Slides from my @ds_ldn talk about Ontologies in the Internet of Things. Note that this is a short version of a talk that I presented earlier this month on O'Reilly Webcasts, still viewable for a while at: http://www.oreilly.com/pub/e/3365
Presented at the Open Data Science meetup London (January 2016). To fully leverage the potential of the Internet of Things requires the exchange of information between devices. Unfortunately, most data remains in vendor silos. This talk explains how the life sciences have tackled similar issues, and why closed, vendor-specific systems may miss out.
O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Thro...Boris Adryan
Traditional machine-to-machine (M2M) uses the internet to replace what was previously achieved through a wire. The challenges for IT are not much different to any other implementation of a prescribed business model.
But how are we going to leverage the connectedness of devices in the consumer Internet of Things (IoT) in a world in which every individual may show a different degree of technology adoption? Not everyone has the connected Crock Pot! The challenges are manifold, and while in 2015 we are still arguing about technical standards that hinder communication of things across platforms, the looming challenges of data integration are even more significant.
Even if all devices e.g. in the connected home of the future are going to speak one language, how are we generating actionable insight from the available information according to the users' need? How do we determine the appropriateness of action? An empty fridge might be alarming, but should we inform the user of an impending hunger crisis if the door hasn't been opened in a week, the heating system is set to low, the car is parked at the local airport? Draw your conclusions!
Ontologies organize things and establish their relationship to each other. They can be used for knowledge inference. For example, a car is a means of transport and ultimately an indicator of absence or presence. Some scientific domains are already making extensive use of ontologies to deal with vast amounts of information. The Gene Ontology (GO) has over 40k interlinked terms that describe cell and molecular biology. For every biological entity on that scale, we can ask: Where is it? What is its function? What process is it involved with? Benefitting from substantial government funding (in the range of > $40M from the NIH since 2001), knowledge inference through GO is widely applied in academic and industry research.
In this webcast I aim to introduce the three main branches localization, function and process that we use in GO and demonstrate how they're immediately applicable in the IoT — after all, a cell is just a large, interconnected system. I will further discuss relationship types that we use in the annotation of biological entities, and propose a few that are more appropriate for the IoT. I will contrast this relatively simple system with other ontologies suggested for the IoT. It is not my aim to sell GO as a one-size-fits-all, but talk about how building a large ontology has taught us pragmatism that is quite remote from many purely academic ontology proposals.
What the IoT should learn from the life sciencesBoris Adryan
What the Internet of Things should learn from the life sciences. About the utility of open data, ontologies and public repositories as routinely used in the academic life science, but rarely in the IoT.
My keynote from the Location Intelligence session at Geo-IoT World in Brussels in May 2016. How location is one of many important context variables in the interpretation of sensor data.
Industry of Things World - Berlin 19-09-16Boris Adryan
This talk makes the case for a measured use of big data pipelines and analytics methods based on the specific business case: one size doesn't fit all. Rather than buying the fastest stack and the most hyped methods, practitioners interested in analytics for Internet-of-Things deployments can save a lot of money by asking themselves a few questions that I lay out in the talk.
PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience.
My talk at Smart IoT London. About adding 'context' for data analytics in the consumer IoT, touching on machine learning, hidden variables, and UX/UI of communicating probabilities.
Presented at the Open Data Science meetup London (January 2016). To fully leverage the potential of the Internet of Things requires the exchange of information between devices. Unfortunately, most data remains in vendor silos. This talk explains how the life sciences have tackled similar issues, and why closed, vendor-specific systems may miss out.
O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Thro...Boris Adryan
Traditional machine-to-machine (M2M) uses the internet to replace what was previously achieved through a wire. The challenges for IT are not much different to any other implementation of a prescribed business model.
But how are we going to leverage the connectedness of devices in the consumer Internet of Things (IoT) in a world in which every individual may show a different degree of technology adoption? Not everyone has the connected Crock Pot! The challenges are manifold, and while in 2015 we are still arguing about technical standards that hinder communication of things across platforms, the looming challenges of data integration are even more significant.
