This document discusses smart farming and precision dairy farming using sensor data. It describes a project called Smart Dairy Farming 1 that aims to support dairy farmers in caring for individual cows using sensor data to improve health and productivity. An "InfoBroker" concept is introduced as an open platform where sensor and other data from different sources can be shared anonymously between data producers and consumers like farmers, advisors and the government. Examples of sensor data collected from Dutch dairy farms are provided. The document emphasizes starting small with pilots and experiments while thinking big about how sensor data could improve agriculture statistics and analytics over time through multi-stakeholder collaboration.
In his keynote presentation titled Advanced Analytics for Any Data at Real-Time Speed, Dan Potter will discuss how a new-found ability to prepare, incorporate, enrich and visualize streaming data for advanced visual analysis is essential for making timelier, high-impact business decisions in tough competitive markets.
Advanced Analytics for Any Data at Real-Time Speeddanpotterdwch
The kenyote presentation from Predictive Analytics World entitled "Advanced Analytics for Any Data at Real-Time Speed" Dan Potter, CMO from Datawatch, presents a new approach to prepare, incorporate, enrich and visualize streaming data for advanced visual analysis is essential for making timelier, high-impact business decisions in tough competitive markets.
Managing your Assets with Big Data ToolsMachinePulse
This presentation was given by Karthigai Muthu, Lead Big Data Analyst, at a meetup organized by the group Internet of Everything in March 2015.
Through his presentation, Karthik provided a comprehensive understanding of available ecosystem tools and how they can be used to perform data engineering and data analytics. Karthik covers the following topics in his presentation:
• Establishment of complete data pipeline using big data ecosystem tools.
• Tackling of high velocity streams using various stream processing engines on cloud and performing Real Time analytics.
• Tackling of historical data using big data ecosystem tools and migration of traditional infrastructure to big data environments.
• Integration of big data ecosystem for data analysis using SAMOA , R and Mahout.
• Deployments of big data environments on the cloud.
In IoT systems, the Security System Levels are determined by Data Classificat...RekaNext Capital
In IoT, the sensor data need to consider Sensor Value, Veracity, Volume, Velocity & Variety within the data classification in its context and cannot be treated equal to be cost efficient for security consideration.
In his keynote presentation titled Advanced Analytics for Any Data at Real-Time Speed, Dan Potter will discuss how a new-found ability to prepare, incorporate, enrich and visualize streaming data for advanced visual analysis is essential for making timelier, high-impact business decisions in tough competitive markets.
Advanced Analytics for Any Data at Real-Time Speeddanpotterdwch
The kenyote presentation from Predictive Analytics World entitled "Advanced Analytics for Any Data at Real-Time Speed" Dan Potter, CMO from Datawatch, presents a new approach to prepare, incorporate, enrich and visualize streaming data for advanced visual analysis is essential for making timelier, high-impact business decisions in tough competitive markets.
Managing your Assets with Big Data ToolsMachinePulse
This presentation was given by Karthigai Muthu, Lead Big Data Analyst, at a meetup organized by the group Internet of Everything in March 2015.
Through his presentation, Karthik provided a comprehensive understanding of available ecosystem tools and how they can be used to perform data engineering and data analytics. Karthik covers the following topics in his presentation:
• Establishment of complete data pipeline using big data ecosystem tools.
• Tackling of high velocity streams using various stream processing engines on cloud and performing Real Time analytics.
• Tackling of historical data using big data ecosystem tools and migration of traditional infrastructure to big data environments.
• Integration of big data ecosystem for data analysis using SAMOA , R and Mahout.
• Deployments of big data environments on the cloud.
In IoT systems, the Security System Levels are determined by Data Classificat...RekaNext Capital
In IoT, the sensor data need to consider Sensor Value, Veracity, Volume, Velocity & Variety within the data classification in its context and cannot be treated equal to be cost efficient for security consideration.
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
Presented FIspace at a matchmaking event in The Netherlands for the FIWARE Accelerator FInish. Also the other accelerators SmarAgriFood, Fractals and SpeedUP!Europe were mentioned.
