Gloseije (Jessy) Bazolana speaks on Data Management &
Analytics in IoT: Extracting
Value from Data in Africa at IoT Forum africa 2023- https://itnewsafrica.com/event/event/iot-forum-africa-2023/.
Group 4 IT INfrastructure Group presentation Final [Auto-saved].pptxOdedeleIfeoluwa
This document discusses big data, internet of things (IoT), and analytics in networks. It begins with an introduction to the rise of interconnected devices and vast amounts of data generated through IoT. It then outlines a plan to discuss big data characteristics, the concept of IoT, and different types of analytics in networks. Specific sections cover background on big data and IoT, performance, security, and predictive analytics, and case studies are provided on applying network monitoring in smart cities and industrial IoT. The document concludes that network analytics plays a critical role in IoT deployments by providing insights to improve decision-making, efficiency, and user experience.
¿Cómo puede ayudarlo Qlik a descubrir más valor en sus datos de IoT?Data IQ Argentina
The document discusses the Internet of Things (IoT) ecosystem and how to extract value from IoT data. It describes how IoT data moves through different layers from devices to connectivity to operations to analytics. At each layer, data takes on different states like in motion, in use, or at rest. To create value from IoT data, it needs to be associated with other data sources and analyzed to gain insights. These insights then need to be shared and acted on. The document promotes Qlik's analytics tools for flexible, scalable analysis of IoT data that can integrate various data sources and enable innovation.
A comprehensive guide on Data Engineering for IoT-1.pdftv2064526
Explore the various applications, architecture, and features of data engineering in IoT through this detailed guide on Data Engineering for IoT. https://www.usdsi.org/data-science-insights/resources/a-comprehensive-guide-on-data-engineering-for-iot
Databases are constantly expanding, and they can potentially bring in greater revenue, increase efficiency and reduce operational costs. With the right data, businesses are able to reach their customers on a more personalized level, provided with the proper extraction of relevant insights.
Despite things rapidly shifting online, data-driven success is not easy to achieve. Most times, lack of data skills and talents become a barrier for businesses to understand and analyse these valuable data and information.
Unveiling Tomorrow_ The Future of Data Science.pdfCIOWomenMagazine
In this exploration, we delve into the burgeoning realm of data science, examining the current state, anticipating future trends, and understanding the transformative potential that lies ahead.
The document discusses the Internet of Things (IoT) and its impact. It begins by quoting the World Economic Forum that we are on the brink of a technological revolution through IoT that will fundamentally change how people live and work. IoT involves connecting physical devices to the internet and to each other. This allows for collection and sharing of data from billions of smart devices. The document then discusses how IoT is being used in various industries and provides examples of IoT applications for smart farming, elderly care, smart home devices, and more. It also outlines some of the advantages and disadvantages of IoT, such as improved customer engagement but also security and privacy concerns.
This document provides an overview of the Internet of Things (IoT). It defines IoT as an ecosystem of connected physical objects that are accessible via the internet. These "things" can collect and transfer data without human intervention through built-in sensors. The document outlines several applications of IoT such as smart homes, wearables, connected cars, industrial uses, smart cities, agriculture, retail, energy, healthcare, and farming. It also discusses the rapid growth expected in IoT with billions of connected devices projected by 2020. However, the document notes issues like platform fragmentation, privacy/security concerns, and lack of interoperability that could impact adoption of IoT technologies.
IRJET- Scope of Big Data Analytics in Industrial DomainIRJET Journal
This document discusses the scope of big data analytics in industrial domains. It begins by defining big data and its key characteristics, known as the "7 V's" - volume, velocity, variety, variability, veracity, value, and volatility. It then discusses how big data is generated in various fields like social media, search engines, healthcare, online shopping, and stock exchanges. The document focuses on how big data analytics can be applied in industrial Internet of Things (IoT) to extract meaningful information from large and continuous data streams generated by IoT devices using machine learning techniques.
Group 4 IT INfrastructure Group presentation Final [Auto-saved].pptxOdedeleIfeoluwa
This document discusses big data, internet of things (IoT), and analytics in networks. It begins with an introduction to the rise of interconnected devices and vast amounts of data generated through IoT. It then outlines a plan to discuss big data characteristics, the concept of IoT, and different types of analytics in networks. Specific sections cover background on big data and IoT, performance, security, and predictive analytics, and case studies are provided on applying network monitoring in smart cities and industrial IoT. The document concludes that network analytics plays a critical role in IoT deployments by providing insights to improve decision-making, efficiency, and user experience.
