The document discusses methodologies for data science and the Internet of Things (IoT). It begins by noting that there is currently no single agreed upon methodology for solving data science problems for IoT (IoT analytics). It then poses some initial questions on whether a distinct IoT data science methodology is needed, and if IoT problems warrant a specific approach. While IoT analytics problems are typical data science problems, the document notes there are some unique considerations for IoT, such as the use of hardware, high data volumes, and streaming data.
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
A Unified Semantic Engine for Internet of Things and Smart Cities: From Senso...Amélie Gyrard
A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications
The 8th IEEE International Conference on Internet of Things (iThings 2015), 11-13 December 2015, Sydney, Australia
Amelie Gyrard, Martin Serrano
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
[Srijan Wednesday Webinars] Artificial Intelligence & the Future of BusinessSrijan Technologies
“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu
Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses.
This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work.
Key Takeaways:
- Define AI and ML, and the philosophy behind these new technologies
- The impact of AI on jobs, communities, business, and industry
- The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc.
-Introduction to machine learning algorithms like classification, regression, neural networks etc.
Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars
Artificial intelligence in practice- part-1GMR Group
Summary is made in 5 parts-
This is Part -1
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe.
• The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment.
• Artificial intelligence and machine learning are cited as the most important modern business trends to drive success.
• It is used in areas ranging from banking and finance to social media and marketing.
• This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries.
• This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others.
• This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution.
• Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:
o Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations
o Expand your knowledge of recent AI advancements in technology
o Gain insight on the future of AI and its increasing role in business and industry
o Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the trans-formative power of technology in 21st century commerce
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
A Unified Semantic Engine for Internet of Things and Smart Cities: From Senso...Amélie Gyrard
A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications
The 8th IEEE International Conference on Internet of Things (iThings 2015), 11-13 December 2015, Sydney, Australia
Amelie Gyrard, Martin Serrano
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
[Srijan Wednesday Webinars] Artificial Intelligence & the Future of BusinessSrijan Technologies
“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu
Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses.
This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work.
Key Takeaways:
- Define AI and ML, and the philosophy behind these new technologies
- The impact of AI on jobs, communities, business, and industry
- The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc.
-Introduction to machine learning algorithms like classification, regression, neural networks etc.
Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars
Artificial intelligence in practice- part-1GMR Group
Summary is made in 5 parts-
This is Part -1
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe.
• The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment.
• Artificial intelligence and machine learning are cited as the most important modern business trends to drive success.
• It is used in areas ranging from banking and finance to social media and marketing.
• This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries.
• This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others.
• This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution.
• Each case study provides a comprehensive overview, including some technical details as well as key learning summaries:
o Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations
o Expand your knowledge of recent AI advancements in technology
o Gain insight on the future of AI and its increasing role in business and industry
o Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the trans-formative power of technology in 21st century commerce
Investor's view on machine intelligence startups, 2.0, Jan 2017Victor Osyka
Updated deeper overview of investor's look at machine learning / deep learning startups, with slight Russian accent. =)
Some slides are courtesy of Russia.ai and personally great friend @Petr Zhegin:
#23, #28 are from http://www.russia.ai/single-post/2016/09/21/Ten-Russian-speaking-venture-capital-funds-one-may-consider-to-back-an-AI-startup
#30 insights are from http://www.slideshare.net/RussiaAI/artificial-intelligence-investment-trends-and-applications-h1-2016
Victor Osyka of Almaz Capital, http://fb.com/victor.osika, http://medium.com/@victorosyka
Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.
https://www.learntek.org/machine-learning-using-spark/
https://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Fi cloudpresentationgyrardaugust2015 v2Amélie Gyrard
Cross-Domain Internet of Things Application Development: M3 Framework and Evaluation
FiCloud 24-26 August 2015, Rome, Italy
Semantic Web technologies, Semantic Interoperability,
Semantic Web Of Things (SWoT), Internet of Things (IoT), Web of Things (WoT), Machine to Machine (M2M), Ubiquitous Computing, Pervasive Computing, Context Awareness
Linked Open Vocabularies for Internet of Things (LOV4IoT),
Sensor-based Linked Open Rules (S-LOR),
Machine-to-Machine Measurement (M3) framework,
sharing and reusing domain knowledge
Artificial Intelligence (AI) is nowadays used frequently in many application domains. Although sometimes considered only as an afterthought in the public discussion compared to other domains such as health, transportation, and manufacturing, the media domain is also transformed by AI enabling new opportunities, from content creation e.g. “robojournalism” and individualised content to optimisation of the content production and distribution. Underlaying many of these new opportunities is the use of AI in its current reincarnation as deep learning for understanding the audio-visual content by extracting structured information from the unstructured data, the audio-visual content.
