Graph Databases vs TypeDB | What you can't do with graphs
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Developing with graph databases has a number of challenges, such as the modelling of complex schemas, and maintaining data consistency in your database. In this talk, we discuss how TypeDB addresses these challenges, as well as how it compares to property graph databases. We’ll look at how to read and write data, how to model complex domains, and TypeDB’s ability to infer new data. The main differences between TypeDB and graph databases can be summarised as: 1. TypeDB provides a concept-level schema with a type system that fully implements the Entity-Relationship (ER) model. Graph databases, on the other hand, use vertices and edges without integrity constraints imposed in the form of a schema 2. TypeDB contains a built-in inference engine - graph databases don’t provide native inferencing capabilities 3. TypeDB is an abstraction over a graph, and leverages a graph database under the hood to create a higher-level model, while graph databases work at different levels of abstraction Tomás Sabat Tomás is the Chief Operating Officer at Vaticle. He works closely with TypeDB's open source and enterprise users who use TypeDB to build applications in a wide number of industries including financial services, life sciences, cyber security and supply chain management. A graduate of the University of Cambridge, Tomás has spent the last seven years founding and building businesses in the technology industry.
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Graph Databases vs TypeDB | What you can't do with graphs
- 1. Tomás Sabat Graph Databases vs TypeDB What you can’t do with graphs
- 2. Introduction The Challenges of Working with a Graph Database Modelling and Defining Schema Reading Data Inference
- 3. Structure (connectedness) Meaning (naming and other definitions) 7 8 5 3 11 2 9 10
- 5. How is TypeDB different to a graph database?
- 6. Introduction The Challenges of Working with a Graph Database Modelling and Defining Schema Reading Data Inference
- 7. What’s wrong with graph databases?
- 9. Maintaining Consistency of Data
- 11. Graph databases are too low level
- 12. Introduction The Challenges of Working with a Graph Database Modelling and Defining Schema Reading Data Inference
- 14. The Graph Model contains Property Graph relationships Nodes Properties may have organise may have contains
- 15. Thing Entity Attribute Relation Role sub sub sub owns plays relates sub sub sub sub Rule
- 16. TypeDB Model Graph Model TypeDB abstract away the graph model
- 17. Mary married Peter on 2/2/2020
- 18. Mary married Peter on 2/2/2020 name: “Mary” name: “Peter” Person Person
- 19. MARRIED_TO name: “Mary” name: “Peter” Person Person Mary married Peter on 2/2/2020
- 20. MARRIED_TO name: “Mary” name: “Peter” Person Person date: 2/2/2020 Mary married Peter on 2/2/2020
- 21. MARRIED_TO name: “Mary” name: “Peter” date: 2/2/2020 Person Person person person marriage spouse spouse date: 2/2/2020 name: Mary name: Peter Mary married Peter on 2/2/2020
- 22. MARRIED_TO name: “Mary” name: “Peter” date: 2/2/2020 Person Person person person marriage spouse spouse date: 2/2/2020 name: Mary name: Peter Instead of nodes and edges, TypeDB implements a concept level entity- relationship model Mary married Peter on 2/2/2020
- 23. In a property graph, an edge is just a pair of vertices, a hyperedge is a set of vertices.
