As part of the 2018 HPCC Systems Summit Community Day event:
This is a proof-of-concept where an HPCC Systems cluster is used to gather current IoT device data from opt-in subscribers. The cluster's architecture and collected data will be described in the presentation, as well as the additional datasets (e.g. property characteristics, weather, etc.) brought in to enhance the data for analysis using predictive analytics for potential applications in the insurance industry.
Dan Camper has been with LexisNexis Risk for four years and is a Senior Architect in the Solutions Lab Group. He has worked for Apple and Dun & Bradstreet, and he ran his own custom programming shop for a decade. He's been writing software professionally for over 35 years and has worked on a myriad of systems, using a lot of different programming languages. He thinks ECL is pretty neat.
Hicham Elhassani is VP Modeling with LexisNexis Risk Solutions.
This document provides an overview of Google Cloud Platform services for IoT and big data analytics, including fully managed ingestion, processing, and analysis of IoT data. It introduces Google Cloud Pub/Sub for messaging, Cloud Dataflow for stream and batch data processing, and BigQuery for petabyte-scale data warehousing and analysis. The presentation includes demos of building an event streaming pipeline using these services to ingest data from Pub/Sub, process it in Dataflow, and analyze results in BigQuery.
This document discusses how the Internet of Things (IoT) will impact businesses. It provides statistics on the growing number of connected devices, with predictions of 50 billion connected devices by 2020. IoT can enable predictive maintenance and reduce costs for facilities management in areas like power, water and fire systems. IoT is also discussed in the context of healthcare, with the potential to improve clinical care through remote patient monitoring and reducing costs through greater efficiency. The overall economic impact of IoT is estimated to reach $11 trillion by 2025 according to one source cited in the document.
The document discusses Internet of Things (IoT) solutions using Red Hat technologies. It defines IoT as the collection of smart devices connected through the internet or cloud that transmit data for analysis. It also defines an Intelligent System as the technology architecture for an IoT solution. It then lists key components of Red Hat's IoT platform including middleware, storage, Linux, virtualization, and mobile technologies. Several examples of real-world IoT use cases involving oil, smart metering, digital cable TV are also mentioned. Finally, it discusses important considerations for IoT solutions like security, messaging, event processing, storage, scalability and application development.
4 Applications of Digital Twin in HealthcareTyrone Systems
In this space, we’ve been discussing the possibilities of using IoT applications to solve costs and revenue leakage for healthcare providers, and to streamline clerical tasks that can often reduce facetime healthcare practitioners have with their patients.
The power of IoT is truly realized when data from the real-world is securely transformed to the digital realm—this is commonly referred to as creating “digital twins.” By “instrumenting” the real-world to provide near real-time data, and by applying the powerful tools and methods of the analytical and artificial intelligence fields, digital twins can speed the creation of new and revolutionary healthcare IoT applications.
7 facts, fictions and predictions about the Internet of Things (IoT) Parveen Goel
The Internet of Things (IoT) refers to combinations of technologies that are enabling tracking, coordinating and controlling of physical objects such as smart watches, thermostats, medical devices, appliances, machines, electric grid, traffic control systems and many other via internet and software applications. When an object is connected and communicated via network it opens up potential for making decisions in real time and more optimally
How Mentor Graphics Uses Google Cloud for the Internet of Things - Google Clo...RightScale
Mentor Graphics is building on its expertise in mobile to provide a cloud services platform for the Internet of Things (IoT). EZmobilePrint, one of the first apps on the platform, lets mobile devices connect to printers in coffee shops or other public places. Mentor Graphics leverages Google Cloud Platform and RightScale to provide automated, scalable infrastructure to power this next-generation IoT platform and many more applications to come.
This document discusses Internet of Things (IoT) concepts including the IoT value chain and challenges of IoT implementation. It then summarizes a case study of a Logicalis solution for waste collection optimization using an IoT platform. The solution resulted in 30% reductions in kilometers traveled, time spent, bins visited and operational costs as well as 30% less CO2 emissions. Finally, the document introduces Eugenio, an IoT platform from Logicalis, and provides contact information for any questions.
This document provides an overview of Google Cloud Platform services for IoT and big data analytics, including fully managed ingestion, processing, and analysis of IoT data. It introduces Google Cloud Pub/Sub for messaging, Cloud Dataflow for stream and batch data processing, and BigQuery for petabyte-scale data warehousing and analysis. The presentation includes demos of building an event streaming pipeline using these services to ingest data from Pub/Sub, process it in Dataflow, and analyze results in BigQuery.
This document discusses how the Internet of Things (IoT) will impact businesses. It provides statistics on the growing number of connected devices, with predictions of 50 billion connected devices by 2020. IoT can enable predictive maintenance and reduce costs for facilities management in areas like power, water and fire systems. IoT is also discussed in the context of healthcare, with the potential to improve clinical care through remote patient monitoring and reducing costs through greater efficiency. The overall economic impact of IoT is estimated to reach $11 trillion by 2025 according to one source cited in the document.
The document discusses Internet of Things (IoT) solutions using Red Hat technologies. It defines IoT as the collection of smart devices connected through the internet or cloud that transmit data for analysis. It also defines an Intelligent System as the technology architecture for an IoT solution. It then lists key components of Red Hat's IoT platform including middleware, storage, Linux, virtualization, and mobile technologies. Several examples of real-world IoT use cases involving oil, smart metering, digital cable TV are also mentioned. Finally, it discusses important considerations for IoT solutions like security, messaging, event processing, storage, scalability and application development.
4 Applications of Digital Twin in HealthcareTyrone Systems
In this space, we’ve been discussing the possibilities of using IoT applications to solve costs and revenue leakage for healthcare providers, and to streamline clerical tasks that can often reduce facetime healthcare practitioners have with their patients.
The power of IoT is truly realized when data from the real-world is securely transformed to the digital realm—this is commonly referred to as creating “digital twins.” By “instrumenting” the real-world to provide near real-time data, and by applying the powerful tools and methods of the analytical and artificial intelligence fields, digital twins can speed the creation of new and revolutionary healthcare IoT applications.
