�ݺ�ߣshows by User: BorisAdryan

�ݺ�ߣshows by User: BorisAdryan / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: BorisAdryan / Thu, 05 Oct 2017 10:15:05 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: BorisAdryan Computational decision making /slideshow/computational-decision-making/80490330 091017m3machinelearning-pdf-171005101505
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.]]>
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.]]> Thu, 05 Oct 2017 10:15:05 GMT /slideshow/computational-decision-making/80490330 BorisAdryan@slideshare.net(BorisAdryan) Computational decision making BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/091017m3machinelearning-pdf-171005101505-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Computational decision making from Boris Adryan

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0 Development and Deployment: The Human Factor /slideshow/development-and-deployment-the-human-factor/79716277 thingmonk120917-170913082752
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.]]>
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.]]> Wed, 13 Sep 2017 08:27:51 GMT /slideshow/development-and-deployment-the-human-factor/79716277 BorisAdryan@slideshare.net(BorisAdryan) Development and Deployment: The Human Factor BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thingmonk120917-170913082752-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Development and Deployment: The Human Factor from Boris Adryan

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0 Zühlke Meetup - Mai 2017 /slideshow/zhlke-meetup-mai-2017/76284794 zuhlkemeetupmai2017-170524061024
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)]]>
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)]]> Wed, 24 May 2017 06:10:24 GMT /slideshow/zhlke-meetup-mai-2017/76284794 BorisAdryan@slideshare.net(BorisAdryan) Zühlke Meetup - Mai 2017 BorisAdryan 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) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/zuhlkemeetupmai2017-170524061024-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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)

Z端hlke Meetup - Mai 2017 from Boris Adryan

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0 Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16 /BorisAdryan/mehr-und-schneller-ist-nicht-automatisch-besser-data2day-061016 data2day061016-161007081457
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.]]>
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.]]> Fri, 07 Oct 2016 08:14:57 GMT /BorisAdryan/mehr-und-schneller-ist-nicht-automatisch-besser-data2day-061016 BorisAdryan@slideshare.net(BorisAdryan) Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16 BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/data2day061016-161007081457-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16 from Boris Adryan

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0 Industry of Things World - Berlin 19-09-16 /slideshow/industry-of-things-world-berlin-190916/66247162 industryofthings190916-160921080533
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.]]>
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.]]> Wed, 21 Sep 2016 08:05:33 GMT /slideshow/industry-of-things-world-berlin-190916/66247162 BorisAdryan@slideshare.net(BorisAdryan) Industry of Things World - Berlin 19-09-16 BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/industryofthings190916-160921080533-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Industry of Things World - Berlin 19-09-16 from Boris Adryan

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0 Just because you can doesn't mean that you should - thingmonk 2016 /BorisAdryan/just-because-you-can-doesnt-mean-that-you-should-thingmonk-2016 day2thingmonk20106140916-160915092819
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).]]>
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).]]> Thu, 15 Sep 2016 09:28:18 GMT /BorisAdryan/just-because-you-can-doesnt-mean-that-you-should-thingmonk-2016 BorisAdryan@slideshare.net(BorisAdryan) Just because you can doesn't mean that you should - thingmonk 2016 BorisAdryan 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). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/day2thingmonk20106140916-160915092819-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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).

Just because you can doesn't mean that you should - thingmonk 2016 from Boris Adryan

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0 Plattformen für das Internet der Dinge, solutions.hamburg, 05.09.16 https://de.slideshare.net/BorisAdryan/plattformen-fr-das-internet-der-dinge-solutionshamburg-050916 solutionshh050916-160915092102
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.]]>
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.]]> Thu, 15 Sep 2016 09:21:02 GMT https://de.slideshare.net/BorisAdryan/plattformen-fr-das-internet-der-dinge-solutionshamburg-050916 BorisAdryan@slideshare.net(BorisAdryan) Plattformen für das Internet der Dinge, solutions.hamburg, 05.09.16 BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/solutionshh050916-160915092102-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

from Boris Adryan

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0 Eclipse IoT - Day 0 of thingmonk 2016 /slideshow/eclipse-iot-day-0-of-thingmonk-2016/65935288 eclipseiotday0thingmonk120916-160912135036
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.]]>
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.]]> Mon, 12 Sep 2016 13:50:36 GMT /slideshow/eclipse-iot-day-0-of-thingmonk-2016/65935288 BorisAdryan@slideshare.net(BorisAdryan) Eclipse IoT - Day 0 of thingmonk 2016 BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eclipseiotday0thingmonk120916-160912135036-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Eclipse IoT - Day 0 of thingmonk 2016 from Boris Adryan