Even if all devices e.g. in the connected home of the future are going to speak one language, how are we generating actionable insight from the available information according to the users' need? How do we determine the appropriateness of action? An empty fridge might be alarming, but should we inform the user of an impending hunger crisis if the door hasn't been opened in a week, the heating system is set to low, the car is parked at the local airport? Draw your conclusions!
Ontologies organize things and establish their relationship to each other. They can be used for knowledge inference. For example, a car is a means of transport and ultimately an indicator of absence or presence. Some scientific domains are already making extensive use of ontologies to deal with vast amounts of information. The Gene Ontology (GO) has over 40k interlinked terms that describe cell and molecular biology. For every biological entity on that scale, we can ask: Where is it? What is its function? What process is it involved with? Benefitting from substantial government funding (in the range of > $40M from the NIH since 2001), knowledge inference through GO is widely applied in academic and industry research.
In this webcast I aim to introduce the three main branches localization, function and process that we use in GO and demonstrate how they're immediately applicable in the IoT — after all, a cell is just a large, interconnected system. I will further discuss relationship types that we use in the annotation of biological entities, and propose a few that are more appropriate for the IoT. I will contrast this relatively simple system with other ontologies suggested for the IoT. It is not my aim to sell GO as a one-size-fits-all, but talk about how building a large ontology has taught us pragmatism that is quite remote from many purely academic ontology proposals.
What the IoT should learn from the life sciencesBoris Adryan
What the Internet of Things should learn from the life sciences. About the utility of open data, ontologies and public repositories as routinely used in the academic life science, but rarely in the IoT.
My keynote from the Location Intelligence session at Geo-IoT World in Brussels in May 2016. How location is one of many important context variables in the interpretation of sensor data.
Industry of Things World - Berlin 19-09-16Boris Adryan
This talk makes the case for a measured use of big data pipelines and analytics methods based on the specific business case: one size doesn't fit all. Rather than buying the fastest stack and the most hyped methods, practitioners interested in analytics for Internet-of-Things deployments can save a lot of money by asking themselves a few questions that I lay out in the talk.
PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience.
My talk at Smart IoT London. About adding 'context' for data analytics in the consumer IoT, touching on machine learning, hidden variables, and UX/UI of communicating probabilities.
Building a Real-Time Security Application Using Log Data and Machine Learning...Sri Ambati
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Inspirational talk on AI (artificial intelligence) and machine learning, i.e., how to give birth to an AI. Introductory and intentionally kept simple for non experts and non technical executives. Care should be taken not too over interpret some of the intentional simplified statements in the presentation.
W-JAX Keynote - Big Data and Corporate Evolutionjstogdill
A look at corporate evolution from the industrial revolution to the information age - with a focus on how Big Data will make an impact.
Presented at W-JAX Java Conference in Munich Germany, 11-8-11
Digital analytics & privacy: it's not the end of the worldOReillyStrata
This presentation starts by revisiting the common best practices related to digital analytics in order to measure digital asset’s effectiveness to increase conversion, common data feeds between tools and possibly data flows between continents for analysis.
These practices are then put in parallel with legal requirements, showing which steps need to be undertaken to assure legal compliance of said practices, how digital responsibles should be trained in data protection matters and what contracts are needed with both data providers & collectors so as to assure minimal liability for these routinely undertaken tasks.
This presentation is NOT about security and goes beyond the over-blown cookie debate in order to highlight how the upcoming EU Personal Data Protection Regulation will influence digital analytics to hopefully start embracing Privacy by Design ways of working.
The Internet of Things, Productivity, and Employment Alex Krause
Presentation by Bob Cohen of the Economic Strategy Institute. Cohen's presentation discusses how technology changes and the internet of things will impact productivity, jobs and employment.
