The document discusses 5 trends in IoT and edge computing to track in 2019: 1) The growth of multi-locational hybrid data architectures to store data locally at the edge as well as in the cloud; 2) Increased convergence of facial analysis and machine learning, such as using cameras to detect driver fatigue; 3) The emergence of data marketplaces as enterprises purchase IoT data streams; 4) The convergence of IoT and blockchain to provide data provenance and immutability; 5) The rise of autonomous edge devices with local storage, ML models, and the ability to take actions without cloud latency.
Will Edge Computing IoT Solutions be a Real Trend in 2019?Tyrone Systems
Edge computing is a method of optimizing Internet of Things applications by performing data processing at the edge of the network, near the source of the data. Edge computing IoT technology is attracting huge investments, to ensure security, ruggedness and establish ROI driven use cases.
Edge computing is redefining the cloud computing space. The growing de-emphasis on the cloud’s role in connected environments is expected to lead to smarter and faster autonomous solutions that have the potential to reshape the IoT landscape. Edge computing will transform the IoT landscape into a hyperconnected environment where the restrictions related to latency and computation capacity will be eliminated. Many companies are transforming their business models to attain edge computing capabilities necessary for offering end to end services.
The recent years have witnessed a number of mergers and acquisitions in the edge computing space for IoT services, with the increase in M&A activities representing the industry’s conundrum of cloud, edge, and hybrid architectures, and the race to achieve a considerable market share.
This report includes an analysis of approximately 60 deals, along with a detailed technology overview and the purpose of the acquisitions. The M&A analysis section offers a comprehensive view of the transactions around edge computing, covering different technology aspects including data center, AI, security, software-defined WAN (SD-WAN), analytics, interoperability, multi-access edge computing (MEC), and others.
To purchase the full report, write to us at info@netscribes.com
Edge Computing: An Extension to Cloud ComputingRamneek Kalra
This presentation was shared by Shally Gupta (PhD Research Scholar | IEEE Graduate Member) & Ramneek Kalra (IEEE Impact Creator) at IEEE MRU Student Branch, Faridabad, Haryana, India.
The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.
Edge computing is becoming a key architectural component for industrial IoT deployments. Gartner Group identifies edge computing as one of their top Tech Trends for 2019. The opportunity to process data at the edge of the network, closer to the sensors and actuators, before data is sent to the cloud results in improved security, more efficient data movement, and better performance for industrial IoT use cases.
This presentation will explore three aspects of edge computing:
The benefits of edge computing for industrial IoT use cases
The key features delivered in edge computing solutions
A survey of different edge computing options available to customers.
Edge computing allows data produced by internet of things (IoT) devices to be processed closer to where it is created instead of sending it across long routes to data centers or clouds.
Doing this computing closer to the edge of the network lets organizations analyze important data in near real-time – a need of organizations across many industries, including manufacturing, health care, telecommunications and finance.Edge computing deployments are ideal in a variety of circumstances. One is when IoT devices have poor connectivity and it’s not efficient for IoT devices to be constantly connected to a central cloud.
Other use cases have to do with latency-sensitive processing of information. Edge computing reduces latency because data does not have to traverse over a network to a data center or cloud for processing. This is ideal for situations where latencies of milliseconds can be untenable, such as in financial services or manufacturing.
Through this presentation, you will get to know about Edge computing and explore the fields where it is needed.
You can start exploring the technical knowledge by seeing what industries are working on now-days
Will Edge Computing IoT Solutions be a Real Trend in 2019?Tyrone Systems
Edge computing is a method of optimizing Internet of Things applications by performing data processing at the edge of the network, near the source of the data. Edge computing IoT technology is attracting huge investments, to ensure security, ruggedness and establish ROI driven use cases.
Edge computing is redefining the cloud computing space. The growing de-emphasis on the cloud’s role in connected environments is expected to lead to smarter and faster autonomous solutions that have the potential to reshape the IoT landscape. Edge computing will transform the IoT landscape into a hyperconnected environment where the restrictions related to latency and computation capacity will be eliminated. Many companies are transforming their business models to attain edge computing capabilities necessary for offering end to end services.
The recent years have witnessed a number of mergers and acquisitions in the edge computing space for IoT services, with the increase in M&A activities representing the industry’s conundrum of cloud, edge, and hybrid architectures, and the race to achieve a considerable market share.
This report includes an analysis of approximately 60 deals, along with a detailed technology overview and the purpose of the acquisitions. The M&A analysis section offers a comprehensive view of the transactions around edge computing, covering different technology aspects including data center, AI, security, software-defined WAN (SD-WAN), analytics, interoperability, multi-access edge computing (MEC), and others.
