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Add Link to this content
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Add to my playlist
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Lecture Structure
- Local as View (LaV) oka CREATEV EW S1 AS (00:03:36)
- v v 1 (WHEREJahr= 2OC (00:03:36)
- OO l lELlQ llIIlCl lEl l llU l I Cgl Jc1IlI J( lIIC)( lebenbed ng ng a) r 21300)S42 NeueFi meBöse(Tite Regie Genre)(Hebenbed ng g Jahr 2000S53 AktuelleFi me(Tite Regie Gen(T IebenbedmgL ng Ja (WHEREJahr 2000)CREATE VlE N S4 ASSELECT Titel Reg e GenreFROM FilmHEREJahr 2000CREATEVIEW S5 AS (00:03:36)
- Local as View (LaV) globale ICs (00:05:21)
- dem globalen Schema kann (00:05:21)
- SELECT Titel Regie Genre (00:05:21)
- cREATE V1EWAIIeFi meBöseAS (00:05:21)
- Überblick (00:06:20)
- LaV Anwendung Anfrageoptirnierung (00:06:22)
- LaV Anwendung Anfrageoptimierung (00:06:24)
- La Anwendung Datawarehouse Design (00:06:25)
- LaV Anwendung Semantisches Caching (00:06:26)
- La Anwendung Anfrageoptimierung (00:06:37)
- Write auf MV (00:06:37)
- Welche Sichten helfen bei der (00:06:37)
- AST Advanced Summary (00:06:37)
- (mater alized views MV) auf (00:06:37)
- La Anwendungen (00:06:38)
- Local as View (Lav) globale ICs (00:06:42)
- geprüft werden (00:06:42)
- SELECT T e Regie Genre (00:06:42)
- CREATEv EWA e =nmeBöseAs (00:06:42)
- Nebenbedingung Jahr 2000 (00:06:42)
- Menenbecnngung Jak r = ZÜOÜ (00:06:42)
- LaV Anwendungen (00:08:19)
- LaV Anwendung Anfrageoptimierung (00:08:22)
- La Anwendung Datawarehouse Design (00:08:23)
- Überblick (00:08:24)
- Anfrageplar ung (00:08:25)
- Query Containment (00:08:27)
- Anfrageplär e (00:08:28)
- Anfragebearbeitung (00:09:27)
- Beispiel (00:10:34)
- u SELECT profFROM DB kurs DWHERE D univ = HPI M n 71 rmn = = G =m= = (00:10:34)
- rageSELECT prof FROM Lehrt L Kurs KWHERE L kurs id = K kurs idAND Ktitel L KE47 Datenbanken (00:10:34)
- LaV Visualisierung (OWA) (00:10:36)
- Beispiel (00:11:35)
- WHERE L L niv = HPI (00:11:35)
- FROM Kurs K (00:11:35)
- Umgeschriebene Anfrage (00:11:35)
- Kuniv HP (00:11:35)
- AND (00:11:35)
- WH ERE L kurs d = K kurs d (00:11:35)
- FROM Lehrt L Kurs K (00:11:35)
- Quelle 1 Alle Datenbankveranstalt (00:11:35)
- EIV Beispiel vergleich (00:13:42)
- CWA (00:16:16)
- Anfrageergebnisse können sich ändern (00:16:16)
- Open World Assumption (OWA) (00:16:16)
- Closed World Assumpt on (CWA) (00:16:16)
- La Beispiel Vergleich (00:17:51)
- geschriebene AnfragSELECT titel kurs idFROM DB kurs DWHERE D univ = HPI ELECT titel kurs idFROM H Pl VLMaximaleAntwort (00:17:51)
- ä fÜ ? L Kurs gE gf f D 9WHERE L Kurs u = urs u WHERE = Pr AHWVOFT (CWA)AND K tite = Datenbanken G ba eA f a9e geschriebe Anfrag Vollstand eAND L univ (00:17:51)
- Antwort (00:17:52)
- Maximale (00:17:52)
- Vollständige (00:17:52)
- Umgäfzl rieäene Anfrage (00:17:52)
- CWA OWA (00:18:27)
- Welchen Anteil an der wofld hat das Ergebnis? (00:18:27)
- Closed World Assumption (CWA) (00:18:27)
