Presentation of the SemaGrow and agINFRA projects during the EDBT/ICDT 2014 Special Track on Big Data Management Challenges and Solutions in the Context of European Projects, 27th of March 2014
http://www.edbticdt2014.gr/index.php/eu-projects-track
Better ways of using Analytics in Agriculture in indiaYagnesh Shetty
Received the 1st Prize for this Research Paper presentation on Better Ways of using Analytics in Agriculture in India. Undertook Primary and Secondary Research to understand innovations in the agricultural sector that could transform the productivity levels and yeild/hectare for Indian farms. Did a comparative study of the Global scenario and made recommendations for Indian scope.
Presentation made on the new CGIAR Big Data in agriculture platform, and how big data approaches can contribute to improved productivity through data driven agronomy.
Today the use of data is having a very revolutionized effect with
cultivatable land in decline demand for food increasing from
developing countries farmers.
Farmers who use data are capable of turning ordinary harvests into
bumper crops and profits behind.This is the precision agriculture hub connecting the world’s biggest agricultural businesses farmers and suppliers using integrated software solutions.
Presentation in the CGIAR Science Week in Montpellier 2016 on how Big Data cna change agricultural research and development, and what the CGIAR needs to do.
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.
Better ways of using Analytics in Agriculture in indiaYagnesh Shetty
Received the 1st Prize for this Research Paper presentation on Better Ways of using Analytics in Agriculture in India. Undertook Primary and Secondary Research to understand innovations in the agricultural sector that could transform the productivity levels and yeild/hectare for Indian farms. Did a comparative study of the Global scenario and made recommendations for Indian scope.
Presentation made on the new CGIAR Big Data in agriculture platform, and how big data approaches can contribute to improved productivity through data driven agronomy.
Today the use of data is having a very revolutionized effect with
cultivatable land in decline demand for food increasing from
developing countries farmers.
Farmers who use data are capable of turning ordinary harvests into
bumper crops and profits behind.This is the precision agriculture hub connecting the world’s biggest agricultural businesses farmers and suppliers using integrated software solutions.
Presentation in the CGIAR Science Week in Montpellier 2016 on how Big Data cna change agricultural research and development, and what the CGIAR needs to do.
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.
Wesley Booth is an Acadia University business student who is working on a research project how predictive analytics can be used in the agriculture industry and how this relates to precision agriculture and farmers here in Kings County. The example outlined in the presentation is using predictive analytics to improve apple scab detection and management.
An overview of the CGIAR Platform for Big Data in Agriculture, officially launched in May 2017. The 15 CGIAR Research Centers and 12 Research Programs are partners in the Platform, alongside 70 external partners ranging international institutions, universities to private companies.
More info at: http://bigdata.cgiar.org
BigDataEurope - Big Data & Food and AgricultureBigData_Europe
Big Data and the Food & Agriculture domain (vis-a-vis the respective H2020 Societal Challenge) - Opportunities, Challenges and Requirements. As presented and discussed in the public launch of the BigDataEurope project.
Smart Farming is a development that emphasizes the use of information and communication technology in the
cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing
are expected to leverage this development and introduce more robots and artificial intelligence in farming.
This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be
captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of
Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following
a structured approach, a conceptual framework for analysiswas developed that can also be used for future
studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond
primary production; 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. Several authors therefore suggest that Big Data will cause major shifts in
roles and power relationsamong different players in current food supply chain networks. The landscape of stakeholders
exhibits an interesting gamebetween 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 in which the farmer is part of a highly integrated
food supply chain or 2) open, collaborative systems inwhich the farmer and every other stakeholder in the chain
network is flexible in choosing business partners as well for the technology as for the food production side. The
further development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective,
the authors propose to give research priority to organizational issues concerning governance issues and suitable
business models for data sharing in different supply chain scenarios.
Among the new and emerging technologies in agriculture, Big Data is the one that promises the best improvements. Producers and growers want superior yields, cost savings, and better real-time data; consumers want healthier agricultural products at better prices; agriculture scientists need improved seeds and plants to face climate changes and prevent famine.