In this talk the current understanding and trends of AI will therefore be discussed, what can be done, what is done, and what challenges remain in the use of AI especially in the context of media applications and services. The talk is not so much focused on the details and fundamentals of deep learning, but rather on a practical perspective on how recent advances in this field can be utilised in use-cases in the media domain, especially with respect to audio-visual content and in the broadcasting domain.
Artificial intelligence (Ai) is back, and the tech industry’s interest is stronger than ever. Ai will have an important impact on the design and creation of software. Application development and delivery (AD&D) professionals need to understand the potential benefits Ai will bring, not only to how they build software but also to the nature of the applications themselves. in parallel, AD&D pros should not ignore the challenges and risks that come with Ai. this report is the first of a series that will examine the impact of Ai on software development and separate myth from reality
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
Artificial intelligence (AI) is a multidisciplinary field of science whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world.
This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Talent: Supply, demand and concentration of talent working in the field.
Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
Research trends on CAPTCHA: A systematic literature IJECEIAES
The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain.
Ethical AI: Establish an AI/ML Governance framework addressing Reproducibility, Explainability, Bias & Accountability for Enterprise AI use-cases.
Presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (https://events.linuxfoundation.org/open-compliance-summit/)
Full article: https://towardsdatascience.com/ethical-ai-its-implications-for-enterprise-ai-use-cases-and-governance-81602078f5db
My talk about data and information models for IoT, how ontologies can establish the relationship between IoT devices, and how Eclipse Vorto could accommodate ontological information. Briefly features Eclipse Smarthome.
Investor's view on machine intelligence startups, 2.0, Jan 2017Victor Osyka
Updated deeper overview of investor's look at machine learning / deep learning startups, with slight Russian accent. =)
Some slides are courtesy of Russia.ai and personally great friend @Petr Zhegin:
#23, #28 are from http://www.russia.ai/single-post/2016/09/21/Ten-Russian-speaking-venture-capital-funds-one-may-consider-to-back-an-AI-startup
#30 insights are from http://www.slideshare.net/RussiaAI/artificial-intelligence-investment-trends-and-applications-h1-2016
Victor Osyka of Almaz Capital, http://fb.com/victor.osika, http://medium.com/@victorosyka
Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.
https://www.learntek.org/machine-learning-using-spark/
https://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Fi cloudpresentationgyrardaugust2015 v2Amélie Gyrard
Cross-Domain Internet of Things Application Development: M3 Framework and Evaluation
FiCloud 24-26 August 2015, Rome, Italy
Semantic Web technologies, Semantic Interoperability,
Semantic Web Of Things (SWoT), Internet of Things (IoT), Web of Things (WoT), Machine to Machine (M2M), Ubiquitous Computing, Pervasive Computing, Context Awareness
Linked Open Vocabularies for Internet of Things (LOV4IoT),
Sensor-based Linked Open Rules (S-LOR),
Machine-to-Machine Measurement (M3) framework,
sharing and reusing domain knowledge
Artificial Intelligence (AI) is nowadays used frequently in many application domains. Although sometimes considered only as an afterthought in the public discussion compared to other domains such as health, transportation, and manufacturing, the media domain is also transformed by AI enabling new opportunities, from content creation e.g. “robojournalism” and individualised content to optimisation of the content production and distribution. Underlaying many of these new opportunities is the use of AI in its current reincarnation as deep learning for understanding the audio-visual content by extracting structured information from the unstructured data, the audio-visual content.
In this talk the current understanding and trends of AI will therefore be discussed, what can be done, what is done, and what challenges remain in the use of AI especially in the context of media applications and services. The talk is not so much focused on the details and fundamentals of deep learning, but rather on a practical perspective on how recent advances in this field can be utilised in use-cases in the media domain, especially with respect to audio-visual content and in the broadcasting domain.
Artificial intelligence (Ai) is back, and the tech industry’s interest is stronger than ever. Ai will have an important impact on the design and creation of software. Application development and delivery (AD&D) professionals need to understand the potential benefits Ai will bring, not only to how they build software but also to the nature of the applications themselves. in parallel, AD&D pros should not ignore the challenges and risks that come with Ai. this report is the first of a series that will examine the impact of Ai on software development and separate myth from reality
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
Artificial intelligence (AI) is a multidisciplinary field of science whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world.