- Anfrageergebnisse können sich ändern (00:18:30)
- Open World Assumption (OWA) (00:18:30)
- Lav Beispiel Vergleich (00:18:39)
- Beispiel (00:18:43)
- WHERE L univ = HPI (00:18:43)
- FROM Kurs K (00:18:43)
- Umgeschriebene Anfrage (00:18:43)
- Quelle 1 Alle Datenbankveranstalt (00:18:43)
- Kurs(Rurs id i univ) (00:18:43)
- Beispiel Vergleich (00:19:02)
- CWA OWA Beispiel (00:19:03)
- CWA OWA (00:23:00)
- w 4if CREATE VIEW V2 ASSELECTB FROM R frage SELECT FROM R CCWA (a b) muss in der Exte n von R se n (00:24:30)
- l VIGW 1 f TE v1Ev @ DAs 7 ELECTA FROM R Evrnncinn 1 1 (00:24:30)
- CWA OWA Beispiel (00:24:32)
- Anfrage SELECT FROM R (00:24:32)
- Relation R(A B) (00:24:32)
- OWA (a b) muss nicht in der Extension von R säml (00:25:54)
- CWA (a b) muss in der Extension von Ffsein (00:25:54)
- 3ELE Ü rf 1 Extension b Öl kw (00:25:54)
- Anfragebearbeitung (00:26:56)
- Überblick (00:29:01)
- Anfrageumschreibungen (00:29:06)
- Anfrageumschreibungen (WdH) (00:31:26)
- Beispiele (00:31:27)
- L univ = Humbo di (00:34:48)
- NP vollständigin Q Q nach (00:36:29)
- t l l Q i W FROM äi ily (00:36:29)
- Prüfung von containment durch Prüfung (00:36:29)
- Mehrere Algorithmen (00:37:38)
- N P vollständig in Q Q nach (00:37:38)
- I Prüfung von containment durch Existenz (00:37:38)
- Zu komplex (00:37:38)
- E5C (00:37:38)
- I aller möglichen Datenbanken? (00:37:38)
- Prüfung von containment durch Prüfung (00:37:38)
- CM77 (00:37:39)
- Prüfung von containment durch Existenz (00:37:39)
- aller möglichen Datenbanken? (00:37:39)
- Datalog Notation (00:38:19)
- SQL Datalog (00:40:17)
- product (PID PN PGN) 1 (00:40:17)
- @ducc (PID PN Pam (00:40:43)
- S prc uct d = (00:40:43)
- SELECT (00:41:29)
- ducc d AND (00:41:29)
- uct d (00:41:30)
- WHERE s c agyea (00:41:30)
- 1999 S (00:41:49)
- Notation (00:41:50)
- 1 uq = I die Menge aller Symbole von (00:41:50)
- ( die Menge aller Konstanten von q (00:41:50)
- J H lp die Menge aller Variablen vun f (00:41:50)
- r beliebige (00:41:50)
- min ex ensionalen Pr dikaben (00:41:50)
- Kons amen Eine k j Da g f g as eine Anfrage (00:41:50)
- Sei I eine Menge vun Variablensymbolen und ( eine Menge von (00:41:50)
- Query Containment (00:42:52)
- map(Mn Ms) c one(Mn Cn ) map(Mn Ms) (00:42:52)
- map(Mn Ms) Mn (00:42:52)
- rnap(Mn Ms) g map(Mn Ms) (00:42:52)
- A query p is containedin a QUEÜ u (p gu) iff al tup es computed (00:42:52)
- Beweis für Query Containment (00:46:52)
- map(IV n l4s) clone(Mn Cn ) (00:46:52)
- p ugd einx (00:46:52)
- map(Mh MS) (00:46:53)
- map(I fn fs) clone(Mn Cn ) (00:46:53)
- Finden von Containmenth Mappings (00:47:46)
- Bei Interesse siehe Buch (00:47:46)
- Auffächerung nach möglichen CMS (00:47:46)
- Problem ist NP vollständig (00:47:46)
- Weitere Containment Beispiele (00:47:47)
- Q product(PID PN PGID PGN) (00:47:47)
- product(PID PN PGID PGN) PGN= Wasser (00:47:47)
- product(PID PN PGID PGN) Q localization(SID SN RID RN) (00:47:47)
- Finden von Containment Map ppings (00:47:49)