¿Cómo puede ayudarlo Qlik a descubrir más valor en sus datos de IoT?Data IQ Argentina
The document discusses the Internet of Things (IoT) ecosystem and how to extract value from IoT data. It describes how IoT data moves through different layers from devices to connectivity to operations to analytics. At each layer, data takes on different states like in motion, in use, or at rest. To create value from IoT data, it needs to be associated with other data sources and analyzed to gain insights. These insights then need to be shared and acted on. The document promotes Qlik's analytics tools for flexible, scalable analysis of IoT data that can integrate various data sources and enable innovation.
A comprehensive guide on Data Engineering for IoT-1.pdftv2064526
Explore the various applications, architecture, and features of data engineering in IoT through this detailed guide on Data Engineering for IoT. https://www.usdsi.org/data-science-insights/resources/a-comprehensive-guide-on-data-engineering-for-iot
Databases are constantly expanding, and they can potentially bring in greater revenue, increase efficiency and reduce operational costs. With the right data, businesses are able to reach their customers on a more personalized level, provided with the proper extraction of relevant insights.
Despite things rapidly shifting online, data-driven success is not easy to achieve. Most times, lack of data skills and talents become a barrier for businesses to understand and analyse these valuable data and information.
Unveiling Tomorrow_ The Future of Data Science.pdfCIOWomenMagazine
In this exploration, we delve into the burgeoning realm of data science, examining the current state, anticipating future trends, and understanding the transformative potential that lies ahead.
The document discusses the Internet of Things (IoT) and its impact. It begins by quoting the World Economic Forum that we are on the brink of a technological revolution through IoT that will fundamentally change how people live and work. IoT involves connecting physical devices to the internet and to each other. This allows for collection and sharing of data from billions of smart devices. The document then discusses how IoT is being used in various industries and provides examples of IoT applications for smart farming, elderly care, smart home devices, and more. It also outlines some of the advantages and disadvantages of IoT, such as improved customer engagement but also security and privacy concerns.
This document provides an overview of the Internet of Things (IoT). It defines IoT as an ecosystem of connected physical objects that are accessible via the internet. These "things" can collect and transfer data without human intervention through built-in sensors. The document outlines several applications of IoT such as smart homes, wearables, connected cars, industrial uses, smart cities, agriculture, retail, energy, healthcare, and farming. It also discusses the rapid growth expected in IoT with billions of connected devices projected by 2020. However, the document notes issues like platform fragmentation, privacy/security concerns, and lack of interoperability that could impact adoption of IoT technologies.
IRJET- Scope of Big Data Analytics in Industrial DomainIRJET Journal
This document discusses the scope of big data analytics in industrial domains. It begins by defining big data and its key characteristics, known as the "7 V's" - volume, velocity, variety, variability, veracity, value, and volatility. It then discusses how big data is generated in various fields like social media, search engines, healthcare, online shopping, and stock exchanges. The document focuses on how big data analytics can be applied in industrial Internet of Things (IoT) to extract meaningful information from large and continuous data streams generated by IoT devices using machine learning techniques.
Here's how big data and the Internet of Things work together: a vast network of sensors (IoT) collect a boatload of information (big data) that is then used to improve services and products in various industries, which in turn generate revenue.
This document discusses fundamentals of IoT data analytics. It defines IoT analytics and explains challenges including dealing with large amounts of data, security issues, and misbehaving devices. It categorizes IoT data as either structured or unstructured, and as data in motion or at rest. Structured data fits a predefined model while unstructured data lacks structure. Data in motion passes through networks while data at rest is stored. Both predictive and prescriptive analytics provide more value but are more complex than descriptive or diagnostic analysis. Class activities involve capturing IoT data examples and presenting categorization and challenges.
Data Management for Internet of things : A Survey and DiscussionIRJET Journal
This document discusses data management for the Internet of Things (IoT). It begins with an abstract that outlines the need for improved data management techniques to handle the massive volumes of data produced by IoT devices. The document then provides background on IoT data characteristics that make traditional database solutions inadequate. It surveys current research in IoT data management and proposes a framework that considers the full data lifecycle from collection to deletion. Finally, it performs a gap analysis of existing solutions based on factors like data format, storage architecture, processing speed, and server response time.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
Outline
Introduction
Definition of the Internet of Things (IoT)
Importance and impact of IoT in today's world
Key Components of IoT
Sensors and devices
Connectivity and networking
Data processing and analytics
User interfaces and applications
Applications of IoT
Smart homes and home automation
Industrial IoT (IIoT) and smart manufacturing
Healthcare and medical applications
Transportation and logistics
Agriculture and farming
Environmental monitoring and conservation
Benefits and Advantages of IoT
Improved efficiency and productivity
Enhanced safety and security
Cost savings and resource optimization
Enhanced decision-making and automation
Improved quality of life
Challenges and Risks of IoT
Security and privacy concerns
Data management and storage
Interoperability and compatibility
Ethical and societal implications
Future Trends and Innovations in IoT
Edge computing and fog computing
5G connectivity and low-power networks
Artificial intelligence and machine learning
Blockchain technology and IoT
Conclusion
FAQs
How does IoT work?