This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Talent: Supply, demand and concentration of talent working in the field.
Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
Research trends on CAPTCHA: A systematic literature IJECEIAES
The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain.
Ethical AI: Establish an AI/ML Governance framework addressing Reproducibility, Explainability, Bias & Accountability for Enterprise AI use-cases.
Presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (https://events.linuxfoundation.org/open-compliance-summit/)
Full article: https://towardsdatascience.com/ethical-ai-its-implications-for-enterprise-ai-use-cases-and-governance-81602078f5db
My talk about data and information models for IoT, how ontologies can establish the relationship between IoT devices, and how Eclipse Vorto could accommodate ontological information. Briefly features Eclipse Smarthome.
Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise ...Dataconomy Media
“Enterprise AI - Artificial Intelligence for the Enterprise."
AI is impacting many areas today. This talk discusses how AI will impact the Enterprise and what it means in the near future. The talk is based on my course I teach at the University of Oxford.
IoT and machine learning - Computational Intelligence conferenceAjit Jaokar
Slides for IoT and Machine learning talk. Sign up at Sign up at www.futuretext.com to get forthcoming copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning algorithms for Smart cities
Dynamic Semantics for the Internet of Things PayamBarnaghi
Ontology Summit 2015 : Track A Session - Ontology Integration in the Internet of Things - Thu 2015-02-05,
http://ontolog-02.cim3.net/wiki/ConferenceCall_2015_02_05
Data Summer Conf 2018, “Architecting IoT system with Machine Learning (ENG)” ...Provectus
In this presentation, the speaker will share his experiences from building successful IoT systems. He will also explain why many IoT systems fail to get traction and how Machine Learning can help in that. Finally, he will talk about the right system architecture and touch upon some of the ML algorithms for IoT systems.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "Imaging + AI: Opportunities Inside the Car and Beyond" at the December 2016 Embedded Vision Alliance Member Meeting. Bünger presents his firm’s perspective on how embedded vision will upend the automotive industry.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
4. Copyright : Futuretext Ltd. London3
Ajit Jaokar
-
Data Science for IoT @Oxford Uni + UPM(Smart cities) + Online
Next book part of Stanford Uni course
In 2015, Ajit was included in 16 Top Data Science bloggers on Data Science
Central, Top 100 blogs on KDnuggets and Top 50 people to follow on Twitter by
IoT central for IoT.
World Economic Forum Spoken at MWC(5 times), CEBIT, CTIA, Web 2.0, CNN,
BBC, Oxford Uni, Uni St Gallen, European Parliament. @feynlabs – teaching
kids Computer Science. Adivsory – Connected Liverpool
www.opengardensblog.futuretext.com
5. Copyright : Futuretext Ltd. London4
Data Science for Internet of Things – practitioner course – March
2016
Now running in it’s second batch ..
Welcome to the world’s first course that helps you to become a
Data Scientist for the Internet Of Things ..
6. Copyright : Futuretext Ltd. London5
Ajit Jaokar
The Big Picture – The Data Science and IoT landscape
7. Copyright : Futuretext Ltd. London
Internet of Things
CNN,
RNN
Data Lake
Event
Based
analysis
Rules/
Workflow
Edge
Processing
Engine Rules/
Workflow
Alerts
Trigger s
Actions
Cloud / Data LakeEdge Device
Event
Collector
Predictive Alerts
Stream Processing System
Event
Store
Analytics
Model
Build Model
HDFS
Batch Processing System
Validate
Event
Sequence
CNN,
RNN
Data Lake
Event
Based
analysis
CEP
CEP
CEP
9. Copyright : Futuretext Ltd. London8
As the term Internet of Things implies (IOT) – IOT is about Smart
objects
For an object (say a chair) to be ‘smart’ it must have three things
- An Identity (to be uniquely identifiable – via iPv6)
- A communication mechanism(i.e. a radio) and
- A set of sensors / actuators
+
Physical context(ex location)
Social context
+
Decisions at the ‘edge’ ex with sensor fusion and even in offline mode
Workflow – (IFTTT) often also at the edge –
Thus, IOT is all about Data ..