Presentation for a group of employees of Centric, a large software consultancy company. It provides an illustration of how IoT is currently being developed in farming, agri-logistics and food consumption. It also addresses the technical and organizational challenges that have to be overcome to make IoT application in agri-food a success. Open platforms and software development and above all appropriate business models are key issues that have to be addressed. The new EU-project "Internet of Food and Farm 2020" will address these issues by fostering a collaborative IoT ecosystem to upscale the use of IoT in agri-food.
How IoT is changing the agribusiness landscapeSjaak Wolfert
Smart Farming involves many sensing and monitoring devices, intelligent software for analysis & planning and mechatronics/robots closing the cyber-physical farm management cycle. Big Data on prices, markets, consumer behavior, etc. increasingly affect the whole agribusiness providing predictive insights in farming operations, drive real-time operational decisions and redesign business processes for game-changing business models. Major shifts in roles and power relations among different players in food supply chain networks can be expected. This presentation will briefly describe the IoT developments in agri-food business and present the changing business landscape with special attention to the role of software ecosystems in this development.
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAIBig Data Week
Charles Cai has more than two decades of experience and track records of global transformational programme deliveries – from vision, evangelism to end-to-end execution in global investment banks, and energy trading companies, where he excels at designing and building innovative, large scale, Big Data systems in high volume low latency trading, global Energy Trading & Risk Management, and advanced temporal and geospatial predictive analytics, as Chief Front Office Technical Architect and Head of Data Science. He’s also a frequent speaker at Google Campus, Big Data Innovation Summit, Cloud World Forum, Data Science London, QCon London and MoD CIO Symposium etc, to promote knowledge and best practice sharing, with audience ranging from developers, data scientists, to CXO level senior executives from both IT and business background. He has in-depth knowledge and experience Scala, Python, C# / F#, C++, Node.js, Java, R, Haskell programming languages in Mobile, Desktop, Hadoop/Spark, Cloud IoT/MCU and BlockChain etc, and TOGAF9, EMC-DS, AWS CNE4 etc. certifications.
Big&open data challenges for smartcity-PIC2014 ShanghaiVictoria López
This talk is about how both private enterprise and government wish to improve the value of their data and how they deal with this issue. The talk summarizes the ways we think about Big Data, Open Data and their use by organizations or individuals. Big Data is explained in terms of collection, storage, analysis and valuation. This data is collected from numerous sources including networks of sensors, government data holdings, company market databases, and public profiles on social networking sites. Organizations use many data analysis techniques to study both structured and unstructured data. Due to volume, velocity and variety of data, some specific techniques have been developed. MapReduce, Hadoop and other related as RHadoop are trendy topics nowadays.
In this talk several applications and case studies are presented as examples. Data which come from government sources must be open. Every day more and more cities and countries are opening their data. Open Data is then presented as a specific case of public data with a special role in Smartcity. The main goal of Big and Open Data in Smartcity is to develop systems which can be useful for citizens. In this sense RMap (Mapa de Recursos) is shown as an Open Data application, an open system for Madrid City Council, available for smartphones and totally developed by the researching group G-TeC (www.tecnologiaUCM.es).
From Danish Food Cluster Mega Trends conference, may 2017. How big data and technology influences the food value chain and which overall tecnnology trends are changing the way we work.
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...BigData_Europe
“Lightning talk” in the Big Data Europe (BDE) workshop on “Big data for food, agriculture and forestry: opportunities and challenges” taking place on 22.9.2015 in Paris by Rob Lokers and Sander Janssen from Alterra, Wageningen UR
The Netherlands.
Please cite as: Kamel Boulos MN. Creating self-aware and smart healthy cities. Invited plenary keynote address followed by sub-plenary round table at WHO 2014 International Healthy Cities Conference, Athens, Greece, 25 October 2014. http://www.healthycities2014.org/ehome/89657/192014/?&
PPT updated in May 2015.
Oct 2017: See also https://www.slideshare.net/sl.medic/how-the-internet-of-things-and-people-can-help-improve-our-health-wellbeing-and-quality-of-life
New technologies such as the Internet of Things and Cloud Computing are expected to leverage the current
trend of Smart Farming, introducing more sensors, robots and artificial intelligence, encompassed by the
phenomenon of Big Data.