- 27. company company supplying part Company Supplying Part SUPPLIER BUYER Company SUPPLIED supplier supplied buyer Relationship Types We end up reifying the graph to represent the relation as a node Ternary Relations
- 28. company company supplying part supplier supplied buyer Relationship Types Company Supplying Part SUPPLIER BUYER Company SUPPLIED define company sub entity, plays supplying:supplier, plays supplying:buyer; part sub entity, plays supplying:supplied; supplying sub relation, relates supplier, relates buyer, relates supplied; Ternary Relations
- 31. (:Company)-[:SUPPLIER]->(:Supplying) (:Company)-[:BUYER]->(:Supplying) (:Part)<-[:SUPPLIED]-(:Supplying) $supplier isa company; $part isa part; $buyer isa company; (supplier: $supplier, supplied: $part, buyer: $buyer) isa supplying; Relationship Types Ternary Relations
- 33. Relationship Types located marriage person person city spouse spouse locating location Nested Relations
- 34. Relationship Types located marriage person person city spouse spouse Person Marriage City: London MARRIED MARRIED Person LOCATED_IN locating location Nested Relations
- 35. located marriage person person city spouse spouse Relationship Types We end up reifying the graph to represent the relation as a node locating location Person Marriage City: London MARRIED MARRIED Person LOCATED_IN Nested Relations
- 36. MARRIED Relationship Types located marriage person person city spouse spouse MARRIED locating location Person Marriage City: London MARRIED MARRIED Person LOCATED_IN define city sub entity, plays located:location; person sub entity, plays marriage:spouse; marriage sub relation, relates spouse, plays located:locating; located sub relation, relates locating, relates location; Nested Relations
- 39. (:Person)-[:MARRIED]->(marriage:Marriage)<- [:MARRIED]-(:PERSON) (marriage)-[:LOCATED_IN]->(:City {name:"London"}) $london isa city, has name "London"; $marriage (spouse: $person1, spouse: $person2) isa marriage; ($marriage, $london) isa located; Relationship Types Nested Relations
- 40. Type Hierarchies vehicle sub car aircraft truck sedan coupe minivan fixed-wing jet-aircraft garbage- truck heavy-truck sub sub sub sub sub sub sub sub sub Type Hierarchies
- 41. Type Hierarchies vehicle sub car aircraft truck sedan coupe minivan fixed-wing jet-aircraft garbage- truck heavy-truck sub sub sub sub sub sub sub sub sub define vehicle sub entity; car sub vehicle; sedan sub car; coupe sub car; minivan sub car; aircraft sub vehicle; fixed-wing sub aircraft; jet-aircraft sub aircraft; truck sub vehicle; garbage-truck sub truck; heavy-truck sub truck; Type Hierarchies
- 42. Insert type hierarchies Type Hierarchies
- 43. Insert type hierarchies CREATE (suzuki:Vehicle:Car:Minivan {name:"Maruti Suzuki Ciaz"}) CREATE (audi:Vehicle:Car:Coupe {name:"Audi A5"}) CREATE (airplane:Vehicle:Airplane {name:"Concept Airplane"}) CREATE (boeing:Vehicle:Airplane:Jet-aircraft {name:"Boeing 747"}) CREATE (siku:Vehicle:Truck:Garbage-truck {name:"Siku Super Bin"}) Type Hierarchies
- 44. Insert type hierarchies CREATE (suzuki:Vehicle:Car:Minivan {name:"Maruti Suzuki Ciaz"}) CREATE (audi:Vehicle:Car:Coupe {name:"Audi A5"}) CREATE (airplane:Vehicle:Airplane {name:"Concept Airplane"}) CREATE (boeing:Vehicle:Airplane:Jet-aircraft {name:"Boeing 747"}) CREATE (siku:Vehicle:Truck:Garbage-truck {name:"Siku Super Bin"}) insert $suzuki isa minivan, has name "Maruti Suzuki Ciaz"; $audi isa coupe, has name "Audi A5"; $aircraft isa aircraft, has name "Concept Airplane"; $boeing isa jet-aircraft, has name "Boeing 747"; $siku isa garbage-truck, has name "Siku Super Bin"; Type Hierarchies