IoT != M2M (M2M is a subset of IoT)
10. Copyright : Futuretext Ltd. London9
Ajit Jaokar
Many of the consumer IOT cases will happen with iBeacon in the next
two years
11. Copyright : Futuretext Ltd. London10
Ajit Jaokar
And 5G will provide the WAN connectivity 5G - Source – Ericsson
12. Copyright : Futuretext Ltd. London
Closed Loop Message –
Response System
Senso
rs
Rules/
Workflow
Edge Processor
Rules/
Workflow
Analytic Workbench: Operational
Investigative, Predictive Analytics
and Machine Learning
Possible
Specialized Store
Enterprise Apps:
ERP, CRM, and
other enterprise
apps
Alerts
Trigger
Actions
Cloud Based
Central Repository
Source: http://events.linuxfoundation.org/sites/events/files/slides/EdgeProcessing-
allseenalliance_4x3_template_24sept2014.pdf
13. Copyright : Futuretext Ltd. London12
iOt relates to Automation in three key areas based on Sensing and Predicting
a) Move from exception handling to patterns of exceptions over time.(are
some exceptions occurring repeatedly? Do I need to redsign my product, Is that a
new product?) –
b) Move from optimization to disruption – ownership to rental ship (Where are all
these dynamic assets?)
c) Move to self learning: Robotics: From assembly line to self learning
robots(Boston Dynamics), autonomous helicopters
14. Copyright : Futuretext Ltd. London13
Machines generate Data - Types of Big Data
Status Data almost everything will have a status data. This will create
vast amounts of data – much of it will be summarized at the ‘edge’
Location Data: Almost everything will have location data even if that
location is static. Things will be in transit (where is my product/car etc etc)
Machines taking action: Thermostat is automatically reduced
Actionable Data: Data in human actionable form – workflow – IFTTT
Machines learning by themselves in areas where there are no
‘rules’ – Most interesting space – best example is Deep Learning
15. Copyright : Futuretext Ltd. London14
Data Science for IoT: The role of hardware in analytics
Processing at the Edge (which Cisco and others have called Fog Computing).
Alternately, we see entirely new classes of hardware specifically involved in
Data Science for IoT(such as synapse chip for Deep learning)
17. Copyright : Futuretext Ltd. London16
Different Data Formats
POS data
Social media
External feeds
Payments
Log data
Telephone
conversations
RFID Scans
Events
Emails
Sensors
Free-form text
Geospatial
Audio
Still images/videos
Transactions
Call center notes
Adapted from Ravi Kalakota PhD
18. Copyright : Futuretext Ltd. London
IoT Reference Stack
Portal Dashboard
API
Manageme
ntEvent Processing and Analytics
Aggregation / Bus Layer
ESB and Message Broker
Devices
Communications
MQTT / HTTP/COAP
DeviceMgr
Identity&AccessManagement
Protocols
Standards
Industrial Internet Consumer Governance
Smart
Grid
Manufacturi
ng
Logistic&
Transpor
tation
Robotics
Connecte
d Car
Wearabl
es
Health
Public
Safety
Smart
Cities
Retail
19. Copyright : Futuretext Ltd. London
Multiple Protocols of IOT
HTTP/ REST, MQTT, COAP, etc
TCP, UDP
IPV6, IPV6 w 6LOWPAN, etc
Wireless (802.15.4, Wifi, BLE,
etc.)
Higher layer protocols
‒ Application
‒ Transport
‒ Network
Higher layer protocols
‒ Link layer
29. Copyright : Futuretext Ltd. London28
What is Machine Learning?
Mitchell's Machine Learning
Tom Mitchell in his book Machine Learning “The field of machine learning is c
oncerned with the question of how to construct computer
programs that automatically improve with experience.”
formally:
“A computer program is said to learn from experience E with respect to
some class of tasks T and performance measure P, if its performance at
tasks in T, as measured by P, improves with experience E.”
Think of it as a design tool where we need to understand:
What data to collect for the experience (E)
What decisions the software needs to make (T) and
How we will evaluate its results (P).
A programmers perspective:
Machine Learning involves:
a) Training of a model from data
b) Predicts/ Extrapolates a decision
c) Against a performance measure.
30. Copyright : Futuretext Ltd. London29
Technique Applicability Algorithms
Classification Most commonly used
technique for predicting a
specific outcome such as
response / no-response, high /
medium / low-value
customer, likely to buy / not
buy.