This presentation will give a quick insight into the state-of-the-art of Big Data applications in Smart Farming
and identify the related challenges that have to be addressed. It shows that the scope of Big Data
applications in Smart Farming goes beyond the farm; it is influencing the entire food supply chain. Big data
are being used to provide predictive insights in farming operations, drive real-time operational decisions, and
redesign business processes for game-changing business models.
It is expected that Big Data will cause major shifts in roles and power relations among different players in
current food supply chain networks. The landscape of stakeholders exhibits an interesting game between
powerful tech companies, venture capitalists and often small startups and new entrants. At the same time
there are several public institutions that publish open data, under the condition that the privacy of persons
must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1)
closed, proprietary systems or 2) open, collaborative systems.
The development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. A major challenge is therefore to
cope with governance issues and define suitable business models for data sharing in different supply chain
scenarios.
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
Presented FIspace at a matchmaking event in The Netherlands for the FIWARE Accelerator FInish. Also the other accelerators SmarAgriFood, Fractals and SpeedUP!Europe were mentioned.
Presentation for a group of employees of Centric, a large software consultancy company. It provides an illustration of how IoT is currently being developed in farming, agri-logistics and food consumption. It also addresses the technical and organizational challenges that have to be overcome to make IoT application in agri-food a success. Open platforms and software development and above all appropriate business models are key issues that have to be addressed. The new EU-project "Internet of Food and Farm 2020" will address these issues by fostering a collaborative IoT ecosystem to upscale the use of IoT in agri-food.
How IoT is changing the agribusiness landscapeSjaak Wolfert
Smart Farming involves many sensing and monitoring devices, intelligent software for analysis & planning and mechatronics/robots closing the cyber-physical farm management cycle. Big Data on prices, markets, consumer behavior, etc. increasingly affect the whole agribusiness providing predictive insights in farming operations, drive real-time operational decisions and redesign business processes for game-changing business models. Major shifts in roles and power relations among different players in food supply chain networks can be expected. This presentation will briefly describe the IoT developments in agri-food business and present the changing business landscape with special attention to the role of software ecosystems in this development.
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAIBig Data Week
Charles Cai has more than two decades of experience and track records of global transformational programme deliveries – from vision, evangelism to end-to-end execution in global investment banks, and energy trading companies, where he excels at designing and building innovative, large scale, Big Data systems in high volume low latency trading, global Energy Trading & Risk Management, and advanced temporal and geospatial predictive analytics, as Chief Front Office Technical Architect and Head of Data Science. He’s also a frequent speaker at Google Campus, Big Data Innovation Summit, Cloud World Forum, Data Science London, QCon London and MoD CIO Symposium etc, to promote knowledge and best practice sharing, with audience ranging from developers, data scientists, to CXO level senior executives from both IT and business background. He has in-depth knowledge and experience Scala, Python, C# / F#, C++, Node.js, Java, R, Haskell programming languages in Mobile, Desktop, Hadoop/Spark, Cloud IoT/MCU and BlockChain etc, and TOGAF9, EMC-DS, AWS CNE4 etc. certifications.
Big&open data challenges for smartcity-PIC2014 ShanghaiVictoria López
This talk is about how both private enterprise and government wish to improve the value of their data and how they deal with this issue. The talk summarizes the ways we think about Big Data, Open Data and their use by organizations or individuals. Big Data is explained in terms of collection, storage, analysis and valuation. This data is collected from numerous sources including networks of sensors, government data holdings, company market databases, and public profiles on social networking sites. Organizations use many data analysis techniques to study both structured and unstructured data. Due to volume, velocity and variety of data, some specific techniques have been developed. MapReduce, Hadoop and other related as RHadoop are trendy topics nowadays.
In this talk several applications and case studies are presented as examples. Data which come from government sources must be open. Every day more and more cities and countries are opening their data. Open Data is then presented as a specific case of public data with a special role in Smartcity. The main goal of Big and Open Data in Smartcity is to develop systems which can be useful for citizens. In this sense RMap (Mapa de Recursos) is shown as an Open Data application, an open system for Madrid City Council, available for smartphones and totally developed by the researching group G-TeC (www.tecnologiaUCM.es).