7 facts, fictions and predictions about the Internet of Things (IoT) Parveen Goel
The Internet of Things (IoT) refers to combinations of technologies that are enabling tracking, coordinating and controlling of physical objects such as smart watches, thermostats, medical devices, appliances, machines, electric grid, traffic control systems and many other via internet and software applications. When an object is connected and communicated via network it opens up potential for making decisions in real time and more optimally
How Mentor Graphics Uses Google Cloud for the Internet of Things - Google Clo...RightScale
Mentor Graphics is building on its expertise in mobile to provide a cloud services platform for the Internet of Things (IoT). EZmobilePrint, one of the first apps on the platform, lets mobile devices connect to printers in coffee shops or other public places. Mentor Graphics leverages Google Cloud Platform and RightScale to provide automated, scalable infrastructure to power this next-generation IoT platform and many more applications to come.
This document discusses Internet of Things (IoT) concepts including the IoT value chain and challenges of IoT implementation. It then summarizes a case study of a Logicalis solution for waste collection optimization using an IoT platform. The solution resulted in 30% reductions in kilometers traveled, time spent, bins visited and operational costs as well as 30% less CO2 emissions. Finally, the document introduces Eugenio, an IoT platform from Logicalis, and provides contact information for any questions.
Technology that is going to create a revolution in every Industry including Health care. What is it, what are the tools and what is the outcome?
NASA started the research on Twins due to space travel and the need to have real time feedback of components. Now it is extending to even Health care to having a Human twin.
Big Data in IoT & Deep Learning
Challenges of IoT Big Data Analytics Applications
Challenges of Cloud-based IoT Platform
Cloud-based IoT Platform Use Case: GE Predix for Smart Building Energy Management
Fog/Edge Computing & Micro Data Centers
Deep Learning for IoT Big Data Analytics Introduction
Deep Learning for IoT Big Data Analytics Use Case
Distributed Deep Learning
Big Data + IoT + Cloud + Deep Learning Insights from Patents
Big Data + IoT + Cloud + Deep Learning Strategy Development
Designing Data-Intensive Applications
Xanadu Functionality
Xanadu Use Case
Xanadu + Deep Learning + Hadoop Integration
From the Testers: Measuring for Energy Efficiency and Energy LabelingIRS srl
New energy efficiency standards and labeling are enforced throughout the world. At this session, hear appliance and HVAC test system experts share why you need to pay special attention to sensor selection and system integration. The NI modular platform can lower complexity, reduce development time, and add machine learning to the task.
A digital twin is a virtual representation of a physical object that can be used to help optimize business decisions. The webinar discusses the history and definition of digital twins, how they are enabled by technologies like IoT, and how companies are using them. Digital twins allow a physical object and its virtual counterpart to be connected, providing a closed loop between the simulated and physical worlds to improve conceptualization, comparison, and remote collaboration.
The Internet of Things - beyond the hype and towards ROIPerry Lea
How do you move beyond the hype of IoT and towards profitability? This short lecture examines the hype and origin of IoT and the reality of the industry. It then talks about my experiences with the industry, customers, and technologists. Some have outright failed in IoT projects, others are succeeding.
Get beyond the prototype and lab experiment.
Cognitive Digital Twin by Fariz SaračevićBosnia Agile
Data are driving the world today and they are becoming world's precious currency. Continuous Engineering, the default set of applications for enterprise software development, produce a wealth of data but it is hard to understand its value. What if you could find hidden patterns in your data your development teams create? What if you could discover ways to improve your team's performance? This presentation reviewed some of the different ways the Collaborative Lifecycle Management team (http://jazz.net) is utilizing Watson Analytics to gain insights into and improve efficiency with their own processes.
I gave this talk at the 'Digital Twin Conference' hosted by LH Corp at COEX, Seoul on August 8th, 2019.
Abstract: 'Digital Twin' is a digital replication of real world objects, processes, phenomena that can be used for various purposes. Digital twin concept backs to manufacturing industry in early 2000s for the PLM (Product Lifecycle Management) purposes. It is based on the idea that a digital informational construct about a physical system could be created as an entity on its own. Definitions of digital twin emphasize the three important levels or characteristics. At first, there should be connection between real physical world and corresponding virtual world. To do this, Level 1 digital twin provides virtual 3D models. Secondly, this connection between real world and virtual world is established by generating (near) real time data using sensors or IoT. This is called Level 2 digital twin. Thirdly, Level 3 digital twin carries out certain analyses, predictions, and simulations using virtual 3D and (near) real time data. ‘Smart Spaces’ are interactive environments where humans and technology can openly communicate with each other in a physical or digital setting. Examples of smart spaces include smart cities, smart factories, and smart homes. ‘Smart Spaces’ is one of Garner’s Top 10 Tech Trends for 2019. As spaces are going through digital transformation with 4th industrial revolution, there are many attempts to apply digital twin technology to manage urban, spatial, and industrial issues around the world. Those attempts look set to play an increasingly important role in the creation of smart cities, smart factories, and smart homes. Bringing the virtual and real worlds together in this way can help to give better analysis, visualization, and simulation to the decision-making process. This will be a multi-way process with iterative feedback among stakeholders.
In this talk, I'll share my real experiences in carrying out digital twin and smart space projects. Also I’ll talk about what I’ve learnt from these projects.
Big Data in IoT & Deep Learning
Challenges of IoT Big Data Analytics Applications
Challenges of Cloud-based IoT Platform
Cloud-based IoT Platform Use Case: GE Predix for Smart Building Energy Management
Fog/Edge Computing & Micro Data Centers
Deep Learning for IoT Big Data Analytics Introduction
Deep Learning for IoT Big Data Analytics Use Case
Distributed Deep Learning
Big Data + IoT + Cloud + Deep Learning Insights from Patents
Big Data + IoT + Cloud + Deep Learning Strategy Development
Designing Data-Intensive Applications
Xanadu Functionality
Xanadu Use Case
Xanadu + Deep Learning + Hadoop Integration
If you have somehow missed the hype, the Internet of Things (IoT) is a fast-growing constellation of internet-connected sensors attached to a wide variety of 'things'. Sensors can take a multitude of possible measurements, Internet connections can be wired or wireless, while 'things' can literally be any object to which you can attach or embed a sensor. If you carry a smartphone, for example, you become a multi-sensor IoT 'thing', and many of your day-to-day activities can be tracked, analysed and acted upon.