Briefing room: An alternative for streaming data collectionmark madsen
Knowing what’s happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. He’ll be briefed by Erik Giesa of ExtraHop, who will showcase his company’s stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. He’ll share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis.
Machine learning and ai in a brave new cloud worldUlf Mattsson
Machine learning platforms are one of the fastest growing services of the public cloud. ML, an approach and set of technologies that use Artificial Intelligence (AI) concepts, is directly related to pattern recognition and computational learning. Early adopters of AI have now rolled out cloud-based services that are bringing AI to the masses.
How are AI, deep learning, machine learning, big data, and cloud related? Can machine learning algorithms enable the use of an individual’s comprehensive biological information to predict or diagnose diseases, and to find or develop the best therapy for that individual? How is Quantum Computing in the Cloud related to the use of AI and Cybersecurity?
Join this webinar to learn more about:
- Machine Learning, Data Discovery and Cloud
- Cloud-Based ML Applications and ML services from AWS and Google Cloud
- How to Automate Machine Learning
3-part approach to turning IoT data into business powerAbhishek Sood
There will be 44 zettabytes of data produced by IoT alone by 2020, according to IDC. That’s a little more than the cumulative size of 44 trillion feature films.
Data from IoT devices will soon be table stakes in your industry, if it isn’t already. Turning that data into quick and actionable insights is the race for all businesses who are investing in IoT devices.
Learn about a 3-pronged approach that can turn your IoT data into business actions:
Business-wide analytics revolution
Connected relationships with customers
Intelligent innovation based on data
IC-SDV 2019: The Economics of Artificial Intelligence and Machine Learning fo...Dr. Haxel Consult
Decision points for when to implement automatic indexing or more intensive subject analysis
Machine learning and artificial intelligence approaches to automatic indexing and other aspects of content enrichment have tremendous potential, but there are significant barriers to successful implementations. The economics of these systems are not now generally affordable, which will indefinitely delay widespread adoption. Significant costs are involved in just the training and maintaining systems that chronically under perform and are fail to scale. Cost and performance data will be characterized and presented. Machine learning and artificial intelligence projects are not for the faint of heart, nor for those with small budgets. Key cost elements are identified along with approaches to estimating costs based on actual and reported cases.
EclipseCon France 2015 - Science TrackBoris Adryan
Software is increasingly playing a big part in scientific research, but in most cases the growth is organic. The life time of research software is often as short as the duration of a postdoctoral contract: Once the researcher moves on, custom-written niche code is frequently not well documented, components are not reusable, and the overall development effort is likely lost.
This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow.
Also, in presenting the problems and discussing possible solution, this talk will look at the role institutions play in helping address these issues. In particular the Software Sustainability Institute (SSI, http://software.ac.uk/) works in the UK to promote the development, maintenance and (re)use of research software.
The Eclipse Foundation, with the Science Working Group, works to facilitate software sharing and reuse. How can organisations like the SSI and Eclipse align their strategies and activities for maximum effect?
Building a Real-Time Security Application Using Log Data and Machine Learning...Sri Ambati
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Inspirational talk on AI (artificial intelligence) and machine learning, i.e., how to give birth to an AI. Introductory and intentionally kept simple for non experts and non technical executives. Care should be taken not too over interpret some of the intentional simplified statements in the presentation.
W-JAX Keynote - Big Data and Corporate Evolutionjstogdill
A look at corporate evolution from the industrial revolution to the information age - with a focus on how Big Data will make an impact.
Presented at W-JAX Java Conference in Munich Germany, 11-8-11
Digital analytics & privacy: it's not the end of the worldOReillyStrata
This presentation starts by revisiting the common best practices related to digital analytics in order to measure digital asset’s effectiveness to increase conversion, common data feeds between tools and possibly data flows between continents for analysis.
These practices are then put in parallel with legal requirements, showing which steps need to be undertaken to assure legal compliance of said practices, how digital responsibles should be trained in data protection matters and what contracts are needed with both data providers & collectors so as to assure minimal liability for these routinely undertaken tasks.