To purchase the full report, write to us at info@netscribes.com
Edge Computing: An Extension to Cloud ComputingRamneek Kalra
This presentation was shared by Shally Gupta (PhD Research Scholar | IEEE Graduate Member) & Ramneek Kalra (IEEE Impact Creator) at IEEE MRU Student Branch, Faridabad, Haryana, India.
The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.
Edge computing is becoming a key architectural component for industrial IoT deployments. Gartner Group identifies edge computing as one of their top Tech Trends for 2019. The opportunity to process data at the edge of the network, closer to the sensors and actuators, before data is sent to the cloud results in improved security, more efficient data movement, and better performance for industrial IoT use cases.
This presentation will explore three aspects of edge computing:
The benefits of edge computing for industrial IoT use cases
The key features delivered in edge computing solutions
A survey of different edge computing options available to customers.
Edge computing allows data produced by internet of things (IoT) devices to be processed closer to where it is created instead of sending it across long routes to data centers or clouds.
Doing this computing closer to the edge of the network lets organizations analyze important data in near real-time – a need of organizations across many industries, including manufacturing, health care, telecommunications and finance.Edge computing deployments are ideal in a variety of circumstances. One is when IoT devices have poor connectivity and it’s not efficient for IoT devices to be constantly connected to a central cloud.
Other use cases have to do with latency-sensitive processing of information. Edge computing reduces latency because data does not have to traverse over a network to a data center or cloud for processing. This is ideal for situations where latencies of milliseconds can be untenable, such as in financial services or manufacturing.
Through this presentation, you will get to know about Edge computing and explore the fields where it is needed.
You can start exploring the technical knowledge by seeing what industries are working on now-days
Edge Computing often misquoted, misrepresented, and misunderstood. In this presentation, I tried to demystify what the Edge Computing is and what role the edge computing plays in the Connected World.
THE ROLE OF EDGE COMPUTING IN INTERNET OF THINGSsuthi
Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.
Edge computing is a method of enabling small processing units near to the source of the data from sensors and central data servers. It utilizes cloud computing systems by performing data processing at the edge of the network, near the source of the data. This reduces the communications bandwidth needed between sensors by performing analytics and data processing.
Edge IoT is a technology Witekio believes in. It is now reaching an inflexion point. The need for responsiveness, local computing capacity (especially for data crunching, AI and machine learning), security, IoT bandwidth makes this«trend » relevant to face B2B and industrial challenges.
The Internet-of-Things provides us with lots of sensor data. However, the data by themselves do not provide value unless we can turn them into actionable, contextualized information. Big data and data visualization techniques allow us to gain new insights by batch-processing and off-line analysis. Real-time sensor data analysis and decision-making is often done manually but to make it scalable, it is preferably automated. Artificial Intelligence provides us the framework and tools to go beyond trivial real-time decision and automation use cases for IoT.
A Survey on Security and Privacy Issues in Edge Computing-Assisted Internet o...DESMOND YUEN
Internet of Things (IoT) is an innovative paradigm
envisioned to provide massive applications that are now part of
our daily lives. Millions of smart devices are deployed within
complex networks to provide vibrant functionalities including
communications, monitoring, and controlling of critical infrastructures. However, this massive growth of IoT devices and the corresponding huge data traffic generated at the edge of the network created additional burdens on the state-of-the-art
centralized cloud computing paradigm due to the bandwidth and
resources scarcity. Hence, edge computing (EC) is emerging as
an innovative strategy that brings data processing and storage
near to the end users, leading to what is called EC-assisted IoT.
Although this paradigm provides unique features and enhanced
quality of service (QoS), it also introduces huge risks in data security and privacy aspects. This paper conducts a comprehensive survey on security and privacy issues in the context of EC-assisted IoT. In particular, we first present an overview of EC-assisted IoT including definitions, applications, architecture, advantages, and challenges. Second, we define security and privacy in the context of EC-assisted IoT. Then, we extensively discuss the major classifications of attacks in EC-assisted IoT and provide possible solutions and countermeasures along with the related research efforts. After that, we further classify some security and privacy issues as discussed in the literature based on security services and based on security objectives and functions. Finally, several open challenges and future research directions for secure EC-assisted IoT paradigm are also extensively provided.
Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data[1]. This reduces the communications bandwidth needed between sensors and the central datacenter by performing analytics and knowledge generation at or near the source of the data.
Interview: What is the main security and privacy risks associated with the ad...Ersin KARA
worldautomotiveconference.co.uk
"The methods of artificial intelligence and augmented reality have always been the substance of rumination and speculation since very recently, where they’ve started to take very a central role in our lives.