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0 Geo-IoT World, 25/05/16 /slideshow/geoiot-world-250516/62471180 geoiotworld250516-160527152205
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.]]>
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.]]> Fri, 27 May 2016 15:22:05 GMT /slideshow/geoiot-world-250516/62471180 BorisAdryan@slideshare.net(BorisAdryan) Geo-IoT World, 25/05/16 BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/geoiotworld250516-160527152205-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Geo-IoT World, 25/05/16 from Boris Adryan

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0 Smart IoT London, 13th April 2016 /BorisAdryan/smart-iot-london-13th-april-2016 smartiotlondon130416-160413104119
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.]]>
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.]]> Wed, 13 Apr 2016 10:41:19 GMT /BorisAdryan/smart-iot-london-13th-april-2016 BorisAdryan@slideshare.net(BorisAdryan) Smart IoT London, 13th April 2016 BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/smartiotlondon130416-160413104119-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Smart IoT London, 13th April 2016 from Boris Adryan

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0 Eclipse IoT - ecosystem /slideshow/eclipse-iot-ecosystem/59934118 eclipseiot-ecosystem-160323131511
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.]]>
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.]]> Wed, 23 Mar 2016 13:15:11 GMT /slideshow/eclipse-iot-ecosystem/59934118 BorisAdryan@slideshare.net(BorisAdryan) Eclipse IoT - ecosystem BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eclipseiot-ecosystem-160323131511-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Eclipse IoT - ecosystem from Boris Adryan

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0 TopConf Linz, 02/02/2016 /slideshow/topconf-linz-02022016/57921466 topconflinz020216-160205130731
The friction zone between probabilities, machine learning and user experience in the consumer Internet of Things -- talk at TopConfAT 2016]]>
The friction zone between probabilities, machine learning and user experience in the consumer Internet of Things -- talk at TopConfAT 2016]]> Fri, 05 Feb 2016 13:07:30 GMT /slideshow/topconf-linz-02022016/57921466 BorisAdryan@slideshare.net(BorisAdryan) TopConf Linz, 02/02/2016 BorisAdryan The friction zone between probabilities, machine learning and user experience in the consumer Internet of Things -- talk at TopConfAT 2016 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/topconflinz020216-160205130731-thumbnail.jpg?width=120&height=120&fit=bounds" /> The friction zone between probabilities, machine learning and user experience in the consumer Internet of Things -- talk at TopConfAT 2016

TopConf Linz, 02/02/2016 from Boris Adryan

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0 IoT - Be Open or Miss Out /slideshow/iot-be-open-or-miss-out/56953668 opendatasciencejanuary2016-160112125108
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.]]>
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.]]> Tue, 12 Jan 2016 12:51:08 GMT /slideshow/iot-be-open-or-miss-out/56953668 BorisAdryan@slideshare.net(BorisAdryan) IoT - Be Open or Miss Out BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/opendatasciencejanuary2016-160112125108-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

IoT - Be Open or Miss Out from Boris Adryan

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0 Thingmonk 2015 /slideshow/thingmonk-2015/55786794 thingmonk2015-151203155427-lva1-app6892
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?]]>
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?]]> Thu, 03 Dec 2015 15:54:27 GMT /slideshow/thingmonk-2015/55786794 BorisAdryan@slideshare.net(BorisAdryan) Thingmonk 2015 BorisAdryan 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? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thingmonk2015-151203155427-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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?

Thingmonk 2015 from Boris Adryan

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0 Node-RED and Minecraft - CamJam September 2015 /BorisAdryan/nodered-and-minecraft-camjam-september-2015 node-redminecraft-september2015-150903090552-lva1-app6892
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.]]>
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.]]> Thu, 03 Sep 2015 09:05:52 GMT /BorisAdryan/nodered-and-minecraft-camjam-september-2015 BorisAdryan@slideshare.net(BorisAdryan) Node-RED and Minecraft - CamJam September 2015 BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/node-redminecraft-september2015-150903090552-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Node-RED and Minecraft - CamJam September 2015 from Boris Adryan

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0 EclipseCon France 2015 - Science Track /BorisAdryan/eclipse-con-france2015 eclipseconfrance2015-150625083006-lva1-app6891
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?]]>
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?]]> Thu, 25 Jun 2015 08:30:06 GMT /BorisAdryan/eclipse-con-france2015 BorisAdryan@slideshare.net(BorisAdryan) EclipseCon France 2015 - Science Track BorisAdryan 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? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eclipseconfrance2015-150625083006-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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?