Many of the conversations taking place around the IoT are incomplete without a mention of big data. Big data is characterised by “4 V’s”: volume, variety, velocity and veracity. That is, big data comes in large amounts (volume), is a mixture of structured and unstructured information (variety), arrives at (often real-time) speed (velocity) and can be different levels of uncertainty (veracity).
As organizations step into IoT, they must understand the symbiotic relationship between IoT and big data. Just like with any big-data play, merely collecting the data isn't enough. The data must be processed and analyzed to derive meaningful insights, and those insights must drive actionable steps that can improve the business.
What that means is that, without Big Data, the IoT can offer an enterprise little more than noise. But wait…! On the other hand, without IoT, the Big Data is little more than any other software lying idle. Actually you need two to Tango. That’s when you get the perfect marriage!
AllThingstalk presenting at Microsoft Innovation CenterStefaan Top
On 26 January, 2016, Tom Casaer and me were invited to present at the Microsoft Innovation Center in Kortrijk, Belgium. Tom shared some of his insights in Internet of Things (IoT) and I presented the AllThingsTalk Liato case to help elderly live a longer independent life. The crowd enjoyed the demo of our AllThingsTalk IoT platform as well.
This document discusses edge computing and distributed intelligence. It begins with definitions of edge computing and fog computing, noting that fog computing refers to computing near the data source rather than in centralized data centers. It then explores architectural choices for distributed intelligence, including moving computation to data sources using multi-tier IoT architectures that incorporate edge devices, gateways and cloud computing. The document discusses how distributed intelligence can create business value by gaining insights from customer data sources. It provides examples of sensing modalities that could be leveraged and recommends evaluating streaming data from various sources to gain insights.
At WomenWhoCode, 2019 https://india.womenwhocode.dev/agenda/
Edge Computing: What, Why, How and Where
Edge Analytics: What, Why, benefits, limitations
Edge computing vs Edge Analytics
Edge Analytics use-cases
This document discusses 5G and its potential impact on the Internet of Things (IoT). It provides background on IoT, including its definition, examples of IoT devices, history and size. It then discusses benefits of IoT for businesses and consumers. Key points made include: 1) IoT refers to billions of physical devices connected to the internet that collect and share data; 2) 5G could unlock IoT's potential by providing higher speeds, lower latency and ability to connect many devices; 3) While 5G may not impact many current IoT projects, industries like manufacturing, transportation and healthcare may be affected.
The document discusses Internet of Things (IoT) systems, architectures, and use cases. It describes typical IoT architectures involving sensors, devices, gateways, edge/fog computing, cloud platforms, and applications. It provides examples of common IoT devices like smart meters and discusses smart home use cases. Distributed software requirements across the IoT stack are also outlined.
Energy IIoT - Industrial Internet of Things (IIoT) in Decentralized Digital O...crlima10
This presentation introduces the framework for an Industrial Internet of Things (IIoT) convergence towards edge/fog computing. It also defines new industry concepts of "Decentralized Digital Oilfield -DDOF" with semi-autonomous intelligent IIoT operation technology (OT), enabled by Blockchain.
IoT Update | Hoe implementeer je IoT Schaalbaar in je IT landschapIoT Academy
This document discusses how to implement IoT at scale within an organization's IT landscape. It provides an overview of how IoT can create business value across industries by enabling new business models and services. It then discusses Software AG's Cumulocity IoT platform, which provides device connectivity, integration capabilities, data analytics and application enablement to support scalable IoT implementations. The document outlines Cumulocity's approach of starting small with initial use cases and then expanding to leverage more advanced capabilities like machine learning over time.
Smart City Lecture 3 - An Open And/Or Secure Smart CityPeter Waher
When considering Interoperability in a Smart City, there seems to be an apparent contradiction between the requirement of Openness and Security. The choice seems to be between allowing others (anyone?) access to your devices, opening the solution to endless attacks and vulnerabilities, or hermetically sealing off your devices from the outside world, protecting your solution, but making it difficult, or practically impossible, to interoperate with others. The lecture presents a solution to this apparent contradiction. Strong global identities can be used to protect access to things and their data. They can also be used to allow others to discover discoverable devices. A method of defining ownership of information is presented.
By utilizing an infrastructure that provides things with decision support across their entire lifecycles, as well as knowledge about ownership, it becomes possible to model owner consent and provision access to devices and data in realtime, based on the desires of their corresponding owners, without impacting operation of the infrastructure. Thus, an Open and Secure Smart City can be built.When considering Interoperability in a Smart City, there seems to be an apparent contradiction between the requirement of Openness and Security. The choice seems to be between allowing others (anyone?) access to your devices, opening the solution to endless attacks and vulnerabilities, or hermetically sealing off your devices from the outside world, protecting your solution, but making it difficult, or practically impossible, to interoperate with others. The lecture presents a solution to this apparent contradiction. Strong global identities can be used to protect access to things and their data. They can also be used to allow others to discover discoverable devices. A method of defining ownership of information is presented.
By utilizing an infrastructure that provides things with decision support across their entire lifecycles, as well as knowledge about ownership, it becomes possible to model owner consent and provision access to devices and data in realtime, based on the desires of their corresponding owners, without impacting operation of the infrastructure. Thus, an Open and Secure Smart City can be built.
This document provides a brief history of big data, from the earliest known uses of data storage thousands of years ago to modern applications of big data. It outlines key developments such as the creation of early data storage and analysis methods, the development of computerized data processing, and the growth of data collection and sharing through the internet and mobile technology. The document also discusses the increasing volume of data generated every day through online activities and defines some of the main challenges in working with big data today.
The Internet of Things (IoT) - What Really Matters for a Start-UpSandy Carter
The document discusses the Internet of Things (IoT) ecosystem and what matters most for success. It notes that $1 billion was invested in IoT ventures in 2013 and that IoT units installed are estimated to reach 26 billion by 2020. The key focuses that really matter are having the right focus in three areas: domain expertise in industries like automotive and healthcare; understanding drivers of value for users/buyers; and prioritizing user-centered design. Integrated solutions that collect, analyze and optimize data from devices and sensors will be important long-term opportunities. Thriving ecosystems provide mentoring, access to capital, and opportunities to scale globally.
Technology that is going to create a revolution in every Industry including Health care. What is it, what are the tools and what is the outcome?
NASA started the research on Twins due to space travel and the need to have real time feedback of components. Now it is extending to even Health care to having a Human twin.