This presentation is NOT about security and goes beyond the over-blown cookie debate in order to highlight how the upcoming EU Personal Data Protection Regulation will influence digital analytics to hopefully start embracing Privacy by Design ways of working.
The Internet of Things, Productivity, and Employment Alex Krause
Presentation by Bob Cohen of the Economic Strategy Institute. Cohen's presentation discusses how technology changes and the internet of things will impact productivity, jobs and employment.
Briefing room: An alternative for streaming data collectionmark madsen
Knowing what’s happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. He’ll be briefed by Erik Giesa of ExtraHop, who will showcase his company’s stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. He’ll share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis.
Machine learning and ai in a brave new cloud worldUlf Mattsson
Machine learning platforms are one of the fastest growing services of the public cloud. ML, an approach and set of technologies that use Artificial Intelligence (AI) concepts, is directly related to pattern recognition and computational learning. Early adopters of AI have now rolled out cloud-based services that are bringing AI to the masses.
How are AI, deep learning, machine learning, big data, and cloud related? Can machine learning algorithms enable the use of an individual’s comprehensive biological information to predict or diagnose diseases, and to find or develop the best therapy for that individual? How is Quantum Computing in the Cloud related to the use of AI and Cybersecurity?
Join this webinar to learn more about:
- Machine Learning, Data Discovery and Cloud
- Cloud-Based ML Applications and ML services from AWS and Google Cloud
- How to Automate Machine Learning
3-part approach to turning IoT data into business powerAbhishek Sood
There will be 44 zettabytes of data produced by IoT alone by 2020, according to IDC. That’s a little more than the cumulative size of 44 trillion feature films.
Data from IoT devices will soon be table stakes in your industry, if it isn’t already. Turning that data into quick and actionable insights is the race for all businesses who are investing in IoT devices.
Learn about a 3-pronged approach that can turn your IoT data into business actions:
Business-wide analytics revolution
Connected relationships with customers
Intelligent innovation based on data
IC-SDV 2019: The Economics of Artificial Intelligence and Machine Learning fo...Dr. Haxel Consult
Decision points for when to implement automatic indexing or more intensive subject analysis
Machine learning and artificial intelligence approaches to automatic indexing and other aspects of content enrichment have tremendous potential, but there are significant barriers to successful implementations. The economics of these systems are not now generally affordable, which will indefinitely delay widespread adoption. Significant costs are involved in just the training and maintaining systems that chronically under perform and are fail to scale. Cost and performance data will be characterized and presented. Machine learning and artificial intelligence projects are not for the faint of heart, nor for those with small budgets. Key cost elements are identified along with approaches to estimating costs based on actual and reported cases.
EclipseCon France 2015 - Science TrackBoris Adryan
Software is increasingly playing a big part in scientific research, but in most cases the growth is organic. The life time of research software is often as short as the duration of a postdoctoral contract: Once the researcher moves on, custom-written niche code is frequently not well documented, components are not reusable, and the overall development effort is likely lost.
This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow.
Also, in presenting the problems and discussing possible solution, this talk will look at the role institutions play in helping address these issues. In particular the Software Sustainability Institute (SSI, http://software.ac.uk/) works in the UK to promote the development, maintenance and (re)use of research software.
The Eclipse Foundation, with the Science Working Group, works to facilitate software sharing and reuse. How can organisations like the SSI and Eclipse align their strategies and activities for maximum effect?
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.
Just because you can doesn't mean that you should - thingmonk 2016Boris Adryan
Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT.
Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly.
A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).
Potentially creepy human-computer interactions in the future of the consumer IoT. Lots of raw data need to be analysed and are represented as result of machine learning exercises. However, consumers are likely scared of probabilities. How can UX address these issues?