- 45. Fetch all vehicles Type Hierarchies
- 46. MATCH(vehicle:Vehicle) RETURN Type Hierarchies Fetch all vehicles
- 47. MATCH(vehicle:Vehicle) RETURN match $vehicle isa vehicle; Type Hierarchies Fetch all vehicles
- 48. MATCH(vehicle:Vehicle) RETURN match $vehicle isa vehicle; But because your data isn’t validated, this Cypher query is unsafe! Type Hierarchies Fetch all vehicles
- 49. MATCH(vehicle:Vehicle) RETURN vehicle UNION MATCH (vehicle:Car) RETURN vehicle UNION MATCH (vehicle:Sedan) RETURN vehicle UNION MATCH (vehicle:Coupe) RETURN vehicle UNION MATCH (vehicle:Minivan) RETURN vehicle UNION MATCH (vehicle:Aircraft) RETURN vehicle UNION MATCH (vehicle:Fixed-wing) RETURN vehicle UNION MATCH (vehicle:Jet-aircraft) RETURN vehicle UNION MATCH (vehicle:Truck) RETURN vehicle UNION MATCH (vehicle:Garbage-truck) RETURN vehicle UNION MATCH (vehicle:Heavy-truck) RETURN vehicle match $vehicle isa vehicle; Type Hierarchies Fetch all vehicles
- 50. MATCH(vehicle:Vehicle) RETURN vehicle UNION MATCH (vehicle:Car) RETURN vehicle UNION MATCH (vehicle:Sedan) RETURN vehicle UNION MATCH (vehicle:Coupe) RETURN vehicle UNION MATCH (vehicle:Minivan) RETURN vehicle UNION MATCH (vehicle:Aircraft) RETURN vehicle UNION MATCH (vehicle:Fixed-wing) RETURN vehicle UNION MATCH (vehicle:Jet-aircraft) RETURN vehicle UNION MATCH (vehicle:Truck) RETURN vehicle UNION MATCH (vehicle:Garbage-truck) RETURN vehicle UNION MATCH (vehicle:Heavy-truck) RETURN vehicle match $vehicle isa vehicle; In Cypher, we’d be forced to decide between a long query with certainty or a short version without. Type Hierarchies Fetch all vehicles
- 51. Relation Type Hierarchies sub employment full-time part-time sub
- 52. sub employment full-time part-time sub define employment sub relation; part-time-employment sub employment; full-time-employment sub employment; Relation Type Hierarchies
- 53. Fetch all persons in an employment relation
- 54. MATCH (organisation)-[employs]->(employees) WHERE employs:PART_TIME_EMPLOYED OR employs:FULL_TIME_EMPLOYED OR employs:EMPLOYED RETURN employees Fetch all persons in an employment relation
- 55. MATCH (organisation)-[employs]->(employees) WHERE employs:PART_TIME_EMPLOYED OR employs:FULL_TIME_EMPLOYED OR employs:EMPLOYED RETURN employees match (employee: $person) isa employment; get $person; Fetch all persons in an employment relation
- 56. Introduction The Challenges of Working with a Graph Database Modelling and Defining Schema Reading Data Inference
- 57. Querying for Data Who are Susan and Bob’s common friends?
- 58. Querying for Data MATCH (bob:Person {name:"Bob"})-[:KNOWS]-> (susan:Person {name:"Susan"}) -[:KNOWS]->(friend:Person), (bob)-[:KNOWS]->(friend) RETURN friend Who are Susan and Bob’s common friends?
- 59. Querying for Data MATCH (bob:Person {name:"Bob"})-[:KNOWS]-> (susan:Person {name:"Susan"}) -[:KNOWS]->(friend:Person), (bob)-[:KNOWS]->(friend) RETURN friend match $bob isa person, has name "Bob"; $susan isa person, has name "Susan"; $friend isa person; ($bob, $susan) isa friendship; ($susan, $friend) isa friendship; ($friend, $bob) isa friendship; get $friend; Who are Susan and Bob’s common friends?
- 60. Querying for Data Which movies have been released after 2002 in the Odeon Cinema by Pixar?
- 61. Querying for Data MATCH (cinema:Cinema {name:”Odeon London"}), (london:City {name:"London"}), (pixar:Cinema {name:"Odeon"}), (london) <-[:LOCATED_AT]-(cinema) <-[:RELEASED_AT] -(movie:MOVIE) - [:PRODUCED_BY] -> (pixar) WHERE movie.year > 2002 RETURN movie.title Which movies have been released after 2002 in the Odeon Cinema by Pixar?