Big Data in IoT & Deep Learning
Challenges of IoT Big Data Analytics Applications
Challenges of Cloud-based IoT Platform
Cloud-based IoT Platform Use Case: GE Predix for Smart Building Energy Management
Fog/Edge Computing & Micro Data Centers
Deep Learning for IoT Big Data Analytics Introduction
Deep Learning for IoT Big Data Analytics Use Case
Distributed Deep Learning
Big Data + IoT + Cloud + Deep Learning Insights from Patents
Big Data + IoT + Cloud + Deep Learning Strategy Development
Designing Data-Intensive Applications
Xanadu Functionality
Xanadu Use Case
Xanadu + Deep Learning + Hadoop Integration
From the Testers: Measuring for Energy Efficiency and Energy LabelingIRS srl
New energy efficiency standards and labeling are enforced throughout the world. At this session, hear appliance and HVAC test system experts share why you need to pay special attention to sensor selection and system integration. The NI modular platform can lower complexity, reduce development time, and add machine learning to the task.
A digital twin is a virtual representation of a physical object that can be used to help optimize business decisions. The webinar discusses the history and definition of digital twins, how they are enabled by technologies like IoT, and how companies are using them. Digital twins allow a physical object and its virtual counterpart to be connected, providing a closed loop between the simulated and physical worlds to improve conceptualization, comparison, and remote collaboration.
The Internet of Things - beyond the hype and towards ROIPerry Lea
How do you move beyond the hype of IoT and towards profitability? This short lecture examines the hype and origin of IoT and the reality of the industry. It then talks about my experiences with the industry, customers, and technologists. Some have outright failed in IoT projects, others are succeeding.
Get beyond the prototype and lab experiment.
Cognitive Digital Twin by Fariz SaračevićBosnia Agile
Data are driving the world today and they are becoming world's precious currency. Continuous Engineering, the default set of applications for enterprise software development, produce a wealth of data but it is hard to understand its value. What if you could find hidden patterns in your data your development teams create? What if you could discover ways to improve your team's performance? This presentation reviewed some of the different ways the Collaborative Lifecycle Management team (http://jazz.net) is utilizing Watson Analytics to gain insights into and improve efficiency with their own processes.
I gave this talk at the 'Digital Twin Conference' hosted by LH Corp at COEX, Seoul on August 8th, 2019.
Abstract: 'Digital Twin' is a digital replication of real world objects, processes, phenomena that can be used for various purposes. Digital twin concept backs to manufacturing industry in early 2000s for the PLM (Product Lifecycle Management) purposes. It is based on the idea that a digital informational construct about a physical system could be created as an entity on its own. Definitions of digital twin emphasize the three important levels or characteristics. At first, there should be connection between real physical world and corresponding virtual world. To do this, Level 1 digital twin provides virtual 3D models. Secondly, this connection between real world and virtual world is established by generating (near) real time data using sensors or IoT. This is called Level 2 digital twin. Thirdly, Level 3 digital twin carries out certain analyses, predictions, and simulations using virtual 3D and (near) real time data. ‘Smart Spaces’ are interactive environments where humans and technology can openly communicate with each other in a physical or digital setting. Examples of smart spaces include smart cities, smart factories, and smart homes. ‘Smart Spaces’ is one of Garner’s Top 10 Tech Trends for 2019. As spaces are going through digital transformation with 4th industrial revolution, there are many attempts to apply digital twin technology to manage urban, spatial, and industrial issues around the world. Those attempts look set to play an increasingly important role in the creation of smart cities, smart factories, and smart homes. Bringing the virtual and real worlds together in this way can help to give better analysis, visualization, and simulation to the decision-making process. This will be a multi-way process with iterative feedback among stakeholders.
In this talk, I'll share my real experiences in carrying out digital twin and smart space projects. Also I’ll talk about what I’ve learnt from these projects.
Big Data in IoT & Deep Learning
Challenges of IoT Big Data Analytics Applications
Challenges of Cloud-based IoT Platform
Cloud-based IoT Platform Use Case: GE Predix for Smart Building Energy Management
Fog/Edge Computing & Micro Data Centers
Deep Learning for IoT Big Data Analytics Introduction
Deep Learning for IoT Big Data Analytics Use Case
Distributed Deep Learning
Big Data + IoT + Cloud + Deep Learning Insights from Patents
Big Data + IoT + Cloud + Deep Learning Strategy Development
Designing Data-Intensive Applications
Xanadu Functionality
Xanadu Use Case
Xanadu + Deep Learning + Hadoop Integration
If you have somehow missed the hype, the Internet of Things (IoT) is a fast-growing constellation of internet-connected sensors attached to a wide variety of 'things'. Sensors can take a multitude of possible measurements, Internet connections can be wired or wireless, while 'things' can literally be any object to which you can attach or embed a sensor. If you carry a smartphone, for example, you become a multi-sensor IoT 'thing', and many of your day-to-day activities can be tracked, analysed and acted upon.
Many of the conversations taking place around the IoT are incomplete without a mention of big data. Big data is characterised by “4 V’s”: volume, variety, velocity and veracity. That is, big data comes in large amounts (volume), is a mixture of structured and unstructured information (variety), arrives at (often real-time) speed (velocity) and can be different levels of uncertainty (veracity).
As organizations step into IoT, they must understand the symbiotic relationship between IoT and big data. Just like with any big-data play, merely collecting the data isn't enough. The data must be processed and analyzed to derive meaningful insights, and those insights must drive actionable steps that can improve the business.
What that means is that, without Big Data, the IoT can offer an enterprise little more than noise. But wait…! On the other hand, without IoT, the Big Data is little more than any other software lying idle. Actually you need two to Tango. That’s when you get the perfect marriage!
AllThingstalk presenting at Microsoft Innovation CenterStefaan Top
On 26 January, 2016, Tom Casaer and me were invited to present at the Microsoft Innovation Center in Kortrijk, Belgium. Tom shared some of his insights in Internet of Things (IoT) and I presented the AllThingsTalk Liato case to help elderly live a longer independent life. The crowd enjoyed the demo of our AllThingsTalk IoT platform as well.
