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San Diego 2010

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Linked data, Life Sciences and RDF Stores - Exploration and Tutorials …

Linked data, Life Sciences and RDF Stores - Exploration and Tutorials

by Jans Aasman, CEO Franz Inc

San Diego Semantic web Jan 30, 2010 presentation

Published in: Education, Travel, Business
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  • 1.      
  • 2.                
  • 3.    •  •  •    •  •  • 
  • 4.                          
  • 5.    • • • • • • • • • •
  • 6.  createTripleStore(“seminar.db" ) addTriple (Person1 first-name Steve) addTriple (Person1 isa Organizer) addTriple (Person1 age 52) addTriple (Person2 first-name Jans) addTriple (Person2 isa Psychologist) addTriple (Person2 age 50) addTriple (Person3 first-name Craig) addTriple (Person3 isa SalesPerson) addTriple (Person3 age 32) addTriple (Person1 colleague-of Person2) addTriple (Person1 colleague-of Person3) addTriple (Person1 likes Pizza)
  • 7.   addTriple ( Person3 neighbor-of Person1) addTriple ( Person3 neighbor-of Person2) addTriple ( Person3 !o:lives-in !o:Place1111) addTriple ( Place1111 !o:name !"Moraga") addTriple ( Place1111 !o:latitude !"37.12223") addTriple ( Place1111 !o:longitude !"-122.4325")
  • 8.  
  • 9.             
  • 10.   
  • 11.   
  • 12.                       
  • 13.  
  • 14.   S1 type stream-segment  S1 upstream S2  S1 upstream S3  S1 left-drainage D1  S1 right-drainage D2  S1 longitude1 12.1  S1 latitude1 -121.2  S1 longitude2 12.12  S1 latitude2 -121.3 
  • 15. 
  • 16.  
  • 17. 
  • 18.    
  • 19.   
  • 20. GSK Competitive Analysis: > 50 % of new products come from buying up small innovative pharma research companies  > 2000 need to be tracked for competitive analysis  We tracked in one system New management New investors New whitepapers New products mentioned that are of interested to GSK New customers New partners
  • 21. 
  • 22.                      
  • 23.                
  • 24.  DB1 DB2 DB1000 Integrated Schemas, Data Integration CUSTOMER CARE INVENTORY CTR 1. Semantified Schema’s 2. Product and customer ontologies 3. Customer -> DB links MARKETING ANALYSIS 4. Product -> DB links 5. Customer/product aggregations
  • 25.             
  • 26.             
  • 27. 
  • 28.                     
  • 29. 
  • 30.                
  • 31. 
  • 32.                  
  • 33. 
  • 34.           Tech reasons      Business   Considerations  
  • 35.                                        
  • 36.                    
  • 37.                    
  • 38.                   
  • 39.   LUBM(8000) Total query time 1200 1000 800 Seco n d s 600 Series1 400 200 0 AllegroGraph 3.2 Other Total
  • 40. LUBM(8000) queries 800 700 600 Seconds 500 AllegroGraph 3.2 400 300 Other 200 100 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Query Num ber
  • 41. LUBM(8000) with long queries zeroed 1 0.8 Seconds 0.6 AllegroGraph 3.2 0.4 Other 0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Queries
  • 42.  LUBM(8000) Total Time 200000 150000 Total Query Time Seconds 100000 Type Materializations 50000 Loading and Indexing 0 Other / Static AllegroGraph AllegroGraph 3.2 3.2 Federated
  • 43.     Find all meetings that happened in November within 5 miles of Berkeley that was attended by the most important person in Jans’ friends and friends of friends. (select (?x) (ego-group person:jans knows ?group 2) SNA (actor-centrality-members ?group knows ?x ?num) SNA (q ?event fr:actor ?x) DB Lookup (qs ?event rdf:type fr:Meeting) RDFS (interval-during ?event “2008-11-01” “2008-11-06”) Temporal (geo-box-around geoname:Berkeley ?event 5 miles) Spatial !)
  • 44.                        
  • 45.                 
  • 46.                    ’  
  • 47.           

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