- 62. Querying for Data MATCH (cinema:Cinema {name:”Odeon London"}), (london:City {name:"London"}), (pixar:Cinema {name:"Odeon"}), (london) <-[:LOCATED_AT]-(cinema) <-[:RELEASED_AT] -(movie:MOVIE) - [:PRODUCED_BY] -> (pixar) WHERE movie.year > 2002 RETURN movie.title match $cinema isa cinema, has name "Odeon London"; $london isa city, has name "London"; $studio isa studio, has name "Pixar"; $movie isa movie, has title $title; ($london, $cinema) isa locating; ($cinema, $movie) isa release; ($movie, $studio) isa production, has year $year; $year > 2002; get $title; Which movies have been released after 2002 in the Odeon Cinema by Pixar?
- 63. Querying for Data city locating location cinema release movie production studio located released releasing producer produced year > 2002 has $title has “London” has “Odeon” has “Pixar” has
- 64. Introduction The Challenges of Working with a Graph Database Modelling and Defining Schema Reading Data Inference
- 65. Rule Based Inferencing Return all siblings
- 67. Rule Based Inferencing MATCH (sibling1:Person)-[:SIBLING_OF]->(sibling2:Person) OR (sibling3:Person)<-[:PARENT_OF]-(:Person)- [:PARENT_OF]->(sibling4:Person) RETURN match (sibling: $sibling1, sibling: $sibling2) isa siblingship; Return all siblings
- 68. Rule Based Inferencing $child parenthood $parent siblingship $child p a r e n t h o o d Return all siblings
- 69. Rule Based Inferencing rule siblingship: when { (parent: $parent, child: $child1) isa parenthood; (parent: $parent, child: $child2) isa parenthood; } then { (sibling: $child1, sibling: $child2) isa siblingship; }; $child parenthood $parent siblingship $child p a r e n t h o o d Return all siblings
- 70. Rule Based Inferencing Return all cousins
- 71. Rule Based Inferencing MATCH (grandparent:Person)-[PARENT_OF)->(parent1:Person) - [PARENT_OF]->(cousin1:Person), (grandparent)-[PARENT_OF)->(parent2:Person) - [PARENT_OF]->(cousin2:Person) RETURN cousin1, cousin2 Return all cousins
- 72. Rule Based Inferencing MATCH (grandparent:Person)-[PARENT_OF)->(parent1:Person) - [PARENT_OF]->(cousin1:Person), (grandparent)-[PARENT_OF)->(parent2:Person) - [PARENT_OF]->(cousin2:Person) RETURN cousin1, cousin2 match (cousin: $cousin1, cousin: $cousin2) isa cousinship; Return all cousins
- 73. Rule Based Inferencing $nephew auntship $parent cousinship $child p a r e n t s h i p Return all cousins
- 74. Rule Based Inferencing rule cousin-inferred: when { $parent isa person; $child isa person; $nephew isa person; (parent: $parent, child: $child) isa parenthood; (aunt: $parent, nephew: $nephew) isa auntship; } then { (cousin: $child, cousin: $nephew) isa cousinship; }; $nephew auntship $parent cousinship $child p a r e n t s h i p Return all cousins
- 75. Rule Based Inferencing Chaining Rules rule cousin-inferred: when { $parent isa person; $child isa person; $nephew isa person; (parent: $parent, child: $child) isa parenthood; (aunt: $parent, nephew: $nephew) isa auntship; } then { (cousin: $child, cousin: $nephew) isa cousinship; }; $nephew auntship $parent cousinship $child p a r e n t s h i p But what if one of these relations is inferred?
- 76. Rule Based Inferencing $sibling siblingship $parent auntship $child p a r e n t h o o d Chaining Rules
- 77. Rule Based Inferencing rule aunt-niece: when { $child isa person, has gender ”female"; $parent isa person; $sibling isa person; (parent: $parent, child: $child) isa parenthood; (sibling: $parent, sibling: $sibling) isa siblingship; } then { (niece: $child, aunt: $sibling) isa auntship; }; $sibling siblingship $parent auntship $child p a r e n t h o o d Chaining Rules
- 78. But we can also represent more complex business logic in rules
- 79. What are schedules that overlap?
- 80. MATCH (sched1:Schedule), (sched2:Schedule) WHERE sched1.end > sched2.start sched1.end <= sched2.end RETURN What are schedules that overlap?