Presentation for AERIAS, the global network of directors of Ag. Econ. Research Instiutes to discuss the FLINT project preliminary findings October 2016 Rome
APPLICATION OF BIG DATA IN ENHANCING EFFECTIVE DECISION MAKING IN AGRICULTURA...Sjaak Wolfert
The agriculture production system increasingly becomes data-driven and data-enabled based on the cyber-physical management cycle. This paper describes several IoT-applications of the EU-funded IoF2020 project in which data and data-sharing plays a crucial role. It provides an integrative framework aiming at cross-fertilisation, co-creation and co-ownership of results. Technical integration, business support and ecosystem development are key mechanisms to realize this.
Digital Agriculture – A key enabler for nutritional security and SDGs by Dr D...ICRISAT
Digital Agriculture - ICT and data ecosystems to support the development and delivery of timely, targeted information and services to make farming profitable and sustainable while delivering safe nutritious and affordable food for ALL.
A confluence of factors have converged to afford the opportunity to apply data science at large scale to agricultural production. The demand for agricultural outputs is growing and there is a need to meet this demand by utilizing increasingly mechanized precision agriculture and enormous data volumes collected to intelligently optimize agriculture outputs. We will consider the machine learning challenges related to optimizing global food production.
Report on the Outcomes of the 3rd Workshop 'Creating Impact with Open Data in...Marion Girard Cisneros
This document outlines some of the key action points discussed at the workshop held in February 2017. More information about the workshop: http://bit.ly/2lt7Vbf More information about the impact of open data for agriculture and nutrition: http://bit.ly/2lyjJqW
Entrepreneurs active in the agricultural sector spend more and more of their time registering and publishing all kinds of data, as the government, certification bodies, banks, clients, the retail sector and consumers all want to have more insight into how safe and sustainable their food is.
The majority of this agriculture-related data is still paper-based, spread over different systems and difficult to exchange between the people who want to access it. This is why digitising agricultural business data is an important item on the agenda. With FarmDigital, we can respond to these developments.
FarmDigital is an action research programme which is currently working towards a situation in which data only needs to be entered once and can be shared easily. It aims to achieve this goal by standardising data and developing and implementing an independent, digital platform for people to use.
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.
This is a keynote presentation presented at a conference on INNOVATIVE TECHNOLOGIES AND DATA APPLICATIONS IN THE AGRIFOOD SECTOR, 26 February 2019 at Boğaziçi Üniversitesi South Campus, Rectorate Conference Hall, Turkey. It describes multi-disciplinary, collaborative, agile approach for digital transformation of the agri-food sector based on the IoF2020 and SmartAgriHubs project. It describes several examples of IoT and Big Data applications from those projects,
The video and voice-over of this presentation can be found at https://youtu.be/wYJVqh6jvSE
AI for intelligent services in Food SystemsSjaak Wolfert
This presentation was presented at the IEEE 5G Worldforum in a session 'Dialogues between 5G/B5G and Vertical Domains: AI for Intelligent Services. Several use cases in Food Systems that use 5G are presented of which the 'weed detection robot' in more detail. Enabling factors and recommendations for the use of 5G to create intelligent services using AI are discussed.
Reflections on making EFSA an open science organisationNikos Manouselis
Slides of talk at the Workshop on e-Infrastructures supporting Food Safety Risk Assessment, hosted by the European Food Safety Authority (EFSA), Parma, May 13th, 2015.
Wesley Booth is an Acadia University business student who is working on a research project how predictive analytics can be used in the agriculture industry and how this relates to precision agriculture and farmers here in Kings County. The example outlined in the presentation is using predictive analytics to improve apple scab detection and management.
An overview of the CGIAR Platform for Big Data in Agriculture, officially launched in May 2017. The 15 CGIAR Research Centers and 12 Research Programs are partners in the Platform, alongside 70 external partners ranging international institutions, universities to private companies.
More info at: http://bigdata.cgiar.org
BigDataEurope - Big Data & Food and AgricultureBigData_Europe
Big Data and the Food & Agriculture domain (vis-a-vis the respective H2020 Societal Challenge) - Opportunities, Challenges and Requirements. As presented and discussed in the public launch of the BigDataEurope project.
