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DES: a Deductive Database System Des.Sourceforge.Net DES: A Deductive Database System des.sourceforge.net FernandoFernando SSááenzenz PPéérezrez GrupoGrupo dede ProgramaciProgramacióónn DeclarativaDeclarativa (GPD)(GPD) DeptDept.. IngenierIngenierííaa deldel SoftwareSoftware ee InteligenciaInteligencia ArtificialArtificial UniversidadUniversidad ComplutenseComplutense dede MadridMadrid FacultadFacultad dede InformInformááticatica PROLE 2010 10/9/2010 1 / 30 ContentsContents 1.1. IntroductionIntroduction 2.2. FeaturesFeatures 3.3. QueryQuery LanguagesLanguages 4.4. OuterOuter JoinsJoins 5.5. AggregatesAggregates 6.6. DESDES asas aa TestTest--BedBed forfor ResearchResearch 7.7. ImpactImpact FactorFactor 8.8. ConclusionsConclusions PROLE 2010 10/9/2010 2 / 30 1.1. IntroductionIntroduction Databases:Databases: FromFrom relationalrelational toto deductivedeductive (Declarative)(Declarative) QueryQuery Languages:Languages: FromFrom SQLSQL toto DatalogDatalog PROLE 2010 10/9/2010 3 / 30 1.1. Introduction:Introduction: DatalogDatalog AA databasedatabase queryquery languagelanguage stemmingstemming fromfrom PrologProlog PrologProlog DatalogDatalog PredicatePredicate RelationRelation GoalGoal QueryQuery MeaningMeaning ofof aa predicatepredicate ((Multi)setMulti)set ofof derivablederivable factsfacts Intensionally (Rules or Clauses) Extensionally (Facts) PROLE 2010 10/9/2010 4 / 30 1.1. Introduction:Introduction: DatalogDatalog WhatWhat aa typicaltypical databasedatabase useruser wouldwould expectexpect fromfrom aa queryquery language?language? FiniteFinite data,data, finitefinite computationscomputations (terminating(terminating queries)queries) No terms or bound depth Be aware of built-in infinite relations! AllAll answeranswer tuplestuples atat onceonce Prolog returns serveral answers upon backtracking PROLE 2010 10/9/2010 5 / 30 1.1. Introduction:Introduction: SystemsSystems DeductiveDeductive databasedatabase systems:systems: LDL++,LDL++, DLV,DLV, Coral,Coral, XSB,XSB, SDS,SDS, Declare,Declare, ConceptBaseConceptBase,, …… YetYet anotheranother system,system, Why?Why? WeWe neededneeded anan interactiveinteractive systemsystem targetedtargeted atat teachingteaching DatalogDatalog inin classroomsclassrooms So,So, whatwhat aa wholewhole setset ofof featuresfeatures wewe wouldwould askask forfor suchsuch aa system?system? PROLE 2010 10/9/2010 6 / 30 2.2. FeaturesFeatures 2.1.2.1. RequiredRequired FeaturesFeatures AA systemsystem orientedoriented atat teachingteaching UserUser--friendly:friendly: InstallationInstallation UsabilityUsability MultiplatformMultiplatform (Windows,(Windows, Linux,Linux, Mac,Mac, ……)) InteractiveInteractive DatabaseDatabase updatesupdates PROLE 2010 10/9/2010 7 / 30 2.2.2.2. DESDES ConcreteConcrete FeaturesFeatures (1/2)(1/2) Free, Open-source, Multiplatform, Portable Query languages sharing EDB/IDB: Datalog (Recursive) SQL Database updates: SQL DML Commands Temporary Datalog views Duplicates (v2.0) Declarative debugging of Datalog programs Test case generation for SQL views Datalog and SQL tracers (v2.0) PROLE 2010 10/9/2010 8 / 30 2.2.2.2. DESDES ConcreteConcrete FeaturesFeatures (2/2)(2/2) Null