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Jack Verhoosel | Semantics in Dairy Farming: towards a Common Dairy Ontology

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Jack Verhoosel | Semantics in Dairy Farming: towards a Common Dairy Ontology

  1. 1. SEMANTICS FOR BIG DATA APPLICATIONS IN SMART DAIRY FARMING Jack Verhoosel, Jacco Spek Presentation at the Semantics 2016 conference 14 September 2016
  2. 2. aantalle n NL noemen Collaboration project 3 Cooperations 7 SME’s 5 Research institutes 7 Real farmers Timeline: SDF1: 2011 – 2014 Nothern part of the Netherlands Website (in Dutch): http://www.smartdairyfarming.nl/nl/ Goal of SDF: 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. Challenge SDF2: more farmers: from 7 to 60 (and prepare for 2500) more sensor suppliers and more data consumers incorporate semantics and big data analysis 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 2 SDF 1.0 (2011 – 2014) SDF 2.0 (2015 – 2017)
  3. 3. 3 Starting point: Cow centric thinking Starting point: Farmer in control “De boer aan het roer” Real time analysis models (at different organisations) Sensors from different suppliers: Lely, Delaval, Agis, Gallagher,… Other data sources, CRV, FC, AgriFirm, Weather, Satellite InfoBroker: Open platform for sharing (sensor) data producers and consumers Cow specifics Workinstructions (SOP) This project is made possible by: Data sharing in the dairy chain Think big, start small 12GB sensordata per year for 7 farms => 310 GB triples From 7 to 50 farms of 15.000 in NL
  4. 4. 4 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 – static data – cow-centric sensor data  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+
  5. 5. InfoBroker – Facts & Figures 5 Farm 1 Farm 2 Farm 3 Farm 4 Farm 5 Farm 6 Farm 7 # cows/calves 459 186 315 239 706 202 351 Behaviour x x Temperature x x Activity x x x x x x Milk production x x x x x Food intake x x x Weight x x x x x x x Water intake x x Milk intake x x Date: february 2015 NB1: this are “sensor data categories” at a farm NB2: not all animals are monitored for SDF (e.g. 3 and 4 only calves)
  6. 6. InfoBroker – Facts & Figures 6 Number of cows vs time Number of sensorfields vs time
  7. 7. WHY LINKED DATA AND SEMANTICS? 1. To make the various data sets accessible in an automatically linkable manner for easier integration 2. To align the semantics of the datasets in isolation as well as in combination using ontologies 3. To enable a rich set of questions to be queried on the datasets for better analysis 7
  8. 8. AgriFirm: “How much feed did a group of cows at a dairy farm take in a certain period per type of feed and how strong is the correlation with milk yield?” CRV: “What was the average weight per day over the last lactation period of a cow and what was the weight in/decrease over that period? BIG DATA ANALYSIS QUESTIONS
  9. 9. SIMPLE EXAMPLE OF LINKED DATA Subject Object predicate Cow Animal is a (type) Parcel grazes on Parcel Grasslandis a (type) 40 ha has surface 11
  10. 10. LINKED DATA ROADMAP* The four design principles of Linked Data (by Tim Berners Lee): 1. Use Uniform Resource Identifiers (URIs) as names for things. 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL). 4. Include links to other URIs so that they can discover more things. 12 LODRefine *Based on PLDN LD roadmap
  11. 11. ….. sensor data….. sensor data ONTOLOGY-BASED SDF 13 Cow specific data per farm per sensor equipment Delaval sensor data Lely sensor data Visualization and analysis apps Common Dairy Ontology MS-ontology Measurements Triples Static Triples ST-ontology Static data (e.g. date of birth) Cow specific data per farm mapping mapping Nedap sensor data Agis sensor data
  12. 12. COMMON DAIRY ONTOLOGY 14 “What was the average weight per day and weight in/decrease over the last lactation period of a cow in a group ?”
  13. 13. ONTOLOGY MAPPING 15 Common Dairy Ontology rdfs:label = “Activity2hours” rdfs:label = “BodyWeight” Measurement ontology