Is IoT only applicable to large-scale industries?
Can IoT improve sustainability efforts?
What are the security risks associated with IoT?
Will IoT replace human jobs?
The Internet of Things (IoT) is a revolutionary concept that has transformed the way we interact with technology and the world around us. In simple terms, IoT refers to the interconnection of various devices, sensors, and systems through the internet, enabling them to communicate, exchange data, and perform intelligent actions. This interconnected network of "things" includes everyday objects such as household appliances, vehicles, wearable devices, industrial machinery, and even entire cities.
Introduction
The Internet of Things has gained immense popularity and has become an integral part of our lives. It has the potential to revolutionize industries, improve efficiency, enhance safety, and create new opportunities. IoT has been fueled by advancements in connectivity, miniaturization of sensors, data analytics, and the increasing availability of high-speed internet. With billions of devices connected, IoT has become a key driver of the digital transformation across various sectors.
Key Components of IoT
To understand how IoT works, it is important to recognize its key components. Firstly, sensors and devices play a crucial role in collecting and transmitting data. These devices can range from temperature and humidity sensors to smart thermostats, fitness trackers, and industrial machines. Secondly, connectivity and networking technologies such as Wi-Fi, Bluetooth, and cellular networks enable seamless communication between devices and the cloud. Thirdly, data processing and analytics platforms help make sense of the vast amounts of data generated by IoT devices, extracting valuable insights for decision-making. Lastly, user interfaces and applications provide a convenient way for users to interact with IoT .
IoT and its applications ..presentation on the latest emerging technologiesjana262
The document discusses Internet of Things (IoT) and its applications in various industries including finance. It defines IoT as interconnected computing devices, objects, animals or people that are provided with unique identifiers and can transfer data over a network. It explains that IoT uses sensors to connect people, systems and applications to collect and share data. Some key benefits of IoT for the finance industry discussed are improved customer service, real-time feedback, enabling IoT payments, improving credit risk management and fraud detection, streamlining operations and auditing. Challenges of IoT discussed are security issues, dealing with large amounts of redundant data, privacy concerns, and complexity of compliance.
In this presentation, Mukta introduces IoT and associated trends. Mukta is interested in IoT applications in healthcare, she talks about reports on BP and breathing habits to help users in managing health.
This document contains information about Mukta V Satish, a 2nd year Computer Science and Engineering student at Dayanand Sagar College of Engineering in Bangalore. It provides an introduction to the Internet of Things (IoT) which connects everyday devices to the network to increase efficiency and enable new services. Some key trends in IoT discussed are the Web of Things, improving industrial applications through real-time monitoring, secure cloud analytics using elastic computing, and machine-generated responses. The document also outlines Mukta's interests in cloud analytics and developing an application to monitor health vitals using wearables.
The document discusses the Internet of Things (IoT). It defines IoT as physical objects embedded with sensors and connectivity that allows them to collect and exchange data over networks. It outlines how IoT works using technologies like RFID, sensors, and smart/nano technologies. It discusses current applications of IoT and the future prospects. Advantages include improved data access, tracking, time savings, and cost reductions. Disadvantages comprise privacy/security risks, safety issues, data management complexity, and potential software hacking. The conclusion is that efforts must address disadvantages while using IoT to create a smarter world.
IRJET- Big Data Management and Growth EnhancementIRJET Journal
1. The document discusses big data management and growth, including definitions of big data, properties of big data like volume, variety, and velocity, and applications of big data in various domains.
2. It describes how big data is used in education to improve student outcomes, in healthcare to enable prevention and more personalized care, and in industries like banking and fraud detection to enhance customer segmentation and risk assessment.
3. Big data analytics refers to analyzing large and complex datasets to extract useful insights and make better decisions. The document provides examples of machine learning and predictive analytics techniques used for big data analysis.
Big Data is the process of harnessing massive Data – structured or unstructured via the means of sensors, actuators, embedded software’s, & network grids.