Plattformen für das Internet der Dinge, solutions.hamburg, 05.09.16Boris Adryan
Talk in German. Abstract: Prospective end users new to IoT are overwhelmed with the vast number of offerings around IoT data brokerage, storage and analysis. This talk exemplifies some of the challenges that have to be met in real-world deployments, and why there is no one-size-fits-all IoT solution. We conclude that IoT solution providers in many cases need to consider PaaS solutions with customer-specific modifications.
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
Eclipse IoT is the M2M/IoT ecosystem provided by the Eclipse Foundation. It offers open source software solutions for end devices, gateway systems and backends. Notable Eclipse IoT projects are Kura (a turn-key ready gateway e.g. for the Raspberry Pi), Eclipse SmartHome (integral part e.g. of openHAB) or the MQTT/CoAP suits Mosquitto, Paho, Californium, Wakama and Leshan. There are also solutions for process plants and manufacturing, as well as tools for large-scale device management.
Big Data is a term to describe technologies which help to collect and analyze big data amounts that cannot be easily processed. Focus of Big Data is on real-time analyses and the recognition of unknown correlations within acquired information.
By using Big Data tools, you will gather competitive advantages through, for instance, customer analyses and simulations of business scenarios, encourage innovations and grow added values of your company. With the help of Big Data, loose data collections will be restructured and supportive tools for decision-making management processes are provided.
Exponentials and Networks - The Existential Challenge Of Radical Innovation For The Enterprise
Exponential technologies tend to take even the experts by surprise. The centralized and hierarchical organizations are under threat by nimbler and more resilient decentralized networks.
How can modern enterprises survive the combined challenges of technological and organizational innovation, internalizing the processes that make companies great and thrive?
Computer science is faced with many challenges as the digital universe expands. From mobile and cloud computing to data security, addressing these issues can require large, structural changes, but an examination of these problems can lead to organizational solutions and improvements in the world.
IoT growth forecasts currently tend to span 30 – 60 Bn ‘Things’ by 2030. However, this ignores the central IoT role in realising sustainable societies where raw materials and component use have to see very high levels of reuse, repurposing, and recycling. In such a world almost everything we possess and use will have to be tagged and be electronically addressable as a part of the IoT. Such a need immediately sees growth estimates of 2Tn or more over the span of Industry 4 and 5. On the basis of energy demands alone, it is inconceivable that the technologies of BlueTooth, WiFi, 4, 5, and 6G could support such demand, and nor are the signaling and security protocols viable on such a scale.
The evolution of the IoT will therefore most likely see a new form of dynamic network requiring new lightweight protocols employing very little signal processing, together with very low energy wireless technologies (in the micro-Watt range) operating over extremely short distances (~10m). This need might be best satisfied by a new form of ‘Zero Infrastructure Mesh Networks’ that engage in active resource sharing, lossy probabilistic routing, and cyber security realised through an integrated ‘auto-immunity’ system. Ultimately, we might also envisage data amalgamation at key nodes that have a direct connection into the internet along with an additional layer of cyber checks and protection.
We justify the above assertions by illustrating the energy and network limitations of today’s 5G networks and those already obvious in current 6G proposals. We then go on to detail how a suitable IoT MeshNet might be configured and realised, along with a few solutions and emergent outcomes on the way.
We are just at the start exploring the opportunities of the Internet, with 90% or more of the possibilities still unexplored! Over three billion new minds are soon going to connect to the global network. The challenges of decentralization and distribution of previously hierarchical and centralized functions are revolutionizing the design of services. The exponential technologies, with their characteristic unpredictability, are disrupting industries that previously thought themselves immune to the digital revolution. What are the strategies to be able to leverage the new waves of technology? How can we quickly revise experiments creating virtuous circles of evolution? In today's hyper-connected world there are no barriers to entry and the distance between idea and action is reduced to zero!
BIG DATA | How to explain it & how to use it for your career?Tuan Yang
If you ask people what BIG DATA is they often say it is about a lot of data. But the world has ALWAYS had a lot of data. It is about datafication – a word so new even spellcheck functions don’t know it is a real word!