Intelligent technologies today are computer-aided systems that completely control all industrial pipelines. They can operate autonomously and on this account all processes can be managed independently.
Today’s logistics do not resemble one-way storage of goods seen up to a few years ago. This is due to new web technologies that allow an entirely new level of interaction within the moving parts of a given logistics eco-system. As these technologies continue developing at a rapid pace, several partially and fully automated logistic frameworks are already readying for deployment."
"When we compare Industry 4.0 advantages and classic ERP programs advantages We see below points ;
- Space-efficient storage. This will save in warehouse areas and volumes. Ex. Kardex Remstar applications, vertical storage solutions
- ERP’s are integrated warehouse management software.
So the error will be absolutely minimal. Prevention of losses due to lack of communication in monolithic systems that have one point of failure.
- Automatic and controlled product circulation. This will allow for increased work safety and fewer work accidents. This will naturally result in risk reduction resulting from controllability, especially in hazardous material logistics.
- Line feed, standby modules. So perfect stock management, “0” inventory loss.
- Automatic finished product warehouses. This will allow for unmanned warehouses, fast vehicle loading and unloading systems that can work 24 hours a day, 365 days a year. Cellular transfer storage systems.
For distribution centers and warehouse management systems that implement Industry 4.0 technologies, data needs to be collected, analyzed, acted on, and secured in order to partake in the data driven decision-making Industry 4.0 advertises."
Edge Computing often misquoted, misrepresented, and misunderstood. In this presentation, I tried to demystify what the Edge Computing is and what role the edge computing plays in the Connected World.
THE ROLE OF EDGE COMPUTING IN INTERNET OF THINGSsuthi
Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.
Edge computing is a method of enabling small processing units near to the source of the data from sensors and central data servers. It utilizes cloud computing systems by performing data processing at the edge of the network, near the source of the data. This reduces the communications bandwidth needed between sensors by performing analytics and data processing.
Edge IoT is a technology Witekio believes in. It is now reaching an inflexion point. The need for responsiveness, local computing capacity (especially for data crunching, AI and machine learning), security, IoT bandwidth makes this«trend » relevant to face B2B and industrial challenges.
The Internet-of-Things provides us with lots of sensor data. However, the data by themselves do not provide value unless we can turn them into actionable, contextualized information. Big data and data visualization techniques allow us to gain new insights by batch-processing and off-line analysis. Real-time sensor data analysis and decision-making is often done manually but to make it scalable, it is preferably automated. Artificial Intelligence provides us the framework and tools to go beyond trivial real-time decision and automation use cases for IoT.
A Survey on Security and Privacy Issues in Edge Computing-Assisted Internet o...DESMOND YUEN
Internet of Things (IoT) is an innovative paradigm
envisioned to provide massive applications that are now part of
our daily lives. Millions of smart devices are deployed within
complex networks to provide vibrant functionalities including
communications, monitoring, and controlling of critical infrastructures. However, this massive growth of IoT devices and the corresponding huge data traffic generated at the edge of the network created additional burdens on the state-of-the-art
centralized cloud computing paradigm due to the bandwidth and
resources scarcity. Hence, edge computing (EC) is emerging as
an innovative strategy that brings data processing and storage
near to the end users, leading to what is called EC-assisted IoT.
Although this paradigm provides unique features and enhanced
quality of service (QoS), it also introduces huge risks in data security and privacy aspects. This paper conducts a comprehensive survey on security and privacy issues in the context of EC-assisted IoT. In particular, we first present an overview of EC-assisted IoT including definitions, applications, architecture, advantages, and challenges. Second, we define security and privacy in the context of EC-assisted IoT. Then, we extensively discuss the major classifications of attacks in EC-assisted IoT and provide possible solutions and countermeasures along with the related research efforts. After that, we further classify some security and privacy issues as discussed in the literature based on security services and based on security objectives and functions. Finally, several open challenges and future research directions for secure EC-assisted IoT paradigm are also extensively provided.
Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data[1]. This reduces the communications bandwidth needed between sensors and the central datacenter by performing analytics and knowledge generation at or near the source of the data.
Interview: What is the main security and privacy risks associated with the ad...Ersin KARA
worldautomotiveconference.co.uk
"The methods of artificial intelligence and augmented reality have always been the substance of rumination and speculation since very recently, where they’ve started to take very a central role in our lives.
Intelligent technologies today are computer-aided systems that completely control all industrial pipelines. They can operate autonomously and on this account all processes can be managed independently.
