Evolution of Fjyswan. Extended SQL Grammar and SQL to SPARQL Translation. Kuba Szulaczkowski Andrew Oldag

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1 Evolution of Fjyswan Extended SQL Grammar and SQL to SPARQL Translation Kuba Szulaczkowski Andrew Oldag

2 Original Fjyswan SQL commands are dissected into strings and expressions Expressions are evaluated, the command is recombined and passed to SQL server SQL commands that can be passed as SQL or SPARQL. CONNECT CREATE INSERT SELECT DROP jsqlparser used to parse SQL statements for translation to SPARQL

3 New SQL Features Grammar rules expanded to support DROP (TABLE, VIEW, INDEX, TRIGGER) CREATE (TABLE, VIEW, INDEX, TRIGGER) SQL SELECT... JOIN and GROUP BY SQL SELECT... subqueries, aggregates SQL UPDATE SQL DELETE

4 New SPARQL Features SPARQL WHERE from SQL SELECT SPARQL JOIN from SQL SELECT SPARQL DELETE SPARQL UPDATE

5 Translation of SQL to SPARQL - SELECT SQL SPARQL PETS SQL Table PETID NAME BIRTH_YEAR 1001 COUSCOUS 2005 PETS Table converted to RDF triple store URI: URI:PETID URI:1001 URI: URI:NAME URI:COUSCOUS URI: URI:BIRTH_YEAR URI:2005 URI = ' Query: SELECT * FROM PETS; Results: PETID NAME BIRTH_YEAR 1001 COUSCOUS 2005 Query converted to SPARQL + SEM_MATCH: SELECT sub, pred, obj FROM TABLE( SEM_MATCH( '(?sub?pred?obj)', SEM_MODELS('PETS_CS345_oldagaw'), null, null, null)); Results: URI: URI:PETID URI:1001 URI: URI:NAME URI:COUSCOUS URI: URI:BIRTH_YEAR URI:2005

6 SQL to SPARQL - SELECT + WHERE SQL SPARQL Query: PETID NAME BIRTH_YEAR 1001 COUSCOUS MAPLE 2009 SELECT petid, name, birth_year FROM PETS WHERE birth_year < 2007; RDF triple store now has 6 rows... but similar to the previous slide Query converted to SPARQL + SEM_MATCH: SELECT b, c, d FROM TABLE( SEM_MATCH( ' (?sub uri:petid?b) (?sub uri:name?c) (?sub uri:birth_year?d)', SEM_MODELS('PETS_CS345_oldagaw'), null, SEM_ALIASES(SEM_ALIAS('uri', ' 'd$_suffix < 2007')); Results: PETID NAME BIRTH_YEAR Results: URI:1001 URI:COUSCOUS URI: COUSCOUS 2005

7 SQL to SPARQL - SELECT + JOIN SQL SPARQL Query: VETS table VETID NAME HIRED_YEAR Results: 7 DHARMA 2010 VISITS table VETID PETID TIME PRICE SELECT VISITS.TIME, VISITS.PRICE, VETS.NAME FROM VISITS JOIN VETS ON VISITS.VETID = VETS.VETID; TIME PRICE NAME 1 75 DHARMA Now two RDF triple stores, (VETS and VISITS). SELECT b, c, d FROM TABLE( SEM_MATCH(' (?sub0 uri0:time?b) (?sub0 uri0:price?c) (?sub1 uri1:name?d) (?sub0 uri0:vetid?obj_join1a) (?sub1 uri1:vetid?obj_join1b)', SEM_MODELS('VISITS_CS345_oldagaw', 'VETS_CS345_oldagaw'), null, SEM_ALIASES( SEM_ALIAS('uri0', ' SEM_ALIAS('uri1', ' 'obj_join1a$_suffix = obj_join1b$_suffix') ); Results: Note: uri0, uri1 mapping in SEM_MATCH uri0:1 uri0:75 uri1:dharma