From Danish Food Cluster Mega Trends conference, may 2017. How big data and technology influences the food value chain and which overall tecnnology trends are changing the way we work.
SC2 Workshop 1: Big Data challenges and solutions in agricultural and environ...BigData_Europe
“Lightning talk” in the Big Data Europe (BDE) workshop on “Big data for food, agriculture and forestry: opportunities and challenges” taking place on 22.9.2015 in Paris by Rob Lokers and Sander Janssen from Alterra, Wageningen UR
The Netherlands.
Please cite as: Kamel Boulos MN. Creating self-aware and smart healthy cities. Invited plenary keynote address followed by sub-plenary round table at WHO 2014 International Healthy Cities Conference, Athens, Greece, 25 October 2014. http://www.healthycities2014.org/ehome/89657/192014/?&
PPT updated in May 2015.
Oct 2017: See also https://www.slideshare.net/sl.medic/how-the-internet-of-things-and-people-can-help-improve-our-health-wellbeing-and-quality-of-life
New technologies such as the Internet of Things and Cloud Computing are expected to leverage the current
trend of Smart Farming, introducing more sensors, robots and artificial intelligence, encompassed by the
phenomenon of Big Data.
This presentation will give a quick insight into the state-of-the-art of Big Data applications in Smart Farming
and identify the related challenges that have to be addressed. It shows that the scope of Big Data
applications in Smart Farming goes beyond the farm; it is influencing the entire food supply chain. Big data
are being used to provide predictive insights in farming operations, drive real-time operational decisions, and
redesign business processes for game-changing business models.
It is expected that Big Data will cause major shifts in roles and power relations among different players in
current food supply chain networks. The landscape of stakeholders exhibits an interesting game between
powerful tech companies, venture capitalists and often small startups and new entrants. At the same time
there are several public institutions that publish open data, under the condition that the privacy of persons
must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1)
closed, proprietary systems or 2) open, collaborative systems.
The development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. A major challenge is therefore to
cope with governance issues and define suitable business models for data sharing in different supply chain
scenarios.
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).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. Sensors Going Smart
Smart Farming
1
Matthijs Vonder
• 1986 - 1992 UT-Electrotechnics
• 1992 - 1994 UT-Mechatronics (TWAIO)
• 1995 - 1998 Buhrs Zaandam
• 1998 - 2002 KPN Research
• 2003 - now TNO
Sr. Technical Scientist
Group: Monitoring & Control Services
Role: IT-Architect, Project leader
Large Scale (Infrastructure) Monitoring
• IJkdijk / Floodcontrol / Earthquakes
• SmartDairyFarming 1, 2, 3, (a.o. Young
Stock Rearing)
• Data Intensive Smart Agriculture Chains
(DISAC) – E-pieper
• IoT / Big Data (architectures/DDA)
matthijs.vonder@tno.nl
Contents
Introduction and positioning
Precision Dairy Farming
Data Sharing (via InfoBroker concept)
Some developments
Messages
2. Introduction and positioning
PF = Precision Farming (animals + agriculture )
PLF = Precision Lifestock Farming (animals, e.g pigs, poultry, cows)
PDF = Precision Dairy Farming (milk cows)
Project Smart Dairy Farming (SDF)
that is about PDF
Goal of project SDF1:
to support dairy farmers in the care of
individual animals.
with the specific goal of a longer
productive stay at the farm due to
improvement of individual health.