The IoT of Energy | From Smart Products to Intelligent SolutionsAdvisian
Rapid changes in consumer, business and industrial products and technologies, the proliferation of sensors and digital footprints and sophisticated data analytics are driving transformational shifts in many sectors. The energy sector has responded to this change with more energy efficient appliances, digital retail innovations and progressive smart grid investments, but this is modest relative to many others.
This document provides an overview of an IoT-based smart irrigation system. It begins with introductions to IoT, explaining what IoT is, why it is useful, and how IoT works. It then describes the key components used in an IoT system, including devices, gateways, cloud infrastructure, analytics, and user interfaces. Specific hardware and software used in the proposed smart irrigation system are also outlined, including sensors, microcontrollers, and programming languages. The document concludes with thanks.
In this presentation, Shiva introduces the topic of IoT and the associated trends. Mobile security is his interest area. His interest areas lie in designing energy efficient smart devices.
The document discusses the Internet of Things (IoT). It defines IoT as a system of interconnected computing devices, objects, and people that are provided with unique identifiers and can transfer data over a network. It describes how IoT works using technologies like sensors, communication capabilities, and data processing. It also outlines several applications of IoT such as smart homes, smart cities, industrial IoT, and more. Finally, it discusses some challenges of IoT including security, privacy, and reliability issues.
Cost-effective internet of things privacy-aware data storage and real-time an...IAESIJAI
It has been estimated that about 20 billion internet of things (IoT) devices are currently connected to the Internet. This has led to voluminous data generation which makes storaging, managing, and decision making on data to be challenging. Hence, exposes users’ privacy to be vulnerable to unauthorized people. To address these issues, this research proposed cost-effective storage for keeping and processing the IoT data in real-time. The proposed Fframework utilized a reliable hybridised data privacy model to protect the personal information of users. An empirically evaluation was done to identify the best models using data k-anonymity (KA), l-diversity (LD), t-closeness (TC), and differential privacy (DP). The performance evaluation of cloud computing and fog computing was done through simulations. The results obtained show that the combination of two data privacy models: differential privacy and k-anonymity models performed better than any individual model and any other combined models in the protection of users’ personal information. Lastly, fog computing was found to perform better than the cloud in terms of latency, energy consumption, network usage and execution time. In conclusion, the current study strongly recommends the use of hybridised privacy model of differential privacy (DP) and k-anonymity (KA) for the protection of IoT generated data privacy.
The Future of IoT Development Trends and Predictions.pdfDark Bears
The interconnected world we live in is about to experience another wave of transformation, all thanks to the rapid advancements in the Internet of Things (IoT). The phrase “The Future of IoT Development: Trends and Predictions” might sound like a glimpse into a science fiction novel, but it’s closer to reality than you might think.
Analytics Unleashed_ Navigating the World of Data Science.pdfkhushnuma khan
The 21st century has witnessed an unprecedented explosion in the volume, variety, and velocity of data. This deluge of information, often referred to as “Big Data,” has spurred the emergence of Data Science as a crucial discipline. Data Science integrates statistical methodologies, advanced programming, and domain expertise to analyze and interpret complex datasets. Its applications span diverse sectors, including business, healthcare, finance, and technology.
Zeshan Sattar- Assessing the skill requirements and industry expectations for...itnewsafrica
Zeshan Sattar- Senior Director of Industry Relations, COMPTIA- Assessing the skill requirements and industry expectations for cyber security at Public Sector Cybersecurity Summit 2024. #PublicSec2024
Here's how big data and the Internet of Things work together: a vast network of sensors (IoT) collect a boatload of information (big data) that is then used to improve services and products in various industries, which in turn generate revenue.
This document discusses fundamentals of IoT data analytics. It defines IoT analytics and explains challenges including dealing with large amounts of data, security issues, and misbehaving devices. It categorizes IoT data as either structured or unstructured, and as data in motion or at rest. Structured data fits a predefined model while unstructured data lacks structure. Data in motion passes through networks while data at rest is stored. Both predictive and prescriptive analytics provide more value but are more complex than descriptive or diagnostic analysis. Class activities involve capturing IoT data examples and presenting categorization and challenges.