Smart Farming is a development that emphasizes the use of information and communication technology in the
cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing
are expected to leverage this development and introduce more robots and artificial intelligence in farming.
This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be
captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of
Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following
a structured approach, a conceptual framework for analysiswas developed that can also be used for future
studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond
primary production; 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. Several authors therefore suggest that Big Data will cause major shifts in
roles and power relationsamong different players in current food supply chain networks. The landscape of stakeholders
exhibits an interesting gamebetween 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 in which the farmer is part of a highly integrated
food supply chain or 2) open, collaborative systems inwhich the farmer and every other stakeholder in the chain
network is flexible in choosing business partners as well for the technology as for the food production side. The
further development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective,
the authors propose to give research priority to organizational issues concerning governance issues and suitable
business models for data sharing in different supply chain scenarios.
Among the new and emerging technologies in agriculture, Big Data is the one that promises the best improvements. Producers and growers want superior yields, cost savings, and better real-time data; consumers want healthier agricultural products at better prices; agriculture scientists need improved seeds and plants to face climate changes and prevent famine.
Presentation for AERIAS, the global network of directors of Ag. Econ. Research Instiutes to discuss the FLINT project preliminary findings October 2016 Rome
APPLICATION OF BIG DATA IN ENHANCING EFFECTIVE DECISION MAKING IN AGRICULTURA...Sjaak Wolfert
The agriculture production system increasingly becomes data-driven and data-enabled based on the cyber-physical management cycle. This paper describes several IoT-applications of the EU-funded IoF2020 project in which data and data-sharing plays a crucial role. It provides an integrative framework aiming at cross-fertilisation, co-creation and co-ownership of results. Technical integration, business support and ecosystem development are key mechanisms to realize this.
Digital Agriculture – A key enabler for nutritional security and SDGs by Dr D...ICRISAT
Digital Agriculture - ICT and data ecosystems to support the development and delivery of timely, targeted information and services to make farming profitable and sustainable while delivering safe nutritious and affordable food for ALL.
A confluence of factors have converged to afford the opportunity to apply data science at large scale to agricultural production. The demand for agricultural outputs is growing and there is a need to meet this demand by utilizing increasingly mechanized precision agriculture and enormous data volumes collected to intelligently optimize agriculture outputs. We will consider the machine learning challenges related to optimizing global food production.
Report on the Outcomes of the 3rd Workshop 'Creating Impact with Open Data in...Marion Girard Cisneros
This document outlines some of the key action points discussed at the workshop held in February 2017. More information about the workshop: http://bit.ly/2lt7Vbf More information about the impact of open data for agriculture and nutrition: http://bit.ly/2lyjJqW
Entrepreneurs active in the agricultural sector spend more and more of their time registering and publishing all kinds of data, as the government, certification bodies, banks, clients, the retail sector and consumers all want to have more insight into how safe and sustainable their food is.
The majority of this agriculture-related data is still paper-based, spread over different systems and difficult to exchange between the people who want to access it. This is why digitising agricultural business data is an important item on the agenda. With FarmDigital, we can respond to these developments.
FarmDigital is an action research programme which is currently working towards a situation in which data only needs to be entered once and can be shared easily. It aims to achieve this goal by standardising data and developing and implementing an independent, digital platform for people to use.
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.
This is a keynote presentation presented at a conference on INNOVATIVE TECHNOLOGIES AND DATA APPLICATIONS IN THE AGRIFOOD SECTOR, 26 February 2019 at Boğaziçi Üniversitesi South Campus, Rectorate Conference Hall, Turkey. It describes multi-disciplinary, collaborative, agile approach for digital transformation of the agri-food sector based on the IoF2020 and SmartAgriHubs project. It describes several examples of IoT and Big Data applications from those projects,
The video and voice-over of this presentation can be found at https://youtu.be/wYJVqh6jvSE
AI for intelligent services in Food SystemsSjaak Wolfert
This presentation was presented at the IEEE 5G Worldforum in a session 'Dialogues between 5G/B5G and Vertical Domains: AI for Intelligent Services. Several use cases in Food Systems that use 5G are presented of which the 'weed detection robot' in more detail. Enabling factors and recommendations for the use of 5G to create intelligent services using AI are discussed.