Logistic Regression —classic
statistical technique but now
available inside the Oracle
Database and supports text
and transactional data
Naive Bayes —Fast, simple,
commonly applicable
Support Vector Machine—
Next generation, supports text
and wide data
Decision Tree —Popular,
provides human-readable
rules
Source: Oracle
31. Copyright : Futuretext Ltd. London30
Regression Technique for predicting
a continuous numerical
outcome such as customer
lifetime value, house
value, process yield rates.
Multiple Regression —
classic statistical
technique but now
available inside the
Oracle Database and
supports text and
transactional data
Support Vector Machine
—Next generation,
supports text and wide
data
Attribute Importance Ranks attributes
according to strength of
relationship with target
attribute. Use cases
include finding factors
most associated with
customers who respond to
an offer, factors most
associated with healthy
patients.
Minimum Description
Length—Considers each
attribute as a simple
predictive model of the
target class
Source: Oracle
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Anomaly Detection Identifies unusual or
suspicious cases based on
deviation from the norm.
Common examples include
health care fraud, expense
report fraud, and tax
compliance.
One-Class Support Vector
Machine —Trains on
"normal" cases to flag
unusual cases
Clustering Useful for exploring data and
finding natural groupings.
Members of a cluster are
more like each other than
they are like members of a
different cluster. Common
examples include finding
new customer segments, and
life sciences discovery.
Enhanced K-Means—
Supports text mining,
hierarchical clustering,
distance based
Orthogonal Partitioning
Clustering—Hierarchical
clustering, density based
Expectation Maximization—
Clustering technique that
performs well in mixed data
(dense and sparse) data
mining problems.
Source: Oracle
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Association Finds rules associated with
frequently co-occuring
items, used for market
basket analysis, cross-sell,
root cause analysis. Useful
for product bundling, in-
store placement, and defect
analysis.
Apriori—Industry standard
for market basket analysis
Feature Selection and Extraction Produces new attributes as
linear combination of
existing attributes.
Applicable for text data,
latent semantic analysis,
data compression, data
decomposition and
projection, and pattern
recognition.
Non-negative Matrix
Factorization—Next
generation, maps the
original data into the new
set of attributes
Principal Components
Analysis (PCA)—creates
new fewer composite
attributes that respresent
all the attributes.
Singular Vector
Decomposition—
established feature
extraction method that has
a wide range of
applications.
Source: Oracle
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Ajit Jaokar
KEY CONCEPTS – DATA SCIENCE AND IOT
Deep learning
Big Data
Complex event Processing
Streaming
36. Copyright : Futuretext Ltd. London
Internet of Things
CNN,
RNN
Data Lake
Event
Based
analysis
Rules/
Workflow
Edge
Processing
Engine Rules/
Workflow
Alerts
Trigger s
Actions
Cloud / Data LakeEdge Device
Event
Collector
Predictive Alerts
Stream Processing System
Event
Store
Analytics
Model
Build Model
HDFS
Batch Processing System
Validate
Event
Sequence
CNN,
RNN
Data Lake
Event
Based
analysis
CEP
CEP
CEP
38. Copyright : Futuretext Ltd. London37
In a groundbreaking paper published today in Nature, a team of
researchers led by DeepMind co-founder Demis Hassabis reported
developing a deep neural network that was able to learn to play such
games at an expert level. What makes this achievement all the more
impressive is that the program was not given any background
knowledge about the games. It just had access to the score and the
pixels on the screen.
It didn’t know about bats, balls, lasers or any of the other things we
humans need to know about in order to play the games.
But by playing lots and lots of games many times over, the computer
learnt first how to play, and then how to play well.
39. Copyright : Futuretext Ltd. London38
Deep Learning and Feature learning
Deep Learning can be hence seen as a more complete, hierarchical and a
‘bottom up’ way for feature extraction and without human intervention.