- 81. MATCH (sched1:Schedule), (sched2:Schedule) WHERE sched1.end > sched2.start sched1.end <= sched2.end RETURN match $schedule isa schedule; ($schedule) isa overlaps; get $schedule; What are schedules that overlap?
- 82. Rule Based Inferencing Overlapping Schedules rule overlapping-schedule: when { $schedule1 isa schedule, has end $1End; $schedule2 isa schedule, has start $2Start, has end $2End; $1End > $2Start; $1End <= $2End; } then { ($schedule1, $schedule2) isa overlaps; };
- 83. Which disease risk factors does John Doe have?
- 84. Which disease risk factors does John Doe have?
- 85. The difference is the number of important operations we can perform without thinking about them Which disease risk factors does John Doe have?
- 86. 𝑖𝑓 𝑎 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑠 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 10 𝑢𝑛𝑖𝑡𝑠 𝑜𝑓 𝑎𝑙𝑐𝑜ℎ𝑜𝑙 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠 𝑇𝑦𝑝𝑒 𝐼𝐼 𝑎𝑛𝑑 𝐻𝑦𝑝𝑜𝑙𝑔𝑦𝑐𝑒𝑚𝑖𝑎
- 87. 𝑖𝑓 𝑎 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑠 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 10 𝑢𝑛𝑖𝑡𝑠 𝑜𝑓 𝑎𝑙𝑐𝑜ℎ𝑜𝑙 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠 𝑇𝑦𝑝𝑒 𝐼𝐼 𝑎𝑛𝑑 𝐻𝑦𝑝𝑜𝑙𝑔𝑦𝑐𝑒𝑚𝑖𝑎 𝑖𝑓 𝑎 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑠 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 12 𝑐𝑖𝑔𝑎𝑟𝑒𝑡𝑡𝑒𝑠 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑒 𝑠𝑐𝑙𝑒𝑟𝑜𝑠𝑖𝑠, ℎ𝑖𝑔ℎ 𝑏𝑙𝑜𝑜𝑑 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒, 𝐶𝑂𝑃𝐷, ℎ𝑖𝑔ℎ 𝑐ℎ𝑜𝑙𝑒𝑠𝑡𝑒𝑟𝑜𝑙 𝑎𝑛𝑑 ℎ𝑒𝑎𝑟𝑡 𝑑𝑖𝑠𝑒𝑎𝑠𝑒
- 88. 𝑖𝑓 𝑎 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑠 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 10 𝑢𝑛𝑖𝑡𝑠 𝑜𝑓 𝑎𝑙𝑐𝑜ℎ𝑜𝑙 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠 𝑇𝑦𝑝𝑒 𝐼𝐼 𝑎𝑛𝑑 𝐻𝑦𝑝𝑜𝑙𝑔𝑦𝑐𝑒𝑚𝑖𝑎 𝑖𝑓 𝑎 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑠 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 12 𝑐𝑖𝑔𝑎𝑟𝑒𝑡𝑡𝑒𝑠 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑒 𝑠𝑐𝑙𝑒𝑟𝑜𝑠𝑖𝑠, ℎ𝑖𝑔ℎ 𝑏𝑙𝑜𝑜𝑑 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒, 𝐶𝑂𝑃𝐷, ℎ𝑖𝑔ℎ 𝑐ℎ𝑜𝑙𝑒𝑠𝑡𝑒𝑟𝑜𝑙 𝑎𝑛𝑑 ℎ𝑒𝑎𝑟𝑡 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑖𝑓 𝑠𝑜𝑚𝑒𝑜𝑛𝑒! 𝑠 𝑝𝑎𝑟𝑒𝑛𝑡 ℎ𝑎𝑠 𝑏𝑒𝑒𝑛 𝑑𝑖𝑎𝑔𝑛𝑜𝑠𝑒𝑑 𝑤𝑖𝑡ℎ 𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠 𝐼𝐼 𝑎𝑛𝑑/𝑜𝑟 𝐴𝑟𝑡ℎ𝑟𝑖𝑡𝑖𝑠 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝑡ℎ𝑜𝑠𝑒 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑠 𝑡𝑜𝑜