Today’s logistics do not resemble one-way storage of goods seen up to a few years ago. This is due to new web technologies that allow an entirely new level of interaction within the moving parts of a given logistics eco-system. As these technologies continue developing at a rapid pace, several partially and fully automated logistic frameworks are already readying for deployment."
"When we compare Industry 4.0 advantages and classic ERP programs advantages We see below points ;
- Space-efficient storage. This will save in warehouse areas and volumes. Ex. Kardex Remstar applications, vertical storage solutions
- ERP’s are integrated warehouse management software.
So the error will be absolutely minimal. Prevention of losses due to lack of communication in monolithic systems that have one point of failure.
- Automatic and controlled product circulation. This will allow for increased work safety and fewer work accidents. This will naturally result in risk reduction resulting from controllability, especially in hazardous material logistics.
- Line feed, standby modules. So perfect stock management, “0” inventory loss.
- Automatic finished product warehouses. This will allow for unmanned warehouses, fast vehicle loading and unloading systems that can work 24 hours a day, 365 days a year. Cellular transfer storage systems.
For distribution centers and warehouse management systems that implement Industry 4.0 technologies, data needs to be collected, analyzed, acted on, and secured in order to partake in the data driven decision-making Industry 4.0 advertises."
Managing Data To Drive Competitive Advantage Bernard Marr
One of the most important lessons we've learned from the pandemic is just how important technology is to building robust and thriving businesses. Organizations that have prospered in the last nine months have done so by leveraging cloud computing, high-speed networks, artificial intelligence (AI), and the internet of things (IoT) to push forward their digital transformation agendas.
How IoT In Automotive Industry Is Transforming Smart CarsMindfire LLC
The Internet of Things is rapidly influencing every sphere of our lives. IoT in Automotive Industry is seeing one such rapid growth. As of 2020, an article by Deloitte cites that over 20 billion IoT devices are in use.
Overtly, the connected vehicle is the most recent embodiment of IoT technology. While automotive engineers and software developers both claim responsibility for this success, the real power behind the wheels lies with the IoT service providers.
Wearable Technology: Automotive's Next Digital FrontierCognizant
Wearables promise to impact the automotive value chain in a similar way to smartphones. But despite their great promise, wearables also lack proven use cases, requiring that companies proceed cautiously while ignoring wearables at their own peril.
The 5 Biggest Computer Vision Trends In 2022Bernard Marr
Computer vision (sometimes called machine vision) is one of the most exciting applications of artificial intelligence. Here we look at the five biggest trends in this fast-developing area.
The future is in the cloud, or at least it's migrating there. Offering scalability, flexibility and agility, the cloud is the obvious solution for businesses seeking to make sense of the deluge of data. Cloud services can also help companies meet sustainability goals and even cut costs. But cloud strategies need to be carefully crafted to avoid the risks of remote storage and realise the potential of cloud-enabled efficiencies.
Digital disruption and the future of the automotive industryPeter Tutty
Digital services centered on increasingly empowered consumers will bring disruption to the automotive industry.
Economic value within this industry and across adjacent markets will be forever altered. In a world where the future is far from certain, automotive companies will need to develop new core capabilities to survive.
What is going to happen next and how to respond? Download the report or explore the infographic, below.
Digital disruption and the future of the automotive industryPeter Tutty
Digital services centered on increasingly empowered consumers will bring disruption to the automotive industry.
Economic value within this industry and across adjacent markets will be forever altered. In a world where the future is far from certain, automotive companies will need to develop new core capabilities to survive.
What is going to happen next and how to respond? Download the report or explore the infographic, below.
VMblog - 2020 IT Predictions from 26 Industry Expertsvmblog
Find out what's going on in the world of #artificialintelligence, #machinelearning, #cloud, #kubernetes, #containers, #virtualization, #security, #disasterrecovery, #networking, #data and so much more in 2020. Read these #predictions from 26 of the industry's leading experts to learn more! Hear from industry thought leaders from companies like Altaro, Citrix, Commvault, Datacore, IGEL, Kaspersky, Liquidware, SolarWinds, Veeam, Vembu, VMware and more. And make sure to also read the more than 430+ other expert predictions here: http://bit.ly/2QVorPI at VMblog.com.
TADSummit, Simfony: Building a Global IoT Service Provider using Programmable...Alan Quayle
Simfony: Building a Global IoT Service Provider using Programmable Telecoms
Stefan Anghel, Product Architect, Simfony Mobile
Where does IoT make sense? Cutting through the hype to real business and real solutions.
Review of the IoT Landscape, understanding the enablers and ecosystem.