EclipseCon France 2015 - Science Track from Boris Adryan

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0 Node-RED workshop at IoT Toulouse /slideshow/node-red-workflowcoursetoulouse/49639734 node-redworkflowcourse-toulouse-150620195646-lva1-app6891
Handouts for IoT Toulouse Node-RED hands-on workshop, June 2015]]>
Handouts for IoT Toulouse Node-RED hands-on workshop, June 2015]]> Sat, 20 Jun 2015 19:56:46 GMT /slideshow/node-red-workflowcoursetoulouse/49639734 BorisAdryan@slideshare.net(BorisAdryan) Node-RED workshop at IoT Toulouse BorisAdryan Handouts for IoT Toulouse Node-RED hands-on workshop, June 2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/node-redworkflowcourse-toulouse-150620195646-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Handouts for IoT Toulouse Node-RED hands-on workshop, June 2015

Node-RED workshop at IoT Toulouse from Boris Adryan

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0 Data Science London - Meetup, 28/05/15 /slideshow/data-science-london-meetup-280515/48742893 datasciencelondon280515-150529082517-lva1-app6892
�ݺ�ߣs 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]]>
�ݺ�ߣs 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]]> Fri, 29 May 2015 08:25:17 GMT /slideshow/data-science-london-meetup-280515/48742893 BorisAdryan@slideshare.net(BorisAdryan) Data Science London - Meetup, 28/05/15 BorisAdryan �ݺ�ߣs 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 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datasciencelondon280515-150529082517-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs 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

Data Science London - Meetup, 28/05/15 from Boris Adryan

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0 O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Through Ontologies /BorisAdryan/oreilly-webcast-organizing-the-internet-of-things-actionable-insight-through-ontologies oreillywebcast8may2015-150504082254-conversion-gate01
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.]]>
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.]]> Mon, 04 May 2015 08:22:54 GMT /BorisAdryan/oreilly-webcast-organizing-the-internet-of-things-actionable-insight-through-ontologies BorisAdryan@slideshare.net(BorisAdryan) O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Through Ontologies BorisAdryan 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oreillywebcast8may2015-150504082254-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Through Ontologies from Boris Adryan

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0 An introduction to workflow-based programming with Node-RED /slideshow/node-redcamjampibirthdayfeb2015/44696140 node-red-camjam-pi-birthday-feb2015-150215080731-conversion-gate01
This is an introduction to Node-RED for beginners. The tutorial explains step-by-step how to implement a basic chat server.]]>
This is an introduction to Node-RED for beginners. The tutorial explains step-by-step how to implement a basic chat server.]]> Sun, 15 Feb 2015 08:07:31 GMT /slideshow/node-redcamjampibirthdayfeb2015/44696140 BorisAdryan@slideshare.net(BorisAdryan) An introduction to workflow-based programming with Node-RED BorisAdryan This is an introduction to Node-RED for beginners. The tutorial explains step-by-step how to implement a basic chat server. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/node-red-camjam-pi-birthday-feb2015-150215080731-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is an introduction to Node-RED for beginners. The tutorial explains step-by-step how to implement a basic chat server.

An introduction to workflow-based programming with Node-RED from Boris Adryan

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0 https://cdn.slidesharecdn.com/profile-photo-BorisAdryan-48x48.jpg?cb=1618500794 After a long time as academic group leader at the University of Cambridge, I've founded thingslearn Ltd., a data analytics company for the Internet of Things. From September 2016, I've joined a well-known engineering company in Germany, Zühlke Engineering GmbH. http://www.adryan.de https://cdn.slidesharecdn.com/ss_thumbnails/091017m3machinelearning-pdf-171005101505-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/computational-decision-making/80490330 Computational decision... https://cdn.slidesharecdn.com/ss_thumbnails/thingmonk120917-170913082752-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/development-and-deployment-the-human-factor/79716277 Development and Deploy... https://cdn.slidesharecdn.com/ss_thumbnails/zuhlkemeetupmai2017-170524061024-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/zhlke-meetup-mai-2017/76284794 Zühlke Meetup - Mai 2017