Data Management for Internet of things : A Survey and DiscussionIRJET Journal
This document discusses data management for the Internet of Things (IoT). It begins with an abstract that outlines the need for improved data management techniques to handle the massive volumes of data produced by IoT devices. The document then provides background on IoT data characteristics that make traditional database solutions inadequate. It surveys current research in IoT data management and proposes a framework that considers the full data lifecycle from collection to deletion. Finally, it performs a gap analysis of existing solutions based on factors like data format, storage architecture, processing speed, and server response time.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
Outline
Introduction
Definition of the Internet of Things (IoT)
Importance and impact of IoT in today's world
Key Components of IoT
Sensors and devices
Connectivity and networking
Data processing and analytics
User interfaces and applications
Applications of IoT
Smart homes and home automation
Industrial IoT (IIoT) and smart manufacturing
Healthcare and medical applications
Transportation and logistics
Agriculture and farming
Environmental monitoring and conservation
Benefits and Advantages of IoT
Improved efficiency and productivity
Enhanced safety and security
Cost savings and resource optimization
Enhanced decision-making and automation
Improved quality of life
Challenges and Risks of IoT
Security and privacy concerns
Data management and storage
Interoperability and compatibility
Ethical and societal implications
Future Trends and Innovations in IoT
Edge computing and fog computing
5G connectivity and low-power networks
Artificial intelligence and machine learning
Blockchain technology and IoT
Conclusion
FAQs
How does IoT work?
Is IoT only applicable to large-scale industries?
Can IoT improve sustainability efforts?
What are the security risks associated with IoT?
Will IoT replace human jobs?
The Internet of Things (IoT) is a revolutionary concept that has transformed the way we interact with technology and the world around us. In simple terms, IoT refers to the interconnection of various devices, sensors, and systems through the internet, enabling them to communicate, exchange data, and perform intelligent actions. This interconnected network of "things" includes everyday objects such as household appliances, vehicles, wearable devices, industrial machinery, and even entire cities.
Introduction
The Internet of Things has gained immense popularity and has become an integral part of our lives. It has the potential to revolutionize industries, improve efficiency, enhance safety, and create new opportunities. IoT has been fueled by advancements in connectivity, miniaturization of sensors, data analytics, and the increasing availability of high-speed internet. With billions of devices connected, IoT has become a key driver of the digital transformation across various sectors.
Key Components of IoT
To understand how IoT works, it is important to recognize its key components. Firstly, sensors and devices play a crucial role in collecting and transmitting data. These devices can range from temperature and humidity sensors to smart thermostats, fitness trackers, and industrial machines. Secondly, connectivity and networking technologies such as Wi-Fi, Bluetooth, and cellular networks enable seamless communication between devices and the cloud. Thirdly, data processing and analytics platforms help make sense of the vast amounts of data generated by IoT devices, extracting valuable insights for decision-making. Lastly, user interfaces and applications provide a convenient way for users to interact with IoT .
IoT and its applications ..presentation on the latest emerging technologiesjana262
The document discusses Internet of Things (IoT) and its applications in various industries including finance. It defines IoT as interconnected computing devices, objects, animals or people that are provided with unique identifiers and can transfer data over a network. It explains that IoT uses sensors to connect people, systems and applications to collect and share data. Some key benefits of IoT for the finance industry discussed are improved customer service, real-time feedback, enabling IoT payments, improving credit risk management and fraud detection, streamlining operations and auditing. Challenges of IoT discussed are security issues, dealing with large amounts of redundant data, privacy concerns, and complexity of compliance.
In this presentation, Mukta introduces IoT and associated trends. Mukta is interested in IoT applications in healthcare, she talks about reports on BP and breathing habits to help users in managing health.
This document contains information about Mukta V Satish, a 2nd year Computer Science and Engineering student at Dayanand Sagar College of Engineering in Bangalore. It provides an introduction to the Internet of Things (IoT) which connects everyday devices to the network to increase efficiency and enable new services. Some key trends in IoT discussed are the Web of Things, improving industrial applications through real-time monitoring, secure cloud analytics using elastic computing, and machine-generated responses. The document also outlines Mukta's interests in cloud analytics and developing an application to monitor health vitals using wearables.
The document discusses the Internet of Things (IoT). It defines IoT as physical objects embedded with sensors and connectivity that allows them to collect and exchange data over networks. It outlines how IoT works using technologies like RFID, sensors, and smart/nano technologies. It discusses current applications of IoT and the future prospects. Advantages include improved data access, tracking, time savings, and cost reductions. Disadvantages comprise privacy/security risks, safety issues, data management complexity, and potential software hacking. The conclusion is that efforts must address disadvantages while using IoT to create a smarter world.
IRJET- Big Data Management and Growth EnhancementIRJET Journal
1. The document discusses big data management and growth, including definitions of big data, properties of big data like volume, variety, and velocity, and applications of big data in various domains.
2. It describes how big data is used in education to improve student outcomes, in healthcare to enable prevention and more personalized care, and in industries like banking and fraud detection to enhance customer segmentation and risk assessment.