- Weitere Containment Beispiele (00:48:48)
- Finden von Contair ment Mappings (00:49:14)
- Beweis für Query Containment (00:49:18)
- map(Mh I TS) (00:49:18)
- map(IV n 4s) clone(Mn Cn ) (00:49:18)
- p u gdw ein containment mapping von u nach p existiert (00:49:18)
- Weitere Cor tainment Beispiele (00:49:25)
- pm ucr(P1 3 PN pero PGN) Q Rn) RN) (00:49:25)
- prod C1ft(PID PI k PGID Wasser ) proüuct(P1D PN PGID PGN) (00:49:25)
- fä f Uä z Zh) ? (00:50:31)
- product(PID PN PGID PGN) PGN= Wasser (00:50:31)
- proauct( = 1D PN PGID W er g pr duct(P1 PN PG1 2 j (00:50:31)
- Weitere Containment Beispiele (00:53:09)
- product(PID PN PGID PGN) PGN= Wasser (00:53:09)
- product(PID PN PGID PGN) Q localization(SID SN RID RN) (00:53:09)
- Beispiel (00:58:25)
- patnLA ) patn k 7 Patn u) path(A B) path(B C) path(C D) path(D E) (00:58:25)
- MÄF (00:58:25)
- h(D) 28 h(D) 3l (01:00:13)
- sales (X Y Z ) time(f Ü (01:00:13)
- Erzeugung der Anfragen (01:00:55)
- d DB Beobachtung SW d ß äf g lnterpretat on als F In j ? f f f@ 1 Ecor ta nment 22 Ä J g aa mg UIQID Ws om V 2 s rwqS rma n Neger i = =s enu n new inep lß vmewb d PYU Shmgpf f if ucnung n mm d 5 am spendenDB rmen) i l i g buchung 7 ? 1 uugnung u me US 13 IDB SladKH3l lS 1E l 0 g) V datum fm 4 menge e mw w U 2 Lmw mk w (01:01:38)
- be rag Q M projekt Sm (01:01:40)
- mm mm r na n T14 name N9 stadt W fw f Sn nde omname = m (01:01:40)
- uummgnu=nun yn (01:02:23)
- E eLspendenDB rmen haushaItDB skadlHausha t oru 5 End ID euvg pmp n wspend r Ü an (01:02:23)
- s iif i f i lf 7stadt M Tm arg z mß (01:02:23)
- (spendenDB rm a oßen) stadtHausha t org)5PEmrm n Wegespena n wiebetrag Q9 Mpmje mspender =Üa r Ei ahm spena nproj fvw bucmm nhuch nnlß wwdatum me menue 1 mmnewwmahuch mg rx (01:03:24)
- m mm nna n @= T f 1 f9 i gn W 1name mSladl =1rrv J omname rmxSpende 77 VW V f n M einnahmespena n f f Spepdel = V nru (01:03:24)
- Üa (01:03:25)
- pm fm Jbunhunglß mv ne u7ct f ng Anu h ng n (01:03:25)
- rm rmal ale v name nrw Sladl ir ndSn ndes au Worg g urgn @ 1 omname mf w (01:03:25)
- Erzeugung cler Anfragen (01:03:44)
- Anfrageumschreibung (01:03:45)
- Überblick (01:11:19)
- Anfrageumschreibung (01:11:20)
- Bucket Algorithmus (nächster Foliensatz) (01:11:20)
- Sichte e Prädikate) (01:11:20)
- e Prädikate) (01:12:40)
- Anfrageumschreibung (01:16:55)
- Überblick (01:16:57)
- Global Local as View 4 (GLAV) (01:17:54)
- Anfrageplanung (01:18:56)
- Überblick (01:21:04)
- Vergieich GaV (01:21:07)
- Vergleich Gav (01:23:27)
- Literatur (01:24:57)
- LMSS95 Alan Y Levy (01:24:57)
- CP 77 fshm f a g qr d af a 1 Qpt mal implernen at o f of (01:24:57)
- Hul 97 Manag ng Semantic Heterogene ty n Databases1A Theoret cal (01:24:57)
- UIIOO Jeffrey D Ullmanz Information Integration Using Logica View (01:24:57)
- Levy01 A on Y Halevyz Answerir g queries us ng views A survey in (01:24:57)
- Mw gqn v wmm nnrm (01:25:37)
- Mw gqn v wmm (01:25:38)
- g=v (01:25:55)
Keyword
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