Reflections on making EFSA an open science organisationNikos Manouselis
Slides of talk at the Workshop on e-Infrastructures supporting Food Safety Risk Assessment, hosted by the European Food Safety Authority (EFSA), Parma, May 13th, 2015.
The overall food system nearly reached the next strategic inflection point. IoT technology, data sharing and consumers’ demand for sustainable production methods are pushing limits. More than ever, agricultural production needs to deploy knowledge intensive farming practices and increase data sharing. Existing data exchange platforms need to become open data exchange ecosystems managing data owners’ consent and facilitate dynamic collaboration of stakeholders. This shall increase productivity and reduce food loss and waste of the circular food system from Farm2Fork.
FIWARE open-source software and agri-food data models are a cornerstone to facilitate this development. New sources of data can be made accessible, while decreasing effort for aggregating, processing, providing, and accessing data. This session is presenting different practical examples for data usage at the farm site and of partners collaborating along the food supply chain towards consumers helping them to learn about their choices.
The session will also summarise challenges and opportunities of the future of connected agriculture. This is specifically considering a technological perspective. At the same time, you will have the opportunity to meet colleagues from different sectors and business domains, aiming at building the foundation of the future data economy for food systems. This will offer the opportunity to learn about synergies considering the close integration of agriculture in smart villages as well as advanced food production and delivery systems that are at the heart of smart cities.
Extended version of slides used for talk on "Scaling up (and doing business with) food safety information transparency" at the Food@Cranfield network (http://www.som.cranfield.ac.uk/som/p19207/research/research-clubs/food-cranfield-research-network), on an event dedicated to Using Big Data. Presented the concept of using AGINFRA to facilitate and scale up food safety data. Part of the Big Data Europe (http://www.big-data-europe.eu) liaison & dissemination activities.
Agro-Know & the European agricultural research information ecosystemNikos Manouselis
Slides of my talk to members of the Agricultural Information Institute (AII) of the Chinese Academy of Agricultural Sciences (CAAS), on September 19th, 2014.
Big data analysis and Integration of Geophysical information from the Catalan...Andreas Kamilaris
The intensification of agriculture in Catalunya creates serious concerns over its impact on the physical environment, in terms of deteriorating the air, soils as well as rivers and lakes. It is particularly important to quantify and understand this impact, in order to perceive overall implications and to develop effective strategies to mitigate its effects. In this presentation, I describe our efforts in combining geospatial information and big data analysis in order to measure the environmental impact of agriculture, with a focus on animal manure. The problems and issues of discovering, locating and understanding relevant datasets are discussed, together with suggestions on how data could become more open and easier to reach and understand.
Agricultural Data Interest Group & Wheat Data Working Group of RDAVassilis Protonotarios
Presentation delivered during the "Engagement in RDA from Southern-Eastern Europe, Mediterranean and Caucasus region" Workshop. 25/6/2015, Athens, Greece
Why are e-Infrastructures useful from a small business perspective?Nikos Manouselis
Slides of talk at seminar for the EuroRIs network (http://www.euroris-net.eu) of National Contact Points (NCPs) for EU funding programmes on Research Infrastructures.
Data Warehousing and Business Intelligence Project on Smart Agriculture and M...Kaushik Rajan
Implemented a Data Warehouse on smart agriculture to solve various Business Intelligence queries. Integrated multiple datasets from 3 different data sources including both structured and unstructured data.