2
aka Smart
Farming
3. Starting point:
Cow centric thinking
Starting point:
Farmer in control
“De boer aan het roer”
Real time models / services
(at different providers)
Sensors from
different suppliers
Other data
sources
InfoBroker: Open platform
for (sensor) data producers
and consumers
Cow specific
workinstruction
(SOP)
Precision Dairy Farming
3
4. Some SDF1 – Facts & Figures
4
Farm 1 Farm 2 Farm 3 Farm 4 Farm 5 Farm 6 Farm 7
# cows/calves 459 186 315 239 706 202 351
Behaviour
Temperature
Activity
Milk production
Feed intake
Weight
Water intake
Milk intake
NB1: blue numbers are animals; not all animals are monitored for SDF (e.g. 3 and 4 only calves)
NB2: the left column gives a list of “sensor data categories” at a farm
NB3: numbers in black are the sensor fields within a category (e.g. 3 fields related to waterintake)
Farm 1 Farm 2 Farm 3 Farm 4 Farm 5 Farm 6 Farm 7
# cows/calves 459 186 315 239 706 202 351
Behaviour 5x 5x
Temperature 1x 1x
Activity 9x 9x 3x 6x 5x 13x 9x
Milk production 16x 20x 1x 2x 19x
Feed intake 24x 24x 10x 24x
Weight 10x 6x 6x 6x 7x 6x 10x
Water intake 3x 3x
Milk intake 7x 11x
See next
slide
6. Starting point:
Cow centric thinking
Starting point:
Farmer in control
“De boer aan het roer”
Real time models / services
(at different providers)
Sensors from
different suppliers
Other data
sources
InfoBroker: Open platform
for (sensor) data producers
and consumers
Cow specific
workinstruction
(SOP)
Precision Dairy Farming
Think big,
start small
6
Numbers for the Dutch situation:
• 15000+ farmers
• in total more then 1.5 million milk cows
• 20 to 200+ datafields per cow
• many different stakeholders in the chain
7. 7
Data Sharing: InfoBroker concept
InfoBroker* functionalities:
Open interfaces for data exchange (API)
Authentication
who are you (are you allowed to login)
Permissions
which data may be used by whom
to be set by the farmers
Namingservice
location where the data can be found
Integration
combining info from different sources
Pay-per-use
fixed costs (connections)
variable costs (used data)
So:
no central datastore for (sensor)data!
but indeed a broker
and reduces/prevents duplication
cow specific work
instructions (SOPs)
InfoBroker
cow centric data
cow centric
data
Cow centric
Sensor data
Static data
(e.g. feed)
Cow centric
Sensor data
Static data
(e.g. date of birth)
Dashboard
Model
Model
Model
x 15.000+
* For InfoBroker see eg. EC-PLF paper (Vonder, van der Waaij, Harmsma, & Donker, 2015)
publications.tno.nl/publication/34623333/DBpkjn/vonder-2015-near.pdf
8. Some scenario’s for using the InfoBroker:
InfoBroker
Realtime-Model
(expert)
Farmer
What else?BenchmarkingCoöperation
Advisor
Static
data
Real time
cow-centric
data
8
GovernmentOther farmers
9. Some Smart Farming / Big (sensor) Data
developments (at TNO)
Big Data IT-architectures
For Multi Stakeholder Collaborations
from Research to Operations: “Think Big, Start small”
Semantic interoperability of (sensor) data
Linked Data, Ontology mapping, etc
Eg. Common Dairy Ontology (CDO)
Data Driven Analyses (DDA)
E.g. “roughage intake prediction”, “look-alike-cow”,
“pregnacy insemination failure prediction”
Interactive Visualisation of BD
Dashboards, pixel plots, etc
Virtual/Augmented reality (Hololens)9
Cooperatie Datahub
Operational implementation of InfoBroker,
permissions and more (live Q4 2017)
10. Messages
Use of sensor (and other) data (via an InfoBroker/Datahub) could be the
right direction for statistical products
Improve current statistics
Develop new statistics
Lower the response burden
A lot of data is already/soon available, for Dairy e.g.
Static and sensor data of dairy cows
Pasture behaviour-, financial-, environmental data
And more agrifood sub domains to come soon
Think about: What-is-in-it for the data-owner (e.g. farmer)?
Money (for the data)?
Lower response burden?
Benchmark-report (relative position compared to peers)?
O
One liner: Think Big, Start small !
Start experimenting now (and give feedback for additions/improvement)!
Work together with others (Multi Stakeholder Collaboration)
10
11. Thank you for your attention
info: matthijs.vonder@tno.nl
For more inspiration:
TIME.TNO.NL