth to some extent. Nevertheless, significant data growth in developing regions is expected to create a number of opportunities for the key players in the industry.

According to the report published by Allied Market Research, the global data fabric market was pegged at $812.6 million in 2018 and is estimated to hit $4.54 billion by 2026, registering a CAGR of 23.8% from 2019 to 2026. The report provides an in-depth analysis of the top investment pockets, top winning strategies, drivers & opportunities, market size & estimations, competitive landscape, and changing market trends.

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Based on type, the disk-based data fabric segment held the major share in 2018, generating three-fifths of the global data fabric market. Growing demand for data fabric solutions among data centers and storage enterprises owing to its ability to integrate and operate in unified environment is expected to drive the growth of the segment in the global market. The in-memory data fabric segment, on the other hand, would showcase the fastest CAGR of 26.1% throughout the estimated period. This is attributed to its ability to perform parallel computing.

Based on deployment, the on-premise segment contributed to nearly two-thirds of the global data fabric market share in 2018, and is expected to retain its dominance by 2026. High number of data centers boosts the growth of the segment. At the same time, the cloud segment would cite the fastest CAGR of 26.9% during 2019–2026. Rise in adoption of cloud deployments especially in developing countries fuels the growth of the segment.

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Based on geography, North America contributed to more than two-fifths of the global data fabric market revenue in 2018, and is anticipated to maintain the lion's share by 2016. North American countries are expected to adopt data fabric solutions at a high rate due to its compatible infrastructure. Simultaneously, the region across Asia-Pacific would register the fastest CAGR of 26.0% during the study period. This is due to due to the presence of high penetration connected devices in the region.

Impact of Covid-19 on Data Fabric Market:

•With adoption of“work-from-home” approach by organizations operating in the IT and other sectors during the lockdown, the demand for data fabric has been increased significantly.

•Many organizations have undergone digital transformation to ensure business continuity and avail data accessibility from anywhere. This augmented the implementation of data fabric technology.

• The healthcare sector has adopted this technology in a rapid pace to carry out remote monitoring and consultation of patients suffering various conditions as hospital and clinic visits have been restricted to extreme cases only.

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The leading market players analyzed in the global data fabric market report include Talend, Global IDs., Hewlett Packard Enterprise Company, Splunk Inc., Denodo Technologies, IBM Corporation, Oracle Corporation, NetApp, SAP SE, and Software AG. These market players have adopted different strategies including partnership, expansion, collaboration, joint ventures, and others to reinforce their status in the industry.

Key Benefits for Data Fabric Market:

•This study includes the market analysis, trends, and future estimations to determine the imminent investment pockets.

•The report presents information related to key drivers, restraints, and opportunities of the market.

•The data fabric market forecast is quantitatively analyzed from 2018 to 2026 to highlight the financial competency of the industry.

•Porter's five forces analysis illustrates the potency of the buyers & suppliers in the data fabric market.

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Thanks for memorizing this article; you can also get an individual chapter-wise section or region-wise report versions like North America, Europe, or Asia.

If you have any special requirements, please let us know and we will offer you the report as per your requirements.

Lastly, this report provides market intelligence most comprehensively. The report structure has been kept such that it offers maximum business value. It provides critical insights into the market dynamics and will enable strategic decision-making for the existing market players as well as those willing to enter the market.

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1. Data Catalog Market

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