- 89. 𝑖𝑓 𝑎 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑠 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 10 𝑢𝑛𝑖𝑡𝑠 𝑜𝑓 𝑎𝑙𝑐𝑜ℎ𝑜𝑙 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠 𝑇𝑦𝑝𝑒 𝐼𝐼 𝑎𝑛𝑑 𝐻𝑦𝑝𝑜𝑙𝑔𝑦𝑐𝑒𝑚𝑖𝑎 rule alcohol-risk-of-diabetes: rule alcohol-risk-of-diabetes: when { $p isa person; $c (consumer: $p, consumed-substance: $s) isa consumption, has units-per-week $u; $u >= 10; $s isa substance, has name "Alcohol"; $d isa disease, has name "Diabetes Type II"; $d2 isa disease, has name "Hypoglycemia"; } then { (person-at-risk: $p, risked-disease: $d, risked-disease: $d2) isa alcohol-risk-factor; };
- 90. 𝑖𝑓 𝑎 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑠 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 12 𝑐𝑖𝑔𝑎𝑟𝑒𝑡𝑡𝑒𝑠 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑒 𝑠𝑐𝑙𝑒𝑟𝑜𝑠𝑖𝑠, ℎ𝑖𝑔ℎ 𝑏𝑙𝑜𝑜𝑑 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒, 𝐶𝑂𝑃𝐷, ℎ𝑖𝑔ℎ 𝑐ℎ𝑜𝑙𝑒𝑠𝑡𝑒𝑟𝑜𝑙 𝑎𝑛𝑑 ℎ𝑒𝑎𝑟𝑡 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 rule smoking-risk-of-multiple-sclerosis: when { $p isa person; $c (consumer: $p, consumed-substance: $s) isa consumption, has units-per-week $u; $u >= 12; $s isa substance, has name "Cigarettes"; $d isa disease, has name "Multiple Sclerosis"; $d2 isa disease, has name "Lung Cancer"; $d3 isa disease, has name "High Blood Pressure"; $d4 isa disease, has name "Multiple Sclerosis"; $d5 isa disease, has name "Chronic Obstructive Pulmonary Disease"; $d6 isa disease, has name "Heart Disease"; } then { (person-at-risk: $p, risked-disease: $d, risked-disease: $d2, risked-disease: $d3, risked-disease: $d4, risked-disease: $d5, risked-disease: $d6) isa smoking-risk-factor; };
- 91. 𝑖𝑓 𝑠𝑜𝑚𝑒𝑜𝑛𝑒!𝑠 𝑝𝑎𝑟𝑒𝑛𝑡 ℎ𝑎𝑠 𝑏𝑒𝑒𝑛 𝑑𝑖𝑎𝑔𝑛𝑜𝑠𝑒𝑑 𝑤𝑖𝑡ℎ 𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠 𝐼𝐼 𝑎𝑛𝑑/𝑜𝑟 𝐴𝑟𝑡ℎ𝑟𝑖𝑡𝑖𝑠 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒𝑦 𝑎𝑟𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑜𝑓 𝑡ℎ𝑜𝑠𝑒 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑠 𝑡𝑜𝑜 rule hereditary-risk-of-diabetes: rule hereditary-risk-of-diabetes: when { $child isa person; $parent isa person; $cause (parent: $parent, child: $child) isa parentship; $diagnosis (patient: $parent, diagnosed-disease: $diabetes) isa diagnosis; $diagnosis2 (patient: $parent, diagnosed-disease: $arthritis) isa diagnosis; $diabetes isa disease, has name "Diabetes Type II"; $arthritis isa disease, has name "Arthritis"; } then { (person-at-risk: $child, risked-disease: $diabetes, risked-disease: $arthritis) isa hereditary-risk-factor; };
- 92. Type System Expressivity Safety Optimisation Inference How is TypeDB different to a graph database?
- 93. TypeDB Model Graph Model TypeDB is an abstraction layer over a graph database
- 94. Tomás Sabat Stöfsel Graph Databases vs TypeDB: What You Can’t Do With Graphs