8 Properly translates complex queries. SELECT b, c, d, e FROM TABLE( SEM_MATCH(' (?sub0 uri0:price?b) (?sub1 uri1:name?c) (?sub2 uri2:name?d) (?sub3 uri3:species?e) (?sub1 uri1:vetid?obj_join1a) (?sub0 uri0:vetid?obj_join1b) (?sub2 uri2:petid?obj_join2a) (?sub0 uri0:petid?obj_join2b) (?sub2 uri2:typeid?obj_join3a) (?sub3 uri3:typeid?obj_join3b)', SELECT VISITS.PRICE, VETS.NAME, PETS.NAME, ANIMALTYPES.SPECIES FROM VISITS JOIN VETS ON VETS.VETID = VISITS.VETID JOIN PETS ON PETS.PETID = VISITS.PETID JOIN ANIMALTYPES ON PETS.TYPEID = ANIMALTYPES.TYPEID WHERE VISITS.PRICE < ((lambda x:x*4)(25)); SEM_MODELS( 'VISITS_CS345_oldagaw', 'VETS_CS345_oldagaw', 'PETS_CS345_oldagaw', 'ANIMALTYPES_CS345_oldagaw'), null, SEM_ALIASES( SEM_ALIAS('uri0', ' SEM_ALIAS('uri1', ' SEM_ALIAS('uri2', ' SEM_ALIAS('uri3', ' org/animaltypes/')), 'obj_join1a$_suffix = obj_join1b$_suffix and obj_join2a$_suffix = obj_join2b$_suffix and obj_join3a$_suffix = obj_join3b$_suffix and b$_suffix < 100'));

9 PETS PETID NAME BIRTH_YEAR 1001 COUSCOUS 2005 DELETE FROM pets WHERE petid = 1001; PETID NAME BIRTH_YEAR

10 PETS_RDF_DATA SUBJECT PROPERTY OBJECT URI: URI:PETID URI:1001 URI: URI:NAME URI:COUSCOUS URI: URI:BIRTH_YEAR URI:2005 DELETE FROM pets_rdf_data b WHERE EXISTS( SELECT a.triple.get_subject() FROM pets_rdf_data a WHERE a.triple.get_property() = '< AND SUBSTR(a.triple.GET_OBJ_VALUE(), 23, 4) = '1001' AND a.triple.get_subject() = b.triple.get_subject()) SUBJECT PROPERTY OBJECT

11 VETS VETID NAME HIRED_YEAR 4001 ANDREW 2011 UPDATE vets SET name = 'HARRY' WHERE vetid = 4001; VETID NAME HIRED_YEAR 4001 HARRY 2011

12 VETS_RDF_DATA SUBJECT PROPERTY OBJECT URI: URI:VETID URI:4001 URI: URI:NAME URI:ANDREW URI: URI:HIRED_YEAR URI:2011 UPDATE vets_rdf_data b SET b.triple = SDO_RDF_TRIPLE_S ('vets_cs345_oldagaw', b.triple. GET_SUBJECT(), b.triple.get_property(), '< org/vets/harry>') WHERE EXISTS ( SELECT a.id FROM vets_rdf_data a WHERE a.triple.get_property() = '< AND SUBSTR(a.triple.GET_OBJ_VALUE(), 23, 4) = '4001' AND a.triple.get_subject() = b.triple.get_subject() AND b.triple.get_property() = '< SUBJECT PROPERTY OBJECT URI: URI:VETID URI:4001 URI: URI:NAME URI:HARRY URI: URI:HIRED_YEAR URI:2011