Source: ELEG 5040 Advanced Topics on Signal Processing (Introduction to
Deep Learning) by Xiaogang Wang
42. Copyright : Futuretext Ltd. London
Internet of Things
CNN,
RNN
Data Lake
Event
Based
analysis
Rules/
Workflow
Edge
Processing
Engine Rules/
Workflow
Alerts
Trigger s
Actions
Cloud / Data LakeEdge Device
Event
Collector
Predictive Alerts
Stream Processing System
Event
Store
Analytics
Model
Build Model
HDFS
Batch Processing System
Validate
Event
Sequence
CNN,
RNN
Data Lake
Event
Based
analysis
CEP
CEP
CEP
45. Copyright : Futuretext Ltd. London
Optional Storage
And Queries
Real-time
Feeds
Stream Processing Application
Alerts
Actions
Memory
Disk
Source: The 8 Requirements of Real-Time Stream Processing
By Michael Stonebraker et al
46. Copyright : Futuretext Ltd. London
Kafka
Producers
Brokers
Consumers
Front End Front End Front End Service
Hadoop
Clusters
Security
systems
Real-time
monitorin
g
Other
consumer
service
Data
warehous
e
47. Copyright : Futuretext Ltd. London
NoSql
HDFSData
Sources
Stream Processing Architecture based on Apache Spark
Adapted from
http://ingest.tips/2015/06/24/real-time-analytics-with-kafka-and-spark-streaming/
49. Copyright : Futuretext Ltd. London
Internet of Things
CNN,
RNN
Data Lake
Event
Based
analysis
Rules/
Workflow
Edge
Processing
Engine Rules/
Workflow
Alerts
Trigger s
Actions
Cloud / Data LakeEdge Device
Event
Collector
Predictive Alerts
Stream Processing System
Event
Store
Analytics
Model
Build Model
HDFS
Batch Processing System
Validate
Event
Sequence
CNN,
RNN
Data Lake
Event
Based
analysis
CEP
CEP
CEP
50. Copyright : Futuretext Ltd. London49
For example:
• Complex event processing involves combining outputs of multiple
sensors and inferring events from readings even when the event is not
directly observed by a specific sensor. For Complex event processing, we
also need to add statistical models such as likelihood, confidence and
probability using techniques like Bayesian networks, neural networks,
Dempster-Shafer methods, kalman filters etc (ex care home – image
Guardian)
51. Copyright : Futuretext Ltd. London
Quaternions
Heading
Pitch, roll and
yawLinear
acceleration
Gravity
Sensor fusion
algorithm
Inputs Outputs
3 –axis earth magnetic field
3 –axis linear acceleration
3 –axis angular rate
Source: ST microsystems
53. Copyright : Futuretext Ltd. London52
Creating an open methodology for Internet of Things (IoT)
Analytics: Data science for Internet of Things
January 9, 2016 By ajit Leave a Comment
54. Copyright : Futuretext Ltd. London53
There is no specific methodology to solve Data Science for IoT (IoT
Analytics) problems.
This leads to some initial questions:
Should there be a distinct methodology to solve Data Science problems for
IoT?
Are IoT problems for Data Science unique enough to warrant a specific
approach?
What existing methodologies should we draw upon?
On one hand , A Data Science for IoT problem is a typical Data Science
problem. On the other hand, there are some unique considerations to IoT –
for example in the use of Hardware, High Data volumes, Use of
CEP(Complex event processing), impact of verticals(like automotive),
Impact of streaming data etc.
55. Copyright : Futuretext Ltd. London54
Background and inspiration
Some initial background:
Data mining has well known methodologies such as Crisp DM. Hilary Mason
and others have also proposed specific methodologies for Data Science .
Kaggle problems have a specific approach to solving them . With techniques
like PFA(Portable format for Analytics) provide a way of formalizing and
moving Analytics models.
All these strategies also apply to IoT. IoT itself has methodologies like Ignite
IoT – but these do not cover IoT analytics in detail.
A methodology for IoT analytics(Data Science for IoT) should cover the
unique aspects of each step in Data Science. For example: It is more than
the choice of the model family. The choice of the model family (ANN, SVM,
Trees, etc) is only one of the many choices to make – Others include :
56. Copyright : Futuretext Ltd. London55
a) Choice of the model structure – optimisation methodology (CV,
Bootstrap, etc)
b) Choice of the model parameter optimisation algorithm (joint gradients
vs. conjugate gradients )
c) Preprocessing of the data (centring, reduction, functional reduction, log-
transform, etc.)
d) How to deal with missing data (case deletion, imputation, etc.)
e) How to detect and deal with suspect data (distance-based outlier
detection, density-based, etc.)
f) How to choose relevant features (filters, wrappers, embedded method ?)
g) How to measure prediction performances (mean square error, mean
absolute error, misclassification rate, lift, precision/recall, etc.)
source Methodology and standards for data analysis with machine learning
tools Damien Fran¸cois ∗
57. Copyright : Futuretext Ltd. London56
The methodology could also cover -
Exploratory analysis of data
Hypothesis testing (“Given a sample and an apparent effect, what is the
probability of seeing such an effect by chance?” )
and other ideas ..