Tools used:
> SQL Server Integration Services for ETL
> SQL Server Management Services for Database
> SQL Server Analysis Services for building the Schema
> Tableau and PowerBI for Visualization
> R for data preprocessing
> LATEX for documentation
Video Presentation: https://www.youtube.com/watch?v=0oIlLQcyPdM
D4Science experience: VREs for increasing the sharing and collaboration in th...e-ROSA
Donatella Castelli's presentation at the eROSA Workshop “Towards Open Science in Agriculture & Food”, a side event to High Level conference on FOOD 2030, Plovdiv, Bulgaria (13/6/2018)
Presentation delivered during the Introductory Course: "Introduction to agricultural & food safety datasets and semantic technologies" (http://irss.iit.demokritos.gr/2014/hackathon/introductory_course) of the SemaGrow 2nd Hackathon (http://wiki.agroknow.gr/agroknow/index.php/SemaGrow_Hackathon)
4/7/2014, NCSR Demokritos, Athens, Greece
Similar to Big Data in Agriculture, the SemaGrow and agINFRA experience (20)
Presentation of the USEMP and Privacy Flag projects during INFO-COM 2015, Athens, Greece, discussing about privacy and risks in today's electronic world
agINFRA vision after the end of the projectAndreas Drakos
The agINFRA project (http://www.aginfra.eu) lasted from the October 2011 to February 2015. This presentation shows the vision for after the end of the project
agINFRA EGI-APARSEN workshop, Amsterdam, 4-6 March 2014Andreas Drakos
Presentation of agINFRA project (www.aginfra.eu) in the EGI-APARSEN workshop, Amsterdam, 4-6 March 2014
“Managing, computing and preserving big data for research”
https://indico.egi.eu/indico/conferenceDisplay.py?confId=2052
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
Big Data in Agriculture, the SemaGrow and agINFRA experience
1. Big data in agriculture
Andreas Drakos
Project Manager, Agro-Know
2. Presentation Outline
• The importance of Big Data in Agriculture
• Major challenges
• The agINFRA and SemaGrow solutions
• Supporting Global Initiatives
EDBT Special Track Big Data, Athens, March 2014 2
3. INTRO TO OPEN DATA IN
AGRICULTURE
EDBT Special Track Big Data, Athens, March 2014 3
Source:http://www.agricorner.com/shareholder-demands-to-shape-modern-agriculture/
4. Agriculture data to solve major
societal challenges
• All demographic and food demand projections
suggest that, by 2050, the planet will face severe food
crises due to our inability to meet agricultural
demand – by 2050:
– 9.3 billion global population, 34% higher than today
– 70% of the world’s population will be urban, compared to
49% today
– food production (net of food used for biofuels) must
increase by 70%
• According to these projections, and in order to achieve
the forecasted food levels by 2050, a total investment
of USD 83 billion per annum will be required
EDBT Special Track Big Data, Athens, March 2014 4
5. Open Data in Agriculture
• In an era of Big Data, one of the most promising routes to
bootstrap innovation in agriculture is by the use of Open
Data:
– e.g. provisioning, maintaining, enriching with relevant metadata,
making openly available a vast amount of information
• The use and wide dissemination of these data sets is
strongly advocated by a number of global and national
policy makers such as:
– The New Alliance for Food Security and Nutrition G-8 initiative
– Food & Agriculture Organization of the UN
– DEFRA & DFID in UK
– USDA & USAID in the US
EDBT Special Track Big Data, Athens, March 2014 5
6. Open Data in agriculture: a political
priority
“How Open Data can be
harnessed to help meet the
challenge of sustainably
feeding nine billion people
by 2050”
April, 2013, Washington, D.C. USA
EDBT Special Track Big Data, Athens, March 2014 6
7. A huge market, globally
Food & Agricultural commodities production, http://faostat.fao.org
EDBT Special Track Big Data, Athens, March 2014 7
8. Some figures
• Food - Gross Production Value globally in 2011:
$2,318,966,621
• Agriculture - Gross Production Value globally in
2011: $2,405,001,443
• Investment in agriculture - Gross Capital Stock
globally: $5,356,830,000
… they are big
EDBT Special Track Big Data, Athens, March 2014 8
9. Open data for businesses
EDBT Special Track Big Data, Athens, March 2014 9
10. Farmers starting to capitalize on
Big Data technology
• Freeing farmers from the constraints of uncertain
factors
– Dairy farm in UK with ‘connected’ herd
• anticipating the risks of epidemics and spotting random factors
in milk production
– Monsanto’s new acquisition protects farmers from
weather issues
• The spread of smart sensors
– Wine-growers in Spain reduced application of fertilizers
and fungicides by 20%, accompanied by a 15%
improvement in overall productivity using humidity
sensors
EDBT Special Track Big Data, Athens, March 2014 10
12. BIG DATA IN AGRICULTURE
EDBT Special Track Big Data, Athens, March 2014 12
13. Agricultural data types I
• Publications, theses, reports, other grey literature
• Educational material and content, courseware
• Research data,
– Primary data, such as measurements & observations
structured, e.g. datasets as tables
digitized, e.g. images, videos
– Secondary data, such as processed elaborations
e.g. dendrograms, pie charts, models
• Sensor data
EDBT Special Track Big Data, Athens, March 2014 13
14. Agricultural data types II
• Provenance information, incl. authors, their
organizations and projects
• Experimental protocols & methods
• Social data, tags, ratings, etc.