This document discusses edge computing and distributed intelligence. It begins with definitions of edge computing and fog computing, noting that fog computing refers to computing near the data source rather than in centralized data centers. It then explores architectural choices for distributed intelligence, including moving computation to data sources using multi-tier IoT architectures that incorporate edge devices, gateways and cloud computing. The document discusses how distributed intelligence can create business value by gaining insights from customer data sources. It provides examples of sensing modalities that could be leveraged and recommends evaluating streaming data from various sources to gain insights.
At WomenWhoCode, 2019 https://india.womenwhocode.dev/agenda/
Edge Computing: What, Why, How and Where
Edge Analytics: What, Why, benefits, limitations
Edge computing vs Edge Analytics
Edge Analytics use-cases
This document discusses 5G and its potential impact on the Internet of Things (IoT). It provides background on IoT, including its definition, examples of IoT devices, history and size. It then discusses benefits of IoT for businesses and consumers. Key points made include: 1) IoT refers to billions of physical devices connected to the internet that collect and share data; 2) 5G could unlock IoT's potential by providing higher speeds, lower latency and ability to connect many devices; 3) While 5G may not impact many current IoT projects, industries like manufacturing, transportation and healthcare may be affected.
The document discusses Internet of Things (IoT) systems, architectures, and use cases. It describes typical IoT architectures involving sensors, devices, gateways, edge/fog computing, cloud platforms, and applications. It provides examples of common IoT devices like smart meters and discusses smart home use cases. Distributed software requirements across the IoT stack are also outlined.
Energy IIoT - Industrial Internet of Things (IIoT) in Decentralized Digital O...crlima10
This presentation introduces the framework for an Industrial Internet of Things (IIoT) convergence towards edge/fog computing. It also defines new industry concepts of "Decentralized Digital Oilfield -DDOF" with semi-autonomous intelligent IIoT operation technology (OT), enabled by Blockchain.
IoT Update | Hoe implementeer je IoT Schaalbaar in je IT landschapIoT Academy
This document discusses how to implement IoT at scale within an organization's IT landscape. It provides an overview of how IoT can create business value across industries by enabling new business models and services. It then discusses Software AG's Cumulocity IoT platform, which provides device connectivity, integration capabilities, data analytics and application enablement to support scalable IoT implementations. The document outlines Cumulocity's approach of starting small with initial use cases and then expanding to leverage more advanced capabilities like machine learning over time.
Smart City Lecture 3 - An Open And/Or Secure Smart CityPeter Waher
When considering Interoperability in a Smart City, there seems to be an apparent contradiction between the requirement of Openness and Security. The choice seems to be between allowing others (anyone?) access to your devices, opening the solution to endless attacks and vulnerabilities, or hermetically sealing off your devices from the outside world, protecting your solution, but making it difficult, or practically impossible, to interoperate with others. The lecture presents a solution to this apparent contradiction. Strong global identities can be used to protect access to things and their data. They can also be used to allow others to discover discoverable devices. A method of defining ownership of information is presented.
By utilizing an infrastructure that provides things with decision support across their entire lifecycles, as well as knowledge about ownership, it becomes possible to model owner consent and provision access to devices and data in realtime, based on the desires of their corresponding owners, without impacting operation of the infrastructure. Thus, an Open and Secure Smart City can be built.When considering Interoperability in a Smart City, there seems to be an apparent contradiction between the requirement of Openness and Security. The choice seems to be between allowing others (anyone?) access to your devices, opening the solution to endless attacks and vulnerabilities, or hermetically sealing off your devices from the outside world, protecting your solution, but making it difficult, or practically impossible, to interoperate with others. The lecture presents a solution to this apparent contradiction. Strong global identities can be used to protect access to things and their data. They can also be used to allow others to discover discoverable devices. A method of defining ownership of information is presented.
By utilizing an infrastructure that provides things with decision support across their entire lifecycles, as well as knowledge about ownership, it becomes possible to model owner consent and provision access to devices and data in realtime, based on the desires of their corresponding owners, without impacting operation of the infrastructure. Thus, an Open and Secure Smart City can be built.
This document provides a brief history of big data, from the earliest known uses of data storage thousands of years ago to modern applications of big data. It outlines key developments such as the creation of early data storage and analysis methods, the development of computerized data processing, and the growth of data collection and sharing through the internet and mobile technology. The document also discusses the increasing volume of data generated every day through online activities and defines some of the main challenges in working with big data today.
The Internet of Things (IoT) - What Really Matters for a Start-UpSandy Carter
The document discusses the Internet of Things (IoT) ecosystem and what matters most for success. It notes that $1 billion was invested in IoT ventures in 2013 and that IoT units installed are estimated to reach 26 billion by 2020. The key focuses that really matter are having the right focus in three areas: domain expertise in industries like automotive and healthcare; understanding drivers of value for users/buyers; and prioritizing user-centered design. Integrated solutions that collect, analyze and optimize data from devices and sensors will be important long-term opportunities. Thriving ecosystems provide mentoring, access to capital, and opportunities to scale globally.
What happens in the Innovation of Things?Kim Escherich
From the ComputerWorld Internet of Things conference in Copenhagen October 27 2015. On definitions, markets, trends, needed capabilities and how to implement using IBM BlueMix.
El IoT y la gestión de las empresas del futuro, IGNASI ERRANDO, CISCODomotys
The document discusses the Internet of Everything (IoE) and its key components of connecting people, processes, data and things. It outlines how the IoE enables new and better connections through applications like smart buildings, lighting, payments and more. It also discusses challenges of implementing the IoE like security issues, limited bandwidth and latency. The document provides an overview of how fog computing and cloud can help address these challenges and unlock business value across industries like manufacturing, transportation, retail and smart cities.
This document discusses the challenges of scaling IoT initiatives to generate ROI within 18 months as recommended. While IoT projects are collecting huge amounts of data across industries, only 1% of the data is being properly analyzed to produce actionable insights. This leads to 275% of IoT projects failing to achieve their financial goals due to a lack of expertise in scaling solutions to leverage the full potential of collected data through analytics and machine learning. The document promotes an innovative cloud platform to help organizations securely connect devices, scale their big data infrastructure, and generate insights to maximize ROI from IoT implementations.