3. Big data analytics refers to analyzing large and complex datasets to extract useful insights and make better decisions. The document provides examples of machine learning and predictive analytics techniques used for big data analysis.
Big Data is the process of harnessing massive Data – structured or unstructured via the means of sensors, actuators, embedded software’s, & network grids.
The IoT of Energy | From Smart Products to Intelligent SolutionsAdvisian
Rapid changes in consumer, business and industrial products and technologies, the proliferation of sensors and digital footprints and sophisticated data analytics are driving transformational shifts in many sectors. The energy sector has responded to this change with more energy efficient appliances, digital retail innovations and progressive smart grid investments, but this is modest relative to many others.
This document provides an overview of an IoT-based smart irrigation system. It begins with introductions to IoT, explaining what IoT is, why it is useful, and how IoT works. It then describes the key components used in an IoT system, including devices, gateways, cloud infrastructure, analytics, and user interfaces. Specific hardware and software used in the proposed smart irrigation system are also outlined, including sensors, microcontrollers, and programming languages. The document concludes with thanks.
In this presentation, Shiva introduces the topic of IoT and the associated trends. Mobile security is his interest area. His interest areas lie in designing energy efficient smart devices.
The document discusses the Internet of Things (IoT). It defines IoT as a system of interconnected computing devices, objects, and people that are provided with unique identifiers and can transfer data over a network. It describes how IoT works using technologies like sensors, communication capabilities, and data processing. It also outlines several applications of IoT such as smart homes, smart cities, industrial IoT, and more. Finally, it discusses some challenges of IoT including security, privacy, and reliability issues.
Cost-effective internet of things privacy-aware data storage and real-time an...IAESIJAI
It has been estimated that about 20 billion internet of things (IoT) devices are currently connected to the Internet. This has led to voluminous data generation which makes storaging, managing, and decision making on data to be challenging. Hence, exposes users’ privacy to be vulnerable to unauthorized people. To address these issues, this research proposed cost-effective storage for keeping and processing the IoT data in real-time. The proposed Fframework utilized a reliable hybridised data privacy model to protect the personal information of users. An empirically evaluation was done to identify the best models using data k-anonymity (KA), l-diversity (LD), t-closeness (TC), and differential privacy (DP). The performance evaluation of cloud computing and fog computing was done through simulations. The results obtained show that the combination of two data privacy models: differential privacy and k-anonymity models performed better than any individual model and any other combined models in the protection of users’ personal information. Lastly, fog computing was found to perform better than the cloud in terms of latency, energy consumption, network usage and execution time. In conclusion, the current study strongly recommends the use of hybridised privacy model of differential privacy (DP) and k-anonymity (KA) for the protection of IoT generated data privacy.
The Future of IoT Development Trends and Predictions.pdfDark Bears
The interconnected world we live in is about to experience another wave of transformation, all thanks to the rapid advancements in the Internet of Things (IoT). The phrase “The Future of IoT Development: Trends and Predictions” might sound like a glimpse into a science fiction novel, but it’s closer to reality than you might think.
Analytics Unleashed_ Navigating the World of Data Science.pdfkhushnuma khan
The 21st century has witnessed an unprecedented explosion in the volume, variety, and velocity of data. This deluge of information, often referred to as “Big Data,” has spurred the emergence of Data Science as a crucial discipline. Data Science integrates statistical methodologies, advanced programming, and domain expertise to analyze and interpret complex datasets. Its applications span diverse sectors, including business, healthcare, finance, and technology.
Zeshan Sattar- Assessing the skill requirements and industry expectations for...itnewsafrica
Zeshan Sattar- Senior Director of Industry Relations, COMPTIA- Assessing the skill requirements and industry expectations for cyber security at Public Sector Cybersecurity Summit 2024. #PublicSec2024
Irene Moetsana-Moeng: Stakeholders in Cybersecurity: Collaborative Defence fo...itnewsafrica
Irene Moetsana-Moeng, Executive Director and Head at Public Sector Agency on Stakeholders in Cybersecurity: Collaborative Defence for Cybersecurity Resilience at Public Sector Cybersecurity Summit 2024
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sectoritnewsafrica
Cobus Valentine, Chief Commercial Officer at Global Command & Control Technologies on Cybersecurity Threats and Solutions for the Public Sector at #PublicSec2024.