Who?
Ajit Jaokar – futuretext
Jean-Jacques (JJ) Bernard, management & technology consultant
Shiva soleimani – student - Isfahan university
59. Copyright : Futuretext Ltd. London58
Data Science for Internet of Things – practitioner course – March
2016
Now running in it’s second batch ..
Welcome to the world’s first course that helps you to become a
Data Scientist for the Internet Of Things ..
60. Copyright : Futuretext Ltd. London59
Weekly schedule
Concepts
Week 0 March 15 Orientation, introductions, Personal learning plans, Platform
signup
Week 1 mar 21 Foundations:An analytics Driven Organization – IoT and
Machine Learning - Data Science for IoT – Unique
characteristics – Data Science for IoT – why now?
Mar 28 Machine Learning concepts Deep Learning concepts
Apr 4 An introduction to IoT (Internet of Things)
Apr 11 IoT platforms – From sensor to Cloud
Apr 18 Concepts of Big Data Part One
Apr 25 Concepts of Big Data Part Two
May 2 Market drivers for IoT
May 9 Choosing a model – what technique to Use?
May 16 Use Cases and IoT datasets (these will continue throughout
the course)
May 23 Time series and NoSQL databases
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May 30 Streaming analytics part One
June 6 Streaming analytics part two
June 13 Deep learning part one
June 20 Deep learning part two
June 2 7 Machine learning algorithms – part one
July 4 Machine learning algorithms – part two
July 11 Mathematical foundations – part one
July 18 Mathematical foundations – part two
July To Dec 31 Project
Contact us at info@futuretext.com to signup
62. Copyright : Futuretext Ltd. London61
Programming
Week 0 Mar 15 Orientation, introductions, Personal
learning plans, Platform signup
Week 1 mar 21
Mar 28
Apr 4 Intro to R, Installations, Basics of R
Apr 11
Apr 18 Data Frames in R & Tabular Data
Apr 25
May 2 Data Processing & Data Visualization in R
May 9
May 16 Scala basics
May 23
May 30 Spark batch processing I
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June 6
June 13 Spark Batch Processing II
June 20
June 2 7 Spark SQL
July 4
July 11 Spark Streaming
July 18
July To Dec 31 Projects
Contact us at info@futuretext.com to signup
65. Copyright : Futuretext Ltd. London64
A Reference Architecture for the Internet of Things
Daniel Karzel, Hannelore Marginean, Tuan-Si Tran
adapted from defined by IoT-A
The IoT interconnects the Things in order to exchange information to fulfill
tasks for the users. Ideas of fridges communicating not only with your
smart-phone, but with the producer's server farm or an energy power plant
will soon become reality.
Terminology:
• Thing: An object of our everyday life placed in our everyday
environment. A thing can be a car, fridge but can also be abstracted to a
complete house or city depending on the use case.
• Device: A sensor, actuator or tag. Usually the device is part of a thing.
The thing processes the devices’ context information and communicates
selected information to other things. Furthermore, the thing can pass
actions to actuators.
• Interoperability and Integration components
• Context aware components
• Middleware components(load balancing etc)
• Security
66. Copyright : Futuretext Ltd. London65
Anind K. Dey’s context toolkit. The context toolkit was designed on an
application level, as it was designed for Geographical Information Systems
(GIS). In the IoT we have to extend the context toolkit towards the
intercommunication between things. However, the basic idea of goal,
context information and resulting actions remains in the IoT world.
67. Copyright : Futuretext Ltd. London66
In the IoT world we don’t only define the goal on the user level (i.e. by
application), but things themselves can work towards certain goals without
actively including the user. In the end the devices still serve the user but
they act autonomously in the background – which is exactly the idea
of ubiquitous computing.
Context defines the state of an environment (usually the user’s
environment) in a certain place at a certain time. The context model usually
distinguishes between context elements and context situation.
Context elements define specific context, usually on the device level. A
context element can be for example a temperature value at a certain time
and location.
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Location and time are context elements themselves, but they play a special
role as they are needed to locate sensor values in space and time. Without
knowing where and when a temperature was measured the temperature
does not help much for making conclusions.
The context situation is an aggregation of context elements. The context
situation is thus a view on the environment in a certain location at a certain
time.