• Germplasm data
• Soil maps
• Statistical data
• Financial data
EDBT Special Track Big Data, Athens, March 2014 14
15. Big Data demand…
• Storage
– High volume storage
– Impractical or impossible to use centralized storage
• Distribution
• Federation
• Computational power
– For efficient discovering / querying
– For aggregating and processing
– For joining
EDBT Special Track Big Data, Athens, March 2014 15
16. Rationale: Problem statement
Enable the inclusion of:
• Large, live, constantly updated datasets and
streams
• Heterogeneous data
Involve publishers that
• cannot or will not directly and immediately make
the transition to standards and best practices
Open Agricultural Data Liaison Meeting 30-31/10/2013EDBT Special Track Big Data, Athens, March 2014 16
17. Use Cases (DLO)
Heterogeneous Data Collections &
Streams
Big data:
– Sensor data: soil data, weather
– GIS data: land usage, forest and natural resources management data
– Historical data: crop yield, economic data
– Forecasts: climate change models
Problem:
– Combine heterogeneous sources to analyze past food production and
forecast future trends
– Cannot clone and translate: large scale, live data streams
– Cannot immediately and directly affect radical re-design of all sensing
and processing currently in place
3rd Plenary & ESG Meeting 21/10/2013EDBT Special Track Big Data, Athens, March 2014 17
18. Use Cases (FAO)
Reactive Data Analysis
Big data:
– Document collections: past experiences, analysis and research results
– Databases: climate conditions and crop yield observations, economic
data (land and food prices)
Problem:
– Retrieving complete and accurate information to compile reports
• Raw data and reports, scientific publications, etc.
– Wastes human resources that could analyze data and synthesize useful
knowledge and advice for food production
• Too much time spent cross-relating responses from different sources
– Too many different organizations and processes rely on the different
schemas to make re-design viable
– Cloning is inefficient: large and constantly updated stores
3rd Plenary & ESG Meeting 21/10/2013EDBT Special Track Big Data, Athens, March 2014 18
19. Use Cases (AK)
Reactive Resource Discovery
Big data:
– Multimedia content about agriculture and biodiversity
Problem:
– Real-time retrieval of relevant content
– Used to compile educational activities
– Schema heterogeneity:
• Different providers (Oganic edunet, Europeana, VOA3R, etc.)