IoT Meets Big Data: The Opportunities and Challenges by Syed Hoda of ParStreamgogo6
Download our special report, IoT Tech for the Manager: http://bit.ly/report1-slideshare
IoT Meets Big Data: The Opportunities and Challenges as presented at the IoT Inc Business' Eighth Meetup. See: http://www.iot-inc.com/iot-meets-big-data-the-opportunities-and-challenges/
In our eighth Meetup we have Syed Hoda, Chief Marketing Officer of ParStream presenting “IoT Meets Big Data: The Opportunities and Challenges”. Come meet other business leaders in the IoT ecosystem and discuss the business issues you face in the Internet of Things.
Presentation Abstract
The Internet of Things (IoT) and Big Data have each made press headlines and continue to be board-level priorities. The intersection of IoT and Big Data is a fascinating area of innovation with tremendous scope for business impact. From industrial sensors to vehicles to health monitors, a huge variety of devices connects to the Internet and share information. At the same time, the cost to store data has dropped dramatically while capabilities for analysis have made huge leaps forward. How can analytics drive business benefits from IoT projects? What are the challenges in storing and analyzing huge amounts of real-world information? How can companies generate more value from their data? We will address these questions and also share our perspectives on innovative technologies enabling new IoT use cases.
The document discusses how digital transformation and the Internet of Everything are disrupting businesses. It notes that by 2020, 75% of businesses will be fully digital or in preparation to become digital. However, only 30% of digitization efforts will be successful. The Internet of Everything connects people, processes, data and things to deliver the right information to the right person or machine at the right time. It provides examples of how the Internet of Everything can transform smart cities and smart buildings through connecting various systems and devices to improve operations and resource management.
The Internet of Things Conference at E4rAVe [PUBS]
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DELL Technologies - The IoT Value Chain - Solutions for the Smart World - Del...Smarter.World
In this presentation we will introduce various aspects of the Internet of Things, Industry 4.0 and the associated challenges in implementing new digital services.
We also refer to IoT / Industry 4.0 terminology, market developments, factors and drivers, IoT platform components, but also to the differentiation and similarities of the Internet of Things and Industry 4.0.
Using various application examples, we will outline the range of DELL Technologies offerings.
Here, however, we remain at an overview level for the first time without paying attention to the details of the deployable DELL-EMC products and solutions.
We would continue this in downstream discussions depending on the identified topic segment.
MT85 Challenges at the Edge: Dell Edge GatewaysDell EMC World
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- Dell's portfolio is designed to address these challenges and unlock IoT potential. Their edge gateways feature diverse connectivity, data protocol support, security, and manageability. This infrastructure combined with partners allows customers to gather and analyze data and
Microservices: The Future-Proof Framework for IoTCapgemini
Dr Michael Capone Principal Analyst - Capgemini
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development, customer support, operations, and supply chain not to mention external users like vendors and partners. Each user group needs to be able to access and select different data and apply different logic and analytic approaches to perform specific tasks.
Furthermore, each group can have unique usability requirements. As companies become more IoT mature and start to plan for “data actionability,” the disadvantages of a homogenous IoT stack or departmental systems become obvious. The best option from a data quality, user acceptance, and ROI perspective is a microservices IoT platform.
Internet of Things & Logistics ECO 270416 FinalErik Cotman
The document discusses the Internet of Things (IoT) and its potential applications and benefits for logistics. It provides definitions of IoT and key facts about the growing number of connected devices. It outlines various IoT applications areas like smart cities, homes, health, transport, and industry. For logistics specifically, it describes how IoT can enhance supply chain visibility and management through technologies like sensors, RFID, and beacons. The benefits highlighted include reduced asset loss, ensured temperature stability, optimized inventory and fleet management, and fuel cost savings. It concludes by advising companies to create a digital vision, identify quick wins, and inventory processes to embrace IoT.
The document discusses an open data hub that provides access to real-time data through APIs and other tools. It summarizes that the hub currently receives over 15 million API calls per month and makes over 3 terabytes of data available from various data providers. The hub also offers data visualization tools like a knowledge graph and web components to help users access and understand the data.
The document discusses the Internet of Things (IoT) market in Russia. It provides an overview of how IoT projects make money, the potential Russian market, challenges, and opportunities. Specifically, it outlines five stages of monetization for IoT projects from hardware sales to commodity services. It also analyzes the best investment opportunities in three blocks - short term areas like home automation and healthcare, medium term infrastructure development, and long term artificial intelligence.
The document discusses the Internet of Things (IoT) market in Russia. It provides an overview of how IoT projects make money, the potential Russian market, challenges, and opportunities. Specifically, it outlines five stages of monetization for IoT projects from hardware sales to commodity services. It also analyzes the best investment opportunities in three blocks - short term areas like home automation and healthcare, medium term infrastructure development, and long term artificial intelligence.
The document discusses Internet of Things (IoT) opportunities in Russia. It outlines several key points:
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Powering the Internet of Things with Apache HadoopCloudera, Inc.
Without the right data management strategy, investments in Internet of Things (IoT) can yield limited results. Apache Hadoop has emerged as a key architectural component that can help make sense of IoT data, enabling never before seen data products and solutions.
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Making IoT Data Actionable Using Predictive Analytics
1. Innovation and
Reinvention Driving
Transformation
OCTOBER 9,
2018
2018 HPCC Systems® Community
Day
Hicham Elhassani – VP Modeling Vertical Support
Dan S. Camper – Sr. Architect, HPCC Solutions Lab
Making IoT Data Actionable Using Predictive Analytics
3. If you think connected “things” are everywhere NOW . . .
Making IoT Data Actionable Using Predictive Analytics
2016 2017 2018 2020
Consumer 3,963 5,244 7,036 12,863
Business:Cross-Industry 1,102 1,501 2,133 4,381
Business:Vertical-Specific 1,317 1,635 2,028 3,171
Grand Total 6,382 8,381 11,197 20,415
Source: Gartner (January 2017)
IoT Units Installed Base by Category
(Millions of Units)
3
4. Value proposition?
Cyber risk?
What does the data say?
Who is driving?
Incremental or revolutionary?
Cost vs. Benefit?