13 Back up

14 SPARQL SELECT + JOIN: In 2 places? SELECT b, c, d FROM TABLE( SEM_MATCH(' (?sub0 uri0:time?b) (?sub0 uri0:price?c) (?sub1 uri1:name?d) (?sub0 uri0:vetid?obj_join1a) (?sub1 uri1:vetid?obj_join1b)', Variables to be returned join filtering SEM_MODELS('VISITS_CS345_oldagaw', 'VETS_CS345_oldagaw'), null, SEM_ALIASES( SEM_ALIAS('uri0', ' SEM_ALIAS('uri1', ' 'obj_join1a$_suffix = obj_join1b$_suffix') ); Removes entries that do not satisfy the requirements of the join (VISITS.VETID = VETS.VETID) Without this filter, TIME, PRICE and NAME entries from the entire cross product of VISITS and VETS are returned. Variables needed for STEP 1) SPARQL - Produces the full cross product of VISITS and VETS. Buried in the cross product is the join that you want. STEP 2) FILTER - Sift through the cross product and keep only the entries of interest. Note the difference between uri0 and uri1. Early on we removed the table name from the URIs with the understanding that it would require unique predicate names in each table because it would allow for easy joins using SPARQL only. However this goes against the concept of a static universal vocabulary - so the table names are back. And the work on creating a join is broken up between SPARQL and filtering. "A Relation is a subset of the cross-product of a set of domains."

15 *.py script output=select VISITS.PRICE, VETS.NAME, PETS.NAME, ANIMALTYPES.SPECIES FROM VISITS JOIN VETS ON VETS.VETID = VISITS.VETID JOIN PETS ON PETS.PETID = VISITS.PETID JOIN ANIMALTYPES ON PETS.TYPEID = ANIMALTYPES.TYPEID WHERE VISITS.PRICE < ((lambda x: x*4)(25)); Expressions evaluated SELECT VISITS.PRICE, VETS.NAME, PETS.NAME, ANIMALTYPES.SPECIES FROM VISITS JOIN VETS ON VETS.VETID = VISITS.VETID JOIN PETS ON PETS.PETID = VISITS.PETID JOIN ANIMALTYPES ON PETS.TYPEID = ANIMALTYPES.TYPEID WHERE VISITS.PRICE < 100

16 Translated to SPARQL SELECT b, c, d, e FROM TABLE(SEM_MATCH(' (?sub0 uri0:price?b) (?sub1 uri1:name?c) (?sub2 uri2:name?d) (?sub3 uri3:species?e) (?sub1 uri1: VETID?obj_join1a) (?sub0 uri0:vetid?obj_join1b) (?sub2 uri2:petid? obj_join2a) (?sub0 uri0:petid?obj_join2b) (?sub2 uri2:typeid? obj_join3a) (?sub3 uri3:typeid?obj_join3b)',sem_models ('VISITS_CS345_oldagaw', 'VETS_CS345_oldagaw', 'PETS_CS345_oldagaw', 'ANIMALTYPES_CS345_oldagaw'), null, SEM_ALIASES(SEM_ALIAS('uri2', 'www. example.org/pets/'), SEM_ALIAS('uri3', ' SEM_ALIAS('uri1', ' SEM_ALIAS('uri0', 'www. example.org/visits/')), 'obj_join1a$_suffix = obj_join1b$_suffix and obj_join2a$_suffix = obj_join2b$_suffix and obj_join3a$_suffix = obj_join3b$_suffix and b$_suffix < 100')) Results ('B', 'C', 'D', 'E') ('URI/VISITS/20', 'URI/VETS/CATHY', 'URI/PETS/PUMPKIN', 'URI/ANIMALTYPES/CAT') ('URI/VISITS/50', 'URI/VETS/ELIZABETH', 'URI/PETS/COUSCOUS', 'URI/ANIMALTYPES/CAT') ('URI/VISITS/95', 'URI/VETS/BOBBY', 'URI/PETS/MAPLE', 'URI/ANIMALTYPES/CAT') ('URI/VISITS/15', 'URI/VETS/DHARMA', 'URI/PETS/LOLA', 'URI/ANIMALTYPES/RABBIT') ('URI/VISITS/80', 'URI/VETS/ELIZABETH', 'URI/PETS/BARKLY', 'URI/ANIMALTYPES/DOG') NOTE: URI was used in place of ' in the output section just to save space on the slide.

Visitor Pattern versus Parse-Translate-Combine. Comparison of code readability in ReL for translation from SQL to SPARQL

Visitor Pattern versus Parse-Translate-Combine Comparison of code readability in ReL for translation from SQL to SPARQL Parse-Translate-Combine Given a valid SQL command string, extract tokens of interest