– Too many different organizations and processes rely on the different
schema to make re-design viable
– Cloning is inefficient: large and constantly updated stores
3rd Plenary & ESG Meeting 21/10/2013EDBT Special Track Big Data, Athens, March 2014 19
20. THE AGINFRA & SEMAGROW SOLUTIONS
EDBT Special Track Big Data, Athens, March 2014 20
21. The agINFRA project
• e-infrastructure for agricultural research
resources (content/data) and services
• Higher interoperability between agricultural
and other data resources (linked data)
• Improved research data services and tools
using Grid and Cloud resources
EDBT Special Track Big Data, Athens, March 2014 21
22. agINFRA Grid & Cloud resources
EDBT Special Track Big Data, Athens, March 2014 22
• PARADOX cluster
704 CPU; 50 TB
• Roma Tre cluster
350 CPUs; 100TB
• Catania cluster
800 CPUs; 700 TB
• SZTAKI cluster
8 CPUs
• PARADOX upgrade
1696 CPU;100 TB
• Total: 3.5 kCPU; 0.9 PT
23. The SemaGrow project
• Develop novel algorithms and methods for
querying distributed triple stores
• Overcome problems stemming from
heterogeneity and unbalanced distribution of
data
• Develop scalable and robust semantic indexing
algorithms that can serve detailed and accurate
data summaries and other data source
annotations about extremely large datasets
EDBT Special Track Big Data, Athens, March 2014 23
24. The SemaGrow Stack
• Integrates the components in order to offer a single
SPARQL endpoint that federates a number of
heterogeneous data sources
• Targets the federation of independently provided
data sources
• Use POWDER to mass-annotate large-
subspaces
– W3C recommendation, exploits natural groupings
of URIs to annotate all resources in a subset of the
URI space
EDBT Special Track Big Data, Athens, March 2014 24
25. Moving Forward
HARVESTER
OAI-PMH Service
Provider #1
Schema #1
OAI-PMH Service
Provider #n
Schema #n
INDEXER
Aggregated
XML Repository
Web Portals
Open AGRIS (FAO)
AgLR/GLN (ARIADNE)
Organic.Edunet (UAH)
VOA3R (UAH)
...
AGRIS AP Schema
IEEE LOM Schema
DC Schema
...
RDF Triple Store
Common Schema
SPARQL endpoint
(Data Source #1)
SPARQL endpoint
(Data Source #n)
INDEXER
Web Portals
SPARQL endpoint
NOW (2012) CASE OF AGRICULTURAL INFRASTRUCTURES 2015 (AgINFRA) CASE OF AGRICULTURAL INFRASTRUCTURES
EDBT Special Track Big Data, Athens, March 2014 25
26. Query
Federated endpoint Wrapper
SemaGrow
SPARQL endpoint
Resource Discovery
Query
results
query fragment,
Source
(#1)
Instance Statistics
Data Summaries
SPARQL endpoint
POWDER
Inference Layer
P-Store
Instance
Statistics
query fragment,
target Source
transformed query
Query Decomposition
query
patterns
Query Results Merger
query fragment,
Source
(#n)
query
results
Client
Reactivity
parameters
Query Decomposer
Data Source(s) Selector
Ctrl
Candidate Source(s) List
Instance Statistics
Load Info
Semantic Proximity
Query Transformation
Service
Schema
Mappings
SPARQL endpoint
(Data Source #n)
SPARQL
query
Ctrl
Ctrl
Load Info
Instance Statistics
Data Summaries
Set of
query
patterns
Query Pattern Discovery
Service
equivalent
patterns
query
pattern
Semantic
Proximity
Resource Selector
query results schema
transformed schema
query
request #1
query
request #n
query
results
SPARQL endpoint
(Data Source #1)
SPARQL
query
Query Manager
What Semantic Web can bring into
the picture
• One Data Access Point for the entire Data Cloud
– Enabling Service-Data level agreements with Data providers
• Application-level Vocabularies / Thesauri / Ontologies
– Enabling different application facets for different communities of users over the SAME data pool
• Going beyond existing Distributed
Triple Store Implementations
–Link Heterogeneous but Semantically Connected
Data
–Index Extremely Large Information Volumes (Peta
Sizes)
–Improve Information Retrieval response • Data (+Metadata)
physically stored in Data
Provider
– No need for harvesting
• Vocabularies / Thesauri /
Ontologies of Data Provider
choice
– No need for aligning
according to common
schemas
EDBT Special Track Big Data, Athens, March 2014 26
28. Global Open Data for Agriculture and
Nutrition (GODAN) godan.info
EDBT Special Track Big Data, Athens, March 2014 28
Research Data Alliance (RDA) rd-alliance.org
Agricultural Data Interoperability Interest Group
Wheat Data Interoperability Working Group
CIARD - global movement dedicated to open
agricultural knowledge www.ciard.net
e-Conference on Germplasm Data
Interoperability
Overcome problems stemming from heterogeneity and from the fact that the distribution of data over nodes is not determined by the needs of better load balancing and more efficient resource discovery, but by data providers