Cloud and Big Data in the agriculture sector
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Cursos de Verano Univ. Almería 2019. Digitalización en Agricultura: Tecnologías, Aplicaciones y Cadena de Valor. Introducción al Cloud y Big Data.
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Cloud and Big Data in the agriculture sector
- 1. Cloud y Big Data A brief voyage to Cloud and Big Data worlds Fernando López – FIWARE Foundation www.fiware.org, @flopezaguilar 10.07.2019
- 2. 1
- 3. Cloud & Big Data terms that you should know 2 I think that all our doubts will be resolved Brilliant, awesome … …what the idiots face are putting these two…
- 4. Cloud Computing 3Source: https://www.kdnuggets.com/2016/06/cloud-computing-key-terms-explained.html
- 7. 6
- 8. 7
- 10. 9 Data as a Service
- 11. 10
- 13. 12 Volume Velocity Variety Variability Veracity Visualization Value The 7 V‘s of Big Data
- 19. 18 29
- 23. EDP + CB + CEF 22
- 24. 23
- 26. 25
- 28. 27 Source: https://www.iowafarmbureau.com/f/4b37b785-c3c3-4773-9727-327e14e56d6b/segment1 ‘Big Data has been providing a useful tool ‘to ensure that each year we are improving our production plan. Small increases in adopting change on the farm can lead to significant long term success […] Every producer enters spring with the best plan for their farm based on the information they have available […] Increase value derived from traditional on-farm data sources: leverage knowledge from planting, fertility, and yield maps to make better input decisions’
- 29. 28 Did you understand anything? I haven't understand anything yet. And You? Me neither… Ok, lets follow with our poker faces…
- 30. 29 Not even double facepalm can explain how do you feel after knowing that it was just the first part of the presentation
- 32. Big Data Analytics 31 Indexed Storage (RDBMS, Apache Solr) Interactive Processing (e.g. Drill, BigQuery, OLAP) MapReduce (e,g, Spark, Hadoop) Realtime Analytics (CEP, Stream Processing) In-Memory Computing (e.g. Spark, SAP Hana, VoltDB) SizeodtheDataHandled (persecond) millis seconds minutes hours days Time to Act 100k events (100MBs) 1k events (1MBs) 100 events (10KBs)
- 33. Big Data Analytics 32https://www.dga.or.th/upload/download/file_fbcd2e42d3fa2274baa5ebbcc612ee2a.pdf
- 34. 33
- 35. 34 Data Lake
- 42. Border of Big Data (image) 41 Beyond Big Data…
- 43. 42
- 44. 43
- 46. Finally, this guy finished being us a bore first
- 47. 46 Captain, may I ask now some questions? Sure… I mean, a question after this one… Spock!! !
- 48. Thank you! http://fiware.org Follow @FIWARE on Twitter
Hinweis der Redaktion
- Cloud computing is an Internet-based computing that largely offers on-demand access to computing resources.
- Cloud computing is an Internet-based computing that largely offers on-demand access to computing resources. A cluster is a collection of commodity computers connected together with a system of high speed network. Grid computing combines computers from multiple administrative domains to reach a common goal, to solve a single task, and may then disappear just as quickly.
- DFS is any file system that allows accessing to file from multiple hosts and may include replication and fault tolerance.
- In sum, cloud computing can help with real-time computation, data access, and storage to users without having to know or worry about the physical location and configuration of the system that delivers the services.
- https://impact.com/marketing-intelligence/7-vs-big-data/ https://dataconomy.com/2014/05/seven-vs-big-data/
- Bit: binary digit (0 or 1) Byte: one character (e.g. „Hello World“ -> 11 bytes) Kilobyte: A paragraph (e.g. low resolution photo -> 100KB ) Megabyte: A short novel (e.g. A high-resolution photograph -> 2MB) Gigabyte: 7 minutes of HD-TV video (e.g. A library floor of academic journals -> 100 GB) Terabyte: 300 hours of good quality video (e.g. The printed collection of the entire Library of Congress -> 10TB) Petabyte: 500 billion pages of standard printed text (e.g. The amount of data processed by Google daily -> 20 PB) Exabyte: 2 million personal computers (e.g. Total data held by Google -> 15 EB) Zettabyte: 250 billion DVDs Yottabyte: Size of the entire World Wide Web (It would take approximately 11 trillion years to download a Yottabyte file from the internet using high-powered broadband.) Brontobyte: 1 followed by 27 zeros! Geopbyte: No one knows why this term was created. It is highly doubtful that anyone alive today will EVER see a Geopbyte hard drive.
- Volume: how much data we have Data production will be x44 times bigger in 2020 than in 2009
- Volume
- Velocity: speed in which data is accessible
- Variety: Data in many forms
- Variety
- Metadata is data that describes other data
- A data model refers to the logical inter-relationships and data flow between different data elements involved in the information world.
- To make it easier for END-USERS and CITY DATABASES and IoT internet-of-things and 3rd-party APPS to exchange INFO. Exchanging data and ontology allows Users and A.I. to see meaning.
- https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL
- Variability refers to data whose meaning is constantly changing.
- Veracity: Big Data is the messy, noisy nature of it, and the amount of work that goes in to producing an accurate dataset before analysis can even begin.
- Visualization: how to show the data
- Value of Data: the last step, after all of them you want to get value of your data
- Agricultural companies, governments, organizations, researchers (from academia and industry) generate, maintain and use huge amount of data related to agricultural production, weather and climate, insurance, marketing, supply chain, packaging, distribution, etc.
- Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. (bounded?) Apache Spark is a unified analytics engine for large-scale data processing. Apache Zeppelin Web-based notebook that enables data-driven, interactive data analytics and collaborative documents Apache Kafka is used for building real-time data pipelines and streaming apps. Apache Storm is a distributed stream processing computation framework Apache Nifi is a framework to automate the flow of data between software systems. Apache Cassandra is a free and open-source distributed wide column store NoSQL database management system. Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation in batch processing.
- OLAP: Online Analytical Processing RDBMS: Relational DB Management System
- Analytics, making inference over the large set of data. Descriptive Analytics: Insight into the past Diagnostic Analytics: Explain why did it happen Predictive Analytics: Understanding the future Prescriptive Analytics: Advise on possible outcomes (outcomes?)
- Dark Data: All the data collected by the companies but not processed
- Data Lake: Large repository of data in raw format (raw?)
- Data Mining: activity to find patterns and deriving insight in large set of data
- Data Scientist: person who can make sense of huge dada
- https://planningtank.com/computer-applications/types-of-data-processing
- ETL: extract, transform, load OLTP: OnLine Transaction Processing ERP: Enterprise Resource Planning CRM: Customer Relationship Management OLAP: Online Analytical Processing
- Machine learning is a method of data analysis that automates analytical model building. Machine learning is a type of artificial intelligence (AI) that enables software applications to become more accurate in forecasting outcomes without being specially programmed.
- Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
- Philosopher Stone of Machine learning