Varsha Sewlal- Cyber Attacks on Critical Critical Infrastructureitnewsafrica
Varsha Sewlal
Executive Legal & Deputy Information Officer, Railway Safety Regulator on Cyber Attacks on Critical Infrastructure at Public Sector Cybersecurity Summit 2024. #PublicSec2024
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...itnewsafrica
Abdul Kader Baba CIO, Infrastructure South Africa on Managing Cybersecurity Risks and Compliance Requirements in the Public Sector at Public Sector Cybersecurity. #PublicSec2024
Ansgar Pabst- Disruptive Innovation through Corporate Collaboration with Star...itnewsafrica
Ansgar Pabst, HOD, GMD Omnichannel at Pick n Pay, on Disruptive Innovation through Corporate Collaboration with Start-Ups, at this year's edition of Digital Retail Africa. #DRA2024 #DigitalRetailAfrica #CorporateCollaboration #CorporateInnovation #BusinessModel #Institutionalization #Intrapreneurship #SMMEs #Corporate #StartUps
Koen den Hollander- The Future is Omniitnewsafrica
Koen den Hollander, Co-founder -Omni-channel Retail Platform at Wolfpact, on The Future is Omni at this year's Digital Retail Africa. #DRA2024 #DigitalRetailAfrica #Omnichannel #eCommerce #RetailInsights #RetailSolutions #CustomerExperience
Wongama Millie- South African Social Media Insights 2023itnewsafrica
Wongama Millie, The Prestige Cosmetics Group's Head of Digital Marketing and Director of eCommerce, on South African Social Media Insights 2023, at this year's edition of Digital Retail Africa. #DRA2024 #DigitalRetailAfrica #CustomerInsights #SocialMedia #SocialMediaInsights #Customerbehaviour #2023Trends #InternetUse
Emphasising Personalization and Customer Journey Mapping in Digital Retailitnewsafrica
Martin Banda, Amazon Web Services (AWS) Solutions Architect, on Emphasising Personalization and Customer Journey Mapping in Digital Retail, at this year's edition of Digital Retail Africa. #DRA2024 #DigitalRetailAfrica #RetailSolutions #PersonalizedShopping #CustomerInsights #CustomerBehaviour #CustomerJourney #RetailInsights #Ecommerce
Munyaradzi Nyikavaranda- Assessing the intersect between UX, AI, Big Data: Cr...itnewsafrica
Munyaradzi Nyikavaranda, Former Group: Executive Head: Digital Analytics & Marketing Technology at Multichoice Group, on Assessing the intersect between UX, AI, Big Data: Creating personalized shopping experiences at this year's edition of Digital Retail Africa. #DRA2024 #DigitalRetailAfrica #ShoppingExperience #ConsumerExperienec #BigData #PersonalizedShopping
Data Analytics & Customer Insights as enablers of businesses to employ predic...itnewsafrica
Vukosi Sambo, Executive Head of Data, Insights & AI at AfroCentric & Medscheme Group, on Data Analytics & Customer Insights as enablers of businesses to employ predictive analytics at this year's edition of Digital Retail Africa. #DRA2024 #DigitalRetailAfrica #customerinsights #dataanalytics
Mark Cockerell- A New Era of Retail Data Integration Mark Cockerell Retail ...itnewsafrica
Mark Cockerell, Retail Director at Circana, on A New Ear of Retail Data Integration, at this year's edition of Digital Retail Africa. #DRA2024 #DigitalRetailAfrica
Pravir Ishvarlal- Artificial Intelligence in Healthcareitnewsafrica
Pravir Ishvarlal, Data Scientist at Netcare, on Artificial Intelligence in Healthcare, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Braden van Breda- The Role of AI, Robotics in African Healthcareitnewsafrica
Braden van Breda, CEO at AI Diagnostics, on The Role of AI, Robotics in African Healthcare, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Rodney Taylor- AVA Disrupts Primary Healthcare with the Latest Asynchronous I...itnewsafrica
Rodney Taylor, Managing Director at Guardian Eye, on AVA Disrupts Primary Healthcare with the Latest Asynchronous IoT Medical device and Telemedicine Platform, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Anish Gupta- Smart Care Coordination Platformitnewsafrica
Anish Gupta, Head- Products and Insights at Heaps (India), on Smart Care Coordination Platform at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Andrew Roberts- How Technology can Transform Healthcare for the Betteritnewsafrica
Andrew Roberts, Chief Information Officer at Clinix Health Group, on How Technology can Transform Healthcare for the Better, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Andrew Roberts - Mobile Health Apps for Improved Patient Engagement and Educa...itnewsafrica
Andrew Roberts, Chief Information Officer at Clinix Health Group, on Mobile Health Apps for Improved Patient Engagement and Education, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
This presentation by OECD, OECD Secretariat, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdfBen Linders
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Collapsing Narratives: Exploring Non-Linearity • a micro report by Rosie WellsRosie Wells
Insight: In a landscape where traditional narrative structures are giving way to fragmented and non-linear forms of storytelling, there lies immense potential for creativity and exploration.