Learn more about:
» How BIG DATA changes career paths of even the most unsuspecting?
» How BIG DATA changes the way business decision are made?
» How BIG DATA changes who makes those decisions & the reshuffle of the balance of power it causes?
» What BIG DATA skills can you bring to the office tomorrow to increase your value to the firm
An introduction to the Internet of Things (IoT)7thingsmedia
As presented at the IAB UK London's offices on 23rd April 2015. 7thingsmedia Founder & CEO, Chris Bishop, gave a detailed introduction to the ‘Internet of Things’ and then looked forward to examine what is around the corner.
The presentation looks at who the early leaders are, what it is now and where it will go. It examines when it will truly enter the mainstream market and what effect it will have on home life, the city, our industries and the environment.
A brief lesson on what constitutes computational decision making, from simple regression via various classification methods to deep learning. No maths, only basic concepts to teach the lingo of machine learning to a lay audience.
Development and Deployment: The Human FactorBoris Adryan
Thingmonk 2017: End-to-end IoT solutions are often highly integrated. Even small changes to the UX of a product can have profound impact on hardware requirements, while physical constraints such as battery capacity can dictate software architecture. A holistic understanding of IoT is key to efficient implementation, the “T-shaped engineer” the star in every development team. Contrast this to intellectual silos and matrix organisation, and you may see why especially large companies fail to move quickly into IoT. Similar issues strike the application of IoT. Deploying a solution in the enterprise is just a cost factor if processes are not adjusted to leverage the connected device and its data. However, changes in process often affect companies across their entire organisational structure. This can require a change of mindsets, making the success of an IoT solution depending on the human factor.
IoT-Daten: Mehr und schneller ist nicht automatisch besser.
Über optimale Sampling-Strategien, wie man rechnen kann, ob IoT sich rechnet, und warum es nicht immer Deep Learning und Real-Time-Analytics sein muss. (Folien Deutsch/Englisch)
Node-RED and Minecraft - CamJam September 2015Boris Adryan
This workshop uses the Node-RED framework as development tool for JavaScript. Building on functionality available for generic programming challenges, we’re going to use the communication standard TCP (Transmission Control Protocol) to interact with the Minecraft API (Application Programming Interface). The material is aimed at people who have had first experience with the Minecraft API on a Raspberry Pi (say, using Python), who now want to understand what's going on behind the scenes and what TCP, API and all those other acronyms mean. It also introduces flow-based programming concepts.
A significant proportion of developments in the Internet of Things (IoT) is driven by non-technical innovators and ambitious hobbyists. Node-RED targets this audience and offers a widely used rapid prototyping platform for IoT data plumbing on the basis of JavaScript. Data platforms for the IoT provide storage facilities and value in the form of visualisation & analytics to business and end users alike. This report details how Node-RED connects to 11 different platforms and what additional services these provide.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Data Science London - Meetup, 28/05/15
1. Semantic web warmed up:
Ontologies for
the IoT
Dr. Boris Adryan
@BorisAdryan
@thingslearn
Currently getting divorced from
logic.sysbiol.cam.ac.uk
2. ‣Everything is connected
‣ Big, noisy, often
unstructured data
‣ We are learning how
biological entities
depend on each other
DNA > RNA > proteins
have been
3. ‣ Everything is
connected
‣ Big, noisy, often
unstructured data
www.thingslearn.com
Analytics, context integration, machine
learning and predictive modelling for
the IoT.
4. 0 clean shirt left
+
washing machine estimates
97% of your last pack of
powder used
+
it’s Wednesday, 23:55
+
the last four Thursdays
had a morning business
meeting
+
the car is parked 20 m from
a shop
+
last retail activity: 8 sec ago
Send immediate text
reminder to pick up
washing powder + send
tweet from @BorisHouse
“need identified” AND
“notification appropriate”Actionable insight.
From everything.