value support à la SQL Outer joins for both SQL and Datalog Aggregates Negation Integrity constraints: Domain Referential integrity Full-fledged arithmetics Type system for SQL tables and views Souce-to-source program transformations: Safety Performance (simplifications) Tabling-based implementation PROLE 2010 10/9/2010 9 / 30 3.3. QueryQuery LanguagesLanguages 3.1.3.1. DatalogDatalog (1/2)(1/2) Program: Set of rules. Rule: head :- body. head. Head: Positive atom. Body: Conjunctions (,) and disjunctions (;) of literals Literal: Atom, negated atom or a built-in. Query: Literal with variables or constants in arguments Body (Conjunctive queries, …) Temporary views PROLE 2010 10/9/2010 10 / 30 tom grace 3.1.3.1. DatalogDatalog (2/2)(2/2) jack amy tony carolI ExampleExample fred carolII father(tom,amy). father(jack,fred). carolIII father(tony,carolII). father(fred,carolIII). DES-Datalog> ancestor(tom,X) { mother(graceI,amy). ancestor(tom,amy), mother(amy,fred). ancestor(tom,carolIII), mother(carolI,carolII). ancestor(tom,fred) mother(carolII,carolIII). } parent(X,Y) :- father(X,Y). DES-Datalog> father(X,Y),mother(Y,Z) parent(X,Y) :- mother(X,Y). answer(X,Y,Z) :- father(X,Y), ancestor(X,Y) :- mother(Y,Z). parent(X,Y). { ancestor(X,Y) :- answer(tom,amy,fred), parent(X,Z), answer(tony,carolII,carolIII) ancestor(Z,Y). ancestor(Z,Y). } PROLE 2010 10/9/2010 11 / 30 3.2.3.2. SQLSQL (1/3)(1/3) DQL: SELECT … FROM … WHERE WITH RECURSIVE … DML: INSERT … UPDATE … DELETE … DDL: CREATE [OR REPLACE] TABLE … CREATE [OR REPLACE] VIEW … DROP … PROLE 2010 10/9/2010 12 / 30 3.2.3.2. SQLSQL (2/3)(2/3) ExampleExample CREATE VIEW parent(parent,child) AS SELECT * FROM father UNION SELECT * FROM mother; CREATE OR REPLACE VIEW ancestor(ancestor,descendant) AS WITH RECURSIVE rec_ancestor(ancestor,descendant) AS SELECT * FROM parent UNION SELECT parent,descendant FROM parent,rec_ancestor WHERE parent.child=rec_ancestor.ancestor SELECT * FROM rec_ancestor; DES-SQL> SELECT * FROM ancestor WHERE ancestor='tom'; PROLE 2010 10/9/2010 13 / 30 3.2.3.2. SQLSQL (3/3)(3/3) SimplifiedSimplified SyntaxSyntax CREATE OR REPLACE VIEW ancestor(ancestor, descendant) AS SELECT parent, child FROM parent UNION SELECT parent, descendant FROM parent, ancestor WHERE parent.child=ancestor.ancestor; PROLE 2010 10/9/2010 14 / 30 3.3.3.3. DatalogDatalog andand SQLSQL Deductive engine (DE): Tabling implementation Datalog programs are solved by DE Compilation of SQL views and queries to Datalog programs SQL queries are also solved by DE Corollary: SQL and Datalog do share the deductive database! Datalog programs can refer to predicates defined as views in SQL PROLE 2010 10/9/2010 15 / 30 3.4.3.4. ODBCODBC ConnectionsConnections New feature in version 2.0, released on August Access to Relational DBMS MySQL MS Access Oracle … SQL statements injected to the DBMS engine Query results are cached by the Datalog engine So, interoperability is allowed! PROLE 2010 10/9/2010 16 / 30 4.4. OuterOuter JoinsJoins (1/2)(1/2) NullNull values:values: CteCte.:.: null Functions:Functions: is_null(Var) is_not_null(Var) OuterOuter joinjoin builtbuilt--insins:: Left:Left: lj(Left_Rel,Right_Rel,ON_Condition) Right:Right: rj(Left_Rel,Right_Rel,ON_Condition) Full:Full: fj(Left_Rel,Right_Rel,ON_Condition) PROLE 2010 10/9/2010 17 / 30 4.4. OuterOuter JoinsJoins (2/2)(2/2) lj(a(X), b(Y), X=Y) SELECT * FROM a LEFT JOIN b ON x=y; lj(a(X), b(X), true) SELECT * FROM a LEFT JOIN b WHERE x=y; lj(a(X), rj(b(Y), c(U,V), Y=U), X=Y) SELECT * FROM a LEFT JOIN (b RIGHT JOIN c ON y=u) ON x=y; PROLE 2010 10/9/2010 18 / 30 5.5. AggregatesAggregates (1/5)(1/5) AggregateAggregate functions:functions: count – COUNT(*) count(Var) – COUNT(Column) min(Var) max(Var) sum(Var) avg(Var) times(Var) PROLE 2010 10/9/2010 19 / 30 5.5. AggregatesAggregates (2/5)(2/5) PredicatePredicate group_by/3 group_by( Relation_A, % FROM / WHERE [Var_1,…,Var_n], % GroupingGrouping columnscolumns Relation_B) % HAVING / ProjectionProjection PROLE 2010 10/9/2010 20 / 30 5.5. AggregatesAggregates (3/5)(3/5) ExampleExample employee Number of employees for each department: Name Department Salary DES-Datalog> group_by(employee(N,D,S), anderson accounting 1200 [D], R=count) andrews accounting 1200 arlingon accounting 1000 Info: Processing: answer(D,R) :- nolan null null group_by(employee(N,D,S),[D],R = count). norton null null { randall resources 800 answer(accounting,3), answer(null,2), sanders sales null answer(resources,1), silver sales 1000 answer(sales,5) } smith sales 1000 Info: 4 tuples computed. Steel sales 1020 Sullivan sales null PROLE 2010 10/9/2010 21 / 30 5.5. AggregatesAggregates (3/5)(3/5) ExampleExample ((contdcontd.).) employee Name Department Salary Active employees (those with assigned salaries): anderson accounting 1200 DES-Datalog> group_by(employee(N,D,S), andrews accounting 1200 [D], R=count(S)) arlingon accounting 1000 nolan null null Info: Processing: answer(D,R) :- norton null null group_by(employee(N,D,S),[D],R = count(S)). randall resources 800 { answer(accounting,3), sanders sales null answer(null,0), silver sales 1000 answer(resources,1), answer(sales,3) smith sales 1000 } Steel sales 1020 Info: 4 tuples computed. Sullivan sales null PROLE 2010 10/9/2010 22 / 30 5.5. AggregatesAggregates (3/5)(3/5) ExampleExample ((contdcontd.).) employee Active employees of departments with more than one active employee: Name Department Salary anderson accounting 1200 DES-Datalog> group_by(employee(N,D,S), andrews accounting 1200 [D], arlingon accounting 1000 count(S)>1) nolan null null Info: Processing: norton null null answer(D) :- group_by(employee(N,D,S), randall resources 800 [D], sanders sales null (A = count(S),A > 1)). { silver sales 1000 answer(accounting), smith sales 1000 answer(sales) } Steel sales 1020 Info: 2 tuples computed. Sullivan sales null PROLE 2010 10/9/2010 23 / 30 5.5. AggregatesAggregates (4/5)(4/5) AggregateAggregate Predicates:Predicates: count(Rel) – COUNT(*) count(Rel,Var) – COUNT(Column) min(Rel,Var) max(Rel,Var) sum(Rel,Var) avg(Rel,Var) times(Rel,Var) PROLE 2010 10/9/2010 24 / 30 5.5. AggregatesAggregates (5/5)(5/5) ExampleExample % SQL Program % Datalog Program CREATE OR REPLACE VIEW path(X,Y,1) :- shortest_paths(Origin,Destination,Length) AS edge(X,Y). WITH RECURSIVE path(X,Y,L) :- path(Origin,Destination,Length) AS path(X,Z,L0), (SELECT edge.*,1
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