'Collapsing Narratives: Exploring Non-Linearity' is a micro report from Rosie Wells.
Rosie Wells is an Arts & Cultural Strategist uniquely positioned at the intersection of grassroots and mainstream storytelling.
Their work is focused on developing meaningful and lasting connections that can drive social change.
Please download this presentation to enjoy the hyperlinks!
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Katharine Kemp, Associate Professor at the Faculty of Law & Justice at UNSW Sydney, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
This presentation by Professor Giuseppe Colangelo, Jean Monnet Professor of European Innovation Policy, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
Carrer goals.pptx and their importance in real lifeartemacademy2
Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.
This presentation by OECD, OECD Secretariat, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
2. Understanding IoT
u The Internet of Things, or IoT, refers
to the billions of physical devices
around the world that are now
connected to the internet, all
collecting and sharing data.
3. “
”
As Africa's digital transformation
unfolds, effective data management
and sophisticated analytics are key to
unlocking the true potential of IoT
5. Africa’s digital transformation
u Africa is currently witnessing a significant digital transformation, driven by
rapid advancements in technology and increased internet connectivity.
u Internet penetration is growing exponentially, with mobile connectivity
leading the charge.
u The rise in digital literacy and the availability of affordable smart devices are
also contributing to this digital boom.
6. Role of IoT in Africa’s Transformation
u The Internet of Things (IoT) is playing a crucial role in this digital
transformation. IoT refers to the network of physical objects—“things”—that
are embedded with sensors, software, and other technologies for the purpose
of connecting and exchanging data with other devices and systems over the
internet.
u From smart agriculture and healthcare to transportation and energy
management, IoT is creating new opportunities and solutions for long-
standing challenges in Africa.
7. The Importance of Effective Data
Management
u With IoT, comes the generation of massive amounts of data. Effective data
management is essential for the successful implementation and operation of
IoT systems.
u This involves ensuring that data from various IoT devices is accurately
collected, securely stored, efficiently processed, and readily available for
use.
u Poor data management can lead to significant issues such as data breaches,
loss of data, or incorrect data analysis, which can hinder the performance and
potential benefits of IoT systems.
8. Sophisticated Analytics: From Raw Data to
Valuable Insights
u Beyond managing the data, deriving meaningful insights from it is vital. This is
where sophisticated analytics come into play.
u By applying advanced analytics techniques to the data collected by IoT
devices, organizations can gain real-time insights, improve decision-making,
predict future trends, and identify potential issues before they occur.
u For example, predictive analytics can be used in smart farming to predict
weather patterns and determine the optimal time for planting crops, thereby
increasing yield and profitability.
9. Data Management
u Data management is the
practice of collecting,
organizing, protecting, and
storing an organization's
data so it can be analyzed
for business decisions.
10. Data Management in IoT
Effective data management is crucial in an IoT infrastructure. It involves
collecting, validating, storing, protecting, and processing required data to
ensure its accessibility, reliability, and timeliness
u Data Collection & Validation: "IoT devices generate vast amounts of data that
need to be accurately collected and validated for quality and integrity.”
u Data Storage & Security: "The collected data must be securely stored and
protected from any potential breaches or loss.”
u Data Processing: "Data is processed and transformed into a usable format for
further analysis."
11. Analytics
u Analytics is the scientific process of
discovering and communicating the
meaningful patterns which can be
found in data.
u It is concerned with turning raw
data into insight for making better
decisions. Analytics relies on the
application of statistics, computer
programming, and operations
research in order to quantify and
gain insight to the meanings of data.
It is especially useful in areas which
record a lot of data or information.
12. Analytics in IoT
Analytics is the systematic computational analysis of data. In IoT, it helps
derive valuable insights from raw data, drive decisions, and predict future
trends.
u Descriptive Analytics: "This form presents data in a way that interprets the
past. It helps understand what has happened.”
u Predictive Analytics: "Using historical data, predictive analytics forecasts
future events. It's particularly helpful in preventive maintenance of IoT
devices.”
u Prescriptive Analytics: "It suggests various course of actions to eliminate
future issues. This can be beneficial in automated decision-making systems."
13. “
”
IoT can act as a catalyst for
technological growth and
socioeconomic development in Africa
by leveraging effective data
management and sophisticated
analytics.