5. NO ANALYTICAL FLEXIBILITY IN M2M/IOT
Matt Hatton, Machina Research
The BLN IoT ‘14
Internet replaces wire
It’s all about the
context
M2M
consumer
IoT
defined I-P-O
like it’s 1975
context
context
context
context
context
context
context
Is it hot?
6. LIFE SCIENCE STRATEGIES
DON’T WORK IN THE IOT
- There are no commonly accepted
- ‘catalogue’ of things,
- ‘ontology’ of things,
- ‘data format’ of things,
- ‘meta data’ for things.
- Most businesses are driven by
revenue, not long-term strategic
vision
- Service providers have no need to
publish
- Data can be highly personal
(cheap excuse)
unless
they’re
7.
8. META DATA, SHARING AND DATA REPOS
founded in Nov. 1999
But this is a complex and ambitious project, and is one of the biggest challenges that bioinformatics
has yet faced. Major difficulties stem from the detail required to describe the conditions of an
experiment, and the relative and imprecise nature of measurements of expression levels. The
potentially huge volume of data only adds to these difficulties.
Nature
Feb. 2000
“
“
Nov. 2000 Oct. 2002
Wide adoption:
as requirement
for publication
in scientific
journals
12. CURRENT GOVERNMENT
INVESTMENTS INTO GENE
ONTOLOGY
NIH alone spent $44,616,906 on
the ontology structure since 2001
(I don’t have data for UK/EU
spendings)
~100 full-time salaries for experts
with domain-specific knowledge
~40,000 terms
13. story
measurements
+ meta data
open, public repositories
human
curators
ontology
terms
community
PUBLISH OR PERISH
ok?
journal
informal exchange - no credit!
funders
assessment
The majority of this
infrastructure is paid for by
governments and charities
industry!
14.
15. measurements
+ meta data
storage &
provenance
human
curators
ontology
terms
user
PUBLISH OR YOU’RE NOT DOING IOT
ok?
Maybe the majority of this
infrastructure should be
paid for by governments?
company
cloud
device
registration
“ “
privileges
dataadded
value
18. ONTOLOGIES HAVE TO BE
PRAGMATIC COMPROMISES
Gene Ontology annotation
15 years of research
47 publications
100+ authors
50+ PhDs
15 direct annotations
~150 inferred annotations
19. THE THREE BRANCHES OF
Adapted from Anurag et al., Mol. BioSyst., 2012,8, 346-352
Localization:Where is an entity acting?
Function:What does the entity do?
Process:When is the entity needed?
20. inferences on “is a”
“part of”
“regulates”
“has part”
from geneontology.org
from Ashburner et al., Nat Genet. 2000, 25(1):25-9.
GO AND CONTEXT
21. THE BRANCHES OF GO AND THE IOT
Localization: inside, (my?) home, living room
Function:
measures temperature
regulates temperature
interacts with user directly
interacts with user via app
Process:
regulation of temperature
measurement of ambient temperature
‘is proxy / is avatar’ for
presence?
fire?
ice age?
winter?
22. A LAST WORD ON PRAGMATISM
“perfect” ontology
The SSN Ontology allows for
inference entirely on the basis
of its structure and annotation.
In reality, many parameters are
difficult to establish and the
effort to annotate things
outweighs the utility.
“crude” ontology
A simplified structure allows for
quick annotation even by non-
specialists.
The lack of details can lead to
clashes in the ontology =>
more smartness has to go into
software; more coding effort.
1 billlion
different things
1 milllion
use cases
23. 0 clean shirt left
+
washing machine estimates
97% of your last pack of
powder used
+
it’s Wednesday, 23:55
+
the last four Thursdays
had a morning business
meeting
+
the car is parked 20 m from
a shop
+
last retail activity: 8 sec ago
Send immediate text
reminder to pick up
washing powder + send
tweet from @BorisHouse
“need identified” AND
“notification appropriate”Actionable insight.
From everything.
“indicator of esteem”
3% left and
not pressed
“not home”
“buying”
credit card:
“highly personal device”
~ alive and awake