  14. 14. PLASIDO: OUR BIG, LINKED DATA PLATFORM Powerful server: 128GB memory, 5TB storage Triplestores: Marmotta Triplestore with Relational TripleDB Jena Fuseki Triplestore with Native GraphDB Virtuoso Relational ClassicalDB with SPARQL-2-SQL interface All SDF data of 2014 and 2015 retrieved from InfoBroker Converted into triples using LODRefine and RDF generator From 12GB to 310GB, increase of factor 25 Stored in Marmotta and Fuseki for comparison Application development: Angular-Javascript JSON converter Google Visualization 16 LODRefine
  15. 15. POC DEMO: AGRIFIRM AND CRV 17
  16. 16. POC DEMO: STATIC COW INFO 18
  17. 17. POC DEMO: MILKYIELD / FEEDINTAKE 19 Correlation? How strong? AI data analytics!
  18. 18. POC DEMO: FEEDINTAKE PER FEED 20
  19. 19. PLASIDO PERFORMANCE TESTS 1. All 2014 SDF triple data from Infobroker into Apache Marmotta triplestore 12GB of CSV data turned into +/- 310 GB of RDF triples Marmotta makes use of classical relational database to store triples Simple queries with only one parameter can be easily answered More complex queries let to unacceptable response time Main reason is inefficient access to underlying RDB 2. Next step: switch all data to Apache Jena Fuseki triplestore Still +/- 310 GB of RDF triples Fuseki makes use of modern graph database to store triples Simple queries with only one parameter can be easily answered More complex analysis queries lead to long, but still acceptable response time, upto 15 minutes So, an acceptable performance. See next slide for some numbers. 21
  20. 20. PLASIDO PERFORMANCE Query Input Graph size Search par Response Select an overview with the number of cows of a farmer Stokman 111,604,625 1 0.04s Bakker 167,894,559 1 0.03s Antonides 79,739,365 1 0.37s Select the list of cows with number and parity Stokman 28,704 3 0.934s Bakker 9,400 3 15.110s Antonides 45,816 3 27.006s Select feed per type per day over all cows of a farmer Stokman 66,551,765 3 913.003s Bakker 38,034,692 3 350.917s Antonides 45,637,592 3 380.470s Select average weight over all cows per day per parity Antonides 45,637,592 3 348.704s Select static info for a cow NL 715820911 45,816 2 0.094s Select weight per day in lactation period NL 715820911 45,683,408 5 5.129s Select weight and milkyield per day in lactation period NL 715820911 45,683,408 7 13.714s Select milkyield per day in lactation period NL 715820911 45,683,408 3 4.142s With set-up 2 using Apache Jena Fuseki triplestore with graphDB
  21. 21. PLASIDO PERFORMANCE TESTS Conclusion of previous 2 tests: working with large triplesets is acceptable However, is it really necessary to put all data into triples? Why not use only the CDO as semantic interface and leave all data in classical table database format? 3. Next step: put all data into tables and use RDB of Virtuoso Only +/- 10 GB of raw data Makes use of classical relational database to store raw data in table form SPARQL interface based on CDO Mapping between SPARQL and SQL to translate queries towards RDB Simple queries with only one parameter can be easily answered More complex analysis queries lead to errors, because the SPARQL-2- SQL mapping generates too large SQL queries and results that cannot be handled by Virtuoso So, an unacceptable performance for this Virtuoso-based solution. 23
  22. 22. CONCLUSION AND NEXT STEPS CDO ontology application in SDF architecture Further enhancement of CDO to cover the dairy domain extensively Architectural study to enable the use of CDO with InfoBroker CDO as semantic interface for InfoBroker Apply AI deep learning algorithms to perform data analytics More extensive performance studies Other linked data platforms to deal with big data: D2RQ Other possibilities for improvements: Linked Data Fragments Dealing with heterogeneous sensordata: differently measured Enabling analysis based on incomplete sensordata RDF stream processing for dealing with streaming sensor data 24
  23. 23. THANK YOU FOR YOUR ATTENTION! 25

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