Making IoT Data Actionable Using Predictive Analytics
BIG QUESTIONS
FOR
INSURANCE
4
5. Making IoT Data Actionable Using Predictive Analytics
Importance of collecting Iot data to company’s insurance strategy
(n=120)
8%
70%
22%
Very / Somewhat Important
Neither important or unimportant
Not at all/not very important
Importance for insurers to collect IoT data today
5
6. Making IoT Data Actionable Using Predictive Analytics
Collection and/or Purchase of Connected Home
Data
(n=120)
1%
4%
19%
38%
38% Collect/purchase, use in decision-making
Collect/purchase, plan to use
Collect/purchase, but not sure how to use
Don’t collect/purchase, but plan to
Don’t collect/purchase, don’t plan to
Collect today
= 24%
Don’t Collect today
= 76%
Collection of Connected Home Data
6
7. Making IoT Data Actionable Using Predictive Analytics
Timeline to begin collecting Connected Home data
Anticipated Timeline for Collecting and/or Using Connected Homes
Data
(among those not currently using, but planning to use connected homes, n=73)
In next year
In next 2-3 years
In next 4-5 years
In 6+ years
Not sure
4%
52%
34%
7%
3%
Next 3Years
= 56%
4+Years
= 41%
7
8. Home Loss Statistics and IOT opportunities
Making IoT Data Actionable Using Predictive Analytics
11
%
OTHERTHEFT
25
%
21% 22% 21%
WIND HAIL FIRE WATER
NON-
WEATHERWATER
WEATHER
LIABILITY
Internals data
Security
Freeze
detection
Leak detection
Smoke/CO
Temp/Humidity
Motion sensor
Appliances
Audio/video
External data
Weather API
Social M
events
Loss history
Property info
Geo
information
Internals data
Security
Freeze
detection
Leak detection
Smoke/CO
Temp/Humidity
Motion sensor
Appliances
Audio/Video
External data
Weather API
Social M
events
Loss history
Property info
Geo
information
Internals data
Security
Freeze
detection
Leak detection
Smoke/CO
Temp/Humidity
Motion sensor
Appliances
Audio/video
External data
Weather API
Social M
events
Loss history
Property info
Geo
information
Internals data
Security
Freeze
detection
Leak detection
Smoke/CO
Temp/Humidity
Motion sensor
Appliances
Audio/video
External data
Weather API
Social M
events
Loss history
Property info
Geo
information
Internals data
Security
Freeze
detection
Leak detection
Smoke/CO
Temp/Humidity
Motion sensor
Appliances
Audio/video
External data
Weather API
Social M
events
Loss history
Property info
Geo
information
8
9. Today, let’s discuss some examples
Occupancy: Monitoring/Prevention
Water Leak:
Monitoring/Alert
9
10. Making IoT Data Actionable Using Predictive Analytics
Smart Thermostat Data: Primary Residence
HVAC Mode Observations
0
50
100
150
200
250
300
350
Eco
July 4th
Weekend
Source: Nest
10
11. Making IoT Data Actionable Using Predictive Analytics
Smart Thermostat Data: Vacation Home
0
20
40
60
80
100
120
Eco
HVAC Mode Observations July 4th
Weekend
Source: Nest
11
13. Example: Water Leak & Assignment of Benefits
Making IoT Data Actionable Using Predictive Analytics
File it
Assign of benefits (AOB) is a
legal tool that allows the
homeowner to transfer their
rights to collect from an
insurance claim to a third
party.
Fix It
AOB is commonly used when
a homeowner employs a
contractor or water
remediation company to fix
water damage from pipe and
appliance leaks
Fake it
This arrangement has
permitted some contractors to
overinflate claims, resulting in
a dramatic increase in
frequency and severity in
Florida water non-weather
claims
Source: Office of Insurance Consumer Advocate, Florida Office of Insurance Regulation
13
14. Assignment of Benefits – Florida vs USA (Excl. Florida)
Making IoT Data Actionable Using Predictive Analytics
30
25
20
15
10
5
0
LossCost($)
2011 2012 2013 2014 2015 2016
Accidental Water Discharge and Appliance Leakage Loss Cost
USA (Excl. Florida) FloridaSource: LexisNexis Internal Research
14
17. Water Leak and Geo-located losses
Making IoT Data Actionable Using Predictive Analytics
0.50%
0.45%
0.40%
0.35%
0.30%
0.25%
0.20%
0.15%
0.10%
0.05%
0.00%
Frequency
2011 2012 2013 2014 2015 2016
Accidental Water Discharge and Appliance Leakage Frequency
Broward County Miami-Dade
County
Palm Beach
County
Florida (Excl. Tri
Counties)
Source: LexisNexis Internal Research
17
19. Weather Events Digital Trail
• Elk City tornado
by the
NOAA:yesterday
17/05/2017
• Flood
• Hail
• Lightning
• Tornado
• Wildfire
Making IoT Data Actionable Using Predictive Analytics 19
20. Stream Analytics: Push and Pull data sources
Making IoT Data Actionable Using Predictive Analytics
Wind Fire Water
(non-
weather)
Water
(weather
)
Theft Liability Other
Hail
20
21. Data platforms will be key to unlocking the full potential of this
opportunity
Making IoT Data Actionable Using Predictive Analytics
MARKETING
CONTACT
QUOTE
UNDERWRITIN
G
RENEWAL
COMPLIANCE
CLAIM
IoT
Platform
Insurer
Automatio
n
Mitigation Utilities
Connected Home
Securit
y
Connecte
d Car
Connecte
d Self
Connecte
d
Business
21
22. How to start unlocking these insights now
Technology/Analytics to
develop and deploy a
pilot program
24. HPCC Systems – Pull Architecture
• Device users register at a web portal
• Authentication and authorization via
device manufacturer’s web site
• Authorization response includes an
access token
• All registration information saved
• Thor queries devices for all registered
users in parallel
• Ancillary data, such as weather
conditions local to every device, is
periodically gathered
• Analytics are also run periodically, as
often as needed
• ROXIE updated with analytics results
and are made available to external
services
Making IoT Data Actionable Using Predictive Analytics 24
25. HPCC Systems – Push Architecture
• Authorized devices whitelisted via
master device management
• Remote devices send their data to
ROXIE
• After validation and normalization,
message stored in Kafka and
Couchbase
• Thor periodically pulls new messages
from Kafka for processing
• Ancillary data, such as weather
conditions local to every device, is
periodically gathered
• Analytics are also run periodically, as
often as needed
• ROXIE updated with analytics results
and are made available to external
services
Making IoT Data Actionable Using Predictive Analytics 25
Editor's Notes
Devices in the Internet of Things communicate with each other, only a human isn’t directly prompting the interaction. Today we call this “The Internet of Things,” but that’s only because it’s new. In five years we’ll probably just call it “the internet.”