Similarly to the context model you can also define an action model that
defines what things can trigger (e.g. open a window, take a photo). Actions
can only be triggered with the combination of context information (e.g. a
context situation) and defined goals. Goals are usually depicted as rules of
a rule engine (e.g. IF temperature > 25* THEN open window).
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Consists of 6 layers. Besides these layers there are two “cross-section-
layers” that affect all other layers, namely “Security” and “Management”.
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The device integration layer connects all the different device types and
consumes device measurements as well as it communicates actions (on
device level). This layer can be seen as a translator that speaks many
languages. The output of the sensors and tags depends on the protocol
they implement. The input of the actuators is also defined by the protocol
they implement.
74. Copyright : Futuretext Ltd. London73
The device management is in charge of taking device registrations and
sensor-measurements from the device integration layer. Furthermore it
communicates status changes for actuators down to the device integration
layer. The device integration layer then just validates that the status change
(i.e. the action) is conform with the actuator and then translates the status
change to the actuator.
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The data management can be seen as a central database that holds all
data of a “thing”, but this is only one possible implementation. For larger
things within the system (e.g. a device life-cycle monitoring system
collecting data from other things) data management might be a data
warehouse or even a complete data farm. The implementation of the data
management layer thus strongly depends on the use-case for the specific
thing.
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The context management defines the central business logic and is
responsible for six tasks: 1. Define the goals of the thing. 2. Consume the
context situation(s) of other things 3. Produce the (own) context situation
of the thing. 4. Evaluate the (own) context situation towards the goal. 5.
Trigger actions that help to fulfill the goal according to the evaluated rules.
6. Publish context situations for other things.
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According to these tasks we can divide the context management into eight
components as shown below.
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Rule Engine & Artificial Intelligence (AI): Define and manage all of the rules
necessary for context evaluation. This includes the goal (which is basically
as set of rules) as well as rules for creating the context situation and
actions.
Context Situation Integration Module: Listens to context situations of other
things and integrates the incoming context situations.
Action Integration Module: Incoming actions of other things are evaluated
and passed on to the device management layer by this component. Rules
have to be considered, that define in which situations an action received
from another thing can be passed on for triggering an actuator.
Context Situation Creator Module: Collects data from the system and builds
the context situation(s). This can also be driven by rules.
Action Creator Module: Similar to the context situation creator module,
action objects have to be created once triggered during rule evaluation.
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Context Situation Publisher Module: Provide context situations to the thing
integration layer. According to the sophistication level of the implementation
the context situation publisher can provide a set of context situations for
different things that are subscribed or one context situation for everybody.
The context situation publisher module has to take care of data permission
levels towards other things. Only trusted other things should receive
selected context information. Furthermore this module has to take care of
defining the context situation schemas that are communicated to other
things that want to subscribe. The schema is used to evaluate whether a
thing is capable of communicating with another thing.
Action Publisher Module: Similar to the context situation publisher module
this module is responsible to communicate actions to the thing integration
layer to be communicated to other things. Additionally the action schema(s)
are managed by this component.
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Context Evaluation Module: Evaluates the rules using the (current) context
situation and triggers actions that are communicated down to the devices or
to the action creator module. The action creator module in turn passes the
created actions to the action publisher that communicates the actions to
other things. One way to simply evaluate rules is to build decision trees
from the rules defined by the rule engine.
The concrete architecture and complexity of offered functionality strongly
depends on the use case for the thing under development. Especially the
rule engine & artificial intelligence component might not have to be very
sophisticated for less intelligent things (e.g. a fridge). For things that collect
context information from other systems these components will, however, be
very sophisticated. Higher sophistication can be for example data science
and data mining techniques.
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The thing integration layer is responsible for finding other things and
communicating with them.
Once two things found each other they have to undergo a registration
mechanism. The thing integration layer has to evaluate if the
communication with the thing to be partnered with is possible. For this
purpose the context situation and/or action schemata have to be compared.
These are provided by the context management layer.
If the schema-match is evaluated positively, the thing can notify the other
thing upon new context situation or action creation. The context situations
and actions to be communicated to other things are provided by the context
management layer.
The thing registration can be done in a central component or by the thing
itself (e.g. auto-discovery network scan).
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The application integration layer connects the user to the thing.
Applications that are (directly) on top of the architecture are located here.
The application integration can be seen as a service layer, or even as a
simple UI on top of the stack. The concrete implementation of the layer
depends on the use case.