# Rationale as a Query Language: from Frege to XML

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﻿Rationale as a Query Language: from Frege to XML Victor Vianu U.C. San Diego

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Logic and Databases: an example of overcoming adversity • FO lies at the center of present day database frameworks • Relational question dialects depend on FO: SQL, QBE • More capable inquiry dialects (the distance to XML) depend on expansions of FO • Foundations lie in established rationale FO : Frege social variable based math : Tarski

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Why is FO so fruitful as a question dialect? • simple to utilize syntactic variations SQL, QBE • productive usage through social polynomial math managable to examination and improvement • potential for impeccable scaling to extensive databases quick reaction can be accomplished utilizing parallel handling

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A social database: consumer bar brew frequents serves Joe's King's Bass Joe Molly's King's Bud Sue's Molly's Bass … ... … • legitimately a limited first-arrange structure

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Find the consumers who visit some bar serving Bass FO:  d :consumer |  b :bar (frequents( d,b )  serves( b, Bass ))  QBE: bar lager consumer bar frequents serves d b Bass consumer answer d

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not Find the consumers who visit some bar serving Bass FO:  d :consumer |  b :bar (frequents( d,b )  serves( b, Bass ))  ¬ QBE: bar brew consumer bar frequents serves d b Bass ¬ consumer answer d

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• Naïve execution : settled circles  d :consumer |  b :bar (frequents( d,b )  serves( b, Bass ))  for every consumer for every bar check the example Number of checks: |drinkers|  |bars| Roughly n : unsuitable for extensive databases! 2 • Better approach : social polynomial math

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Relational variable based math operations • union, contrast bar lager King's Bass Molly's Bass • choice  (serves) = … brew = Bass bar King's • projection  (serves) = bar Molly's …

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• join |  | frequents |  | serves consumer bar brew frequents serves Joe's King's Bass Joe Molly's King's Bud Sue's Molly's Bass … ... … consumer bar brew frequents |  | serves King's Bass Joe King's Bass Molly's Bass Joe King's Bud … Joe Molly's Bass Sue Molly's Bass …

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Relational polynomial math questions Find the consumers who visit some bar serving Bass  (  ( frequents |  | serves )) consumer lager = Bass consumer bar lager consumer bar brew consumer Joe King's Bass King's Bass King's Bass Joe Sue … .. Joe King's Bass Molly's Bass Molly's Bass Joe King's Bud … Joe Molly's Bass Joe Molly's Bass Sue Molly's Bass Sue Molly's Bass … Theorem: Relational variable based math and FO are proportionate

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Journey of a Query FO (SQL) z(P(xz)  Q(zy))  … Relational Algebra  13 (P Q)  … Query Rewriting  14 (P S)  Q  R Query Execution Plan Execution Physical Level   14 Q R  P S

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• modifying rules for polynomial math inquiries  (  ( frequents |  | serves )) consumer brew = Bass • productive calculations for individual operations Indexes: unique "catalogs" to information cost: generally n ( log n) much superior to n for huge databases! 2

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• revising rules for variable based math inquiries  (  ( frequents |  | serves )) consumer brew = Bass  [ frequents |  |  (  ( serves ))] consumer bar lager = Bass • proficient calculations for individual operations Indexes: exceptional "catalogs" to information cost: generally n ( log n) much superior to n for expansive databases! 2

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Most dynamite: hypothetical potential for immaculate scaling! • consummate scaling: given adequate assets, execution does not corrupt as the database gets to be bigger • key: parallel handling • cost: number of processors polynomial in the span of the database • part of variable based math: operations highlight parallelism

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Each polynomial math operation can on a fundamental level be actualized effectively in parallel Example: projection  (serves) bar brew bar serves King's Bass' King's Bud's Molly's Bass … Constant parallel time!

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Another illustration: join frequents |  | serves consumer bar frequents Joe King's Joe Molly's consumer bar brew Sue Molly's … ... Ruler's Bass Joe King's Bass Joe King's Bass Molly's Bass Joe King's Bud … Joe Molly's Bass bar brew Sue Molly's Bass serves King's Bass King's Bud … Molly's Bass …

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Every social variable based math inquiry takes consistent parallel time !  (  ( frequents |  | serves )) consumer lager = Bass  consumer  brew = Bass steady parallel time |  | frequents serves

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Summary in this way: • Keys to the accomplishment of FO as a question dialect : - convenience - effective execution by means of social variable based math • Constant parallel unpredictability: the maximum capacity of FO as an inquiry dialect remains yet to be figured it out!

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Beyond social databases: the Web and XML • relations supplanted by trees (XML information) • structure portrayed by outlines (e.g., DTDs) Again, rationale gives the establishments: • DTDs are proportional to tree automata (MSO on trees) • XML inquiries are basically tree transducers • Can utilize automata and rationale to comprehend semantics and expressiveness, perform static investigation

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Most XML question dialects are expansions of SQL • usage in light of same worldview • utilizes augmentations of social polynomial math • inquiry improvement expands upon social procedures

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Data Type Definition (DTD)  : letter set of component names, root   set of standards: e r consistent expression over  component name

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Documents fulfilling a DTD root … . e r e 1 … . e k  r Set of trees fulfilling DTD d: T(d)

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Example A DTD and a tree fulfilling it: root section*; area introduction, section*,conclusions; root segment introduction segment conc introduction conc introduction segment conc introduction conc introduction conc

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Specialization merchant UsedCars NewCars advertisement promotion show year display promotion has distinctive structure in various settings

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Specialization merchant UsedCars NewCars promotion utilized promotion new model year demonstrate promotion has diverse structure in various settings

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• What sets of trees can be characterized? Precisely the consistent tree dialects! - trees acknowledged by tree automata - trees characterized by Monadic Second-Order Logic (MSO) • XML inquiry dialects are basically tree transducers • Consequences: can utilize automata/rationale procedures to investigate and control DTDs and XML questions

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Example: static examination for powerful information reconciliation Integrated View Common DTD XML Source XML Source DTDs

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Example: static investigation for strong information joining Integrated View Tree machine B Common DTD Tree transducer T XML Source XML Source Tree robot A Source DTDs

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Example: static examination for vigorous information incorporation Integrated View Tree robot B Common DTD Tree transducer T XML Source XML Source Tree machine A Source DTDs Need to check: T(A)  B Key: T - 1 (B) quantifiable in MSO

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Conclusion Logic has given the establishments of databases, from social databases the distance to XML FO lies at the center of social database frameworks • XML and its question dialects are established upon tree automata, tree transducers, and rationales on trees • Implementation utilizes augmentations of social variable based math and expands upon social database systems