Gartner put the number of IoT devices at 8 billion in 2017. For 2020, they estimate TWENTY billion. Cisco estimates 50 billion. We can be sure they’re both wrong, but one of them might be close. The point is, there will be tens of billions of devices generating data.
And on the data side, what’s interesting is that humans have generated the majority of the data out there today, from pictures and texts, to movies, to scholarly articles. But soon the data created by “things” will dwarf the data created by humans.
There has been a lot of activity over the past year but these same key questions are still largely unanswered.
[Walk through points]
And I’ll add one more --- Consumer engagement. What gets the consumer to push through setup challenges, encourage them to replace batteries, or even engage with the device through an app?
There is still a lot of ambivalence and complexity out there so instead of taking a step back like we did last year, let’s take a step in and look at some specific use cases.
Who will be the winners and loser in the devices and platforms. There will continue to be consolidation, new entries and exits. This makes partnerships and data agreements complicated.
Who is driving? Is it the Consumer, the insurer or the infrastructure. As I showed on the previous slide… You may want to prevent water losses, but that doesn’t mean your policyholder shares that concern. He or she may be more likely to opt for voice activated mood lighting. Discounts or carrier device buys may help to remedy this over time. Connected utility meters, built in capabilities may influence in time.
Cyber risk: In 2016 there was a major Distributed Denial of Service attack that shut down a number of websites. Wifi enable baby monitors have been hacked. Carriers do have to consider this when potentially connected their brand with a device. Do you want that connected thermostat you encouraged your customer to buy to be susceptible to ransomware that extort a payment to keep the heat on during the winter? .. . The good news is that there are good companies out there today working on building more sophisticated technology to protect connected devices.
Much of the purported benefit of the connected home is speculation. How does this data really play out? Does the connected water sensor really prevent loss payments to a significant degree. Does it reduce frequency? Just Severity? How much? We need a lot more data to know for sure. And multiply that across the dozens of devices that are available.
How big is the disruption? If at the end of the day we end up with a lot of new data sources that allow us to offer another 5% discount, or that help us validate the home security system discounts carriers are already giving . . . Then it’s still useful but not revolutionary. On the other hand, being able to price a risk from the ground-up using a multitude of IoT real time data becomes a reality then maybe it does. The other question here is loss mitigation versus loss avoidance.
Finally, is cost. Particularly the cost of the device. As we discussed above, the consumer may not buy the devices you want them to have, which means the insurer would potentially need to foot the bill (either directly or through discounting and/or rate). That math needs to work, and a $5 device will be a lot more attractive to mitigate flood risk under a give sink then an $80 device.
Insurers can explore many ways to avoid and limit losses
So where does LexisNexis fit in the IoT world? We can analyze, normalize, and score this data for our customers (WITH THE CONSUMERS PERMISSION, OF COURSE). We can solve the many to many challenge, not only for insurers, but for IoT companies, too. We can take millions of datapoints and turn them into something digestible and meaningful to the industry. I hope this all sounds familiar, because it’s what we do every day already.
And the normalization can take many forms. It’s not hard to imagine that the Nest, the Ecobee, the Lyric, and the Sensi - all smart thermostats which use occupancy to make decisions – might produce different data. It might come at different intervals, at different levels of granularity, and there may be differences in sensitivity between them. Clearly there’s an opportunity for us to normalize that data on the way in so that we can produce occupancy score or attribute from thermostats that works for ALL popular models of thermostat. This is not too different from what we’ve done in the UBI space to normalize driver scores across phone types.
This is one piece of the data that we can collect from Nest thermostats. In this case I once again got one of my co-workers to agree to let me use his data – but he won’t let me use his real name because he is paranoid that his rates will go up. We are going to call him “Shawn”
Shawn has two Nest thermostats and they each send data nearly 150 times a day. This data stream has dozens of field including everything from the actual temperature in the home, the desired temperature, the location of the thermostat the consumer has specified and whether someone has locked in a temperature other than those in the settings. The nest thermostat switches to “Eco” mode when it doesn’t detect anyone present in the home and this data is captured as well.
Here is Shawn’s lake House. Only one thermostat in this house but it is consistently reporting “Eco Status” until we get to the Holiday weekend.
Now this is a very clear example and not every example will be this clear but it is evident.
Assignment of Benefits mainly impacts water non-weather claims associated with leaking pipes and damaged appliances
Small circles are tweets containing ‘tornado’, large circles are official sightings
So we are starting to harvest based on keywords to
1: build up data to have a baseline (i.e. background noise)
2: ‘hoping’ for an event to see spikes
Right now we are grabbing tweets with words (also partial) containing the keywords
Flood
Hail
Lightning
Tornado
Wildfire
So where does LexisNexis fit in the IoT world? We can analyze, normalize, and score this data for our customers (WITH THE CONSUMERS PERMISSION, OF COURSE). We can solve the many to many challenge, not only for insurers, but for IoT companies, too. We can take millions of datapoints and turn them into something digestible and meaningful to the industry. I hope this all sounds familiar, because it’s what we do every day already.
And the normalization can take many forms. It’s not hard to imagine that the Nest, the Ecobee, the Lyric, and the Sensi - all smart thermostats which use occupancy to make decisions – might produce different data. It might come at different intervals, at different levels of granularity, and there may be differences in sensitivity between them. Clearly there’s an opportunity for us to normalize that data on the way in so that we can produce occupancy score or attribute from thermostats that works for ALL popular models of thermostat. This is not too different from what we’ve done in the UBI space to normalize driver scores across phone types.
For a carrier that wants to get started in IoT the first objective is to get data, and this can be a challenge by yourself. However, LexisNexis offers to be your partner in collecting and interpreting this data. An easy place to start is by leveraging the devices that are already in your customer’s homes.
LexisNexis is in the process of rolling out internal pilots with our employees to collect Nest thermostat data via an API connection. As we move into phase II of this program by early next year, we invite you to join us. For your customers that opt in, and have a Nest in their home, you will be able to simply supply them with a URL to begin collecting data.
LexisNexis will then collect and process data, including pooling with participants should you choose to participate in data sharing and share the aggregate results with the broader group.
If you are interested in a water device pilot, we are happy to work with you as well and are happy to facilitate conversations with device makers that fit your needs.