Simfony's IoT Platform: an M2M focused MVNO. Delivering solutions to business problems.
The Future of IoT service providers.
If you interested in the Internet of Things(IoT) then this presentation is all about IoT. How the implementation of IoT systems in industries or factories increases growth. How IoT systems help in business growth and reduce operational costs and reduce time.
As more and more enterprises look at leveraging the capabilities of public clouds, they face an array of important decisions. for example, they must decide which cloud(s) and what technologies they should use, how they operate and manage resources, and how they deploy applications.
Design and Optimize your code for high-performance with Intel® Advisor and I...Tyrone Systems
For all that we’re unable to attend or would like to recap our live webinar Unleash the Secrets of Performance Profiling with Intel® oneAPI Profiling Tools, all the resources you need are available to you!
Learn about locating and removing bottlenecks is an inherent challenge for every application developer. And it’s made more complex when porting an app to a new platform (say, from a CPU to a GPU). Developers must not only identify bottlenecks; they must figure out which parts of the code will benefit from offloading in the first place. This webinar will focus on how to do just that using two profiling tools from Intel: Intel® VTune Amplifier and Intel Advisor.
How can Artificial Intelligence improve software development process?Tyrone Systems
Artificial intelligence has impacted retail, finance, healthcare and many industries around the world. It has transformed the way the software industry functions. With the help of the below SlideShare, let's explore how can Artificial Intelligence improve software development process:
Four ways to digitally transform with HPC in the cloudTyrone Systems
As cloud computing rapidly becomes better, faster, and cheaper than on-premises, no workload will be left untouched, and companies will need to adapt it to remain competitive over the next decade and beyond. So what is the cloud transformation in HPC? Why are on-premises HPC systems not enough anymore? Check out this slideshare to know more.
At Netweb we believe that innovation is a critical business need. As data analytics, high-performance computing and artificial intelligence continue to evolve, we are building solutions and to help you keep pace with the constantly evolving landscape.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. Based on the industry use case, the storage
may be on-premises or on the cloud or even
have a hybrid model. However, in the last
couple of years, the edge is no longer just a
data-generation medium.
The challenges around network latency and
the need for immediate real-time
insights have pushed enterprises to evolve the
edge to be smarter. To enable this, in some
use cases in specific verticals, there is a new
need to store data locally at the edge as well.
This leads to the evolution of a new model of
multi-locational hybrid data architectures.
GROWTH OF
MULTI-LOCATIONAL
DATA STORAGE
3. Over the last couple of years, we have seen
limited success with video cameras capturing
and understanding human sentiment using
facial analysis for use cases across retail for
customer engagement.
For example: The camera inside a truck
watching the driver’s actions and facial
movements. It can quickly detect fatigue on
the driver’s face and alert them immediately.
Another requirement was to understand how
many times the driver is taking their eyes off
the road and is distracted with the radio.
The idea is to increase driver safety and
prevent loss of life or property in their fleet.
INCREASED
CONVERGENCE OF
FACIAL ANALYSIS
AND MACHINE
LEARNING
4. With the hyper-growth in data production
potentially reaching up to 163 zettabytes in
just a few years, and with IoT and streaming
data being a major contributor to that, every
enterprise is sitting on goldmines of data. As
vertical data ecosystems emerge as has been
evident in how insurance companies tap into
connected cars for driving history of its
subscribers.
The need for refining and building more
sophisticated and more reliable ML models
will warrant the need for more data. So,
naturally, enterprises will start purchasing
data – even subscribe to IoT data streams
directly.
EMERGENCE
OF DATA
MARKETPLACES
5. One of the key elements of streaming data
that we tend to overlook is the importance of
data provenance and lineage tracking. We
build our businesses amidst so many
compliance laws and regulations and it is only
fair that we keep track of the data – its origin,
the personas that handle it, the varying values
through the data chain etc.
With trust and data immutability being critical
requirements in this model, blockchain will be
an inherent part of this architecture.
CONVERGENCE OF
IOT AND
BLOCKCHAIN IS
INEVITABLE
6. As the realization dawns on us that device
autonomy is already a reality in a few
verticals today, we should start preparing for
accepting the autonomous edge for many
more use cases across automotive,
healthcare, retail, manufacturing etc.
When the use case cannot afford the cost of
latency for the sake of detailed analysis, the
edge has to become smarter, self-reliant, has
access to local storage, has a set of rules
(and a light-weight rules engine too maybe),
has ML models to score the data and even has
the capability to fire off a set of actions –
potentially involving other robotic devices.
RISE OF THE
AUTONOMOUS EDGE
7. T H A N K Y O U !
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