.. 443020,,., 6, vid@svptus.ru / : +7 (846) 333-27-70 :,, -, Abstract This article presents the approach to realization of the methodology FCA into the relation databases. (), - [,2]. (formal concept), -.,,, -.. - [3,4,0].,. -,. - OLAP-... -. (relation).. - :. <_ : _>, -. <_ : _ >.. -. -,. : R =< Q, A, J >, Q (); A 409
( ); J = Q A -. : K =< O, P, I >, O -, P I = O P - - [,2]. -. «-»., (fuzzy) [8,2]. : «CONTEXTS», «OBJECTS», «PROPERTIES» «SPCONTEXTS». - 3- (3NF) «- -». «CONTEXTS» :,,. «OBJECTS» -. - «NAE» «CONTEXT_RN». «OBJECT_RN» -. «NAE + CONTEXT_RN».. «PROPERTIES» -,.., «SPCONTEXTS». - «VALUE». OBJECTS OBJECT_RN (PK) NAE VARCHAR2(80); CONTEXT_RN (FK) CONTEXTS CONTEXT_RN (PK) NAE VARCHAR2(80); CREATE DATE; AUTHID VARCHAR2(30); SPCONTEXTS RN (PK) CONTEXT_RN (FK) OBJECT_RN (FK) PROPERTY_RN (FK) VALUE NUBER; PROPERTIES PROPERTY_RN (PK) NAE VARCHAR2(80); CONTEXT_RN (FK) -, - : (clarification), (reduction), (FCA merge) (concept lattices).. [2,6,7,3]. 40
-. 2 R ( A, A2,.. An ) R2( A, A2,.. Am ). : g( R, R2 ) : R[ Ai ] O; R2[ Aj ] P; R[ A θa2 ] R2 I, R [ A i ] O - R A i - O ; R2 [ Aj ] P - R2 A j - P ; R [ A θ A 2 ] R 2 I - R R2 - A A2 I = O P, θ - - A A2. : ) R [ A θ A2 ] R2 = I = O P = - ; 2) g( Rx, Rx ) = ( Rx[ A ]; Rx[ A2 ]; Rx[ A3 = A3 ] Rx ) ; 3) g R, R ) = g( R, R ) - ; ( x y y x ' 4) g( Rx, Ry ) g ( Rx, Ry ) -. :. : INSERT INTO CONTEXTS (NAE, CREATE, AUTHID) VALUES (<_>,sysdate,user); 2. O R: For crel in ( SELECT Ai FRO R GROUP BY Ai )loop INSERT INTO OBJECTS (NAE) VALUES(cRel.Ai); end loop; 3. P R2: For crel in ( SELECT Aj FRO R2 GROUP BY Aj)loop INSERT INTO PROPERTIES (NAE) VALUES(cRel.Aj); end loop; 4. I - R R2, - ( μ ( Ai, Aj ) = 0 μ( Ai, Aj ) = ; μ ( A i, A j ) [ 0, ] ): For crel in ( SELECT Ai, Aj, µ(ai, Aj) FRO R, R2 WHERE R.Ap=R2.Af GROUP BY Ai, Aj) loop INSERT INTO SPCONTEXTS (VALUE) VALUES(cRel.µ(Ai, Aj)); end loop. - ( ), -. 3 -. - 2. R «CARS», -, : -,,,. R2 «INTERVIEWEES» -, -., R3 «PREFERENCES» -. «EVALUATE» 4
R3 μ( A i, A j ) [ 0, ]. - CARS CAR_RN (PK) ARKA VARCHAR2(20); YEAR NUBER(4); CAR_BODY VARCHAR2(40); TOP_SPEED NUBER; 2 CARS (R) CAR_RN ARKA YEAR CAR_BODY TOP_SPEED 233907655-20 978 42 233907660-220 999 40 23390766-20 999 70 233907662-2 2004 75 233907663-22 2004 70 233907664-23 2004 50 233907665-24 2007 60 233907666-25 200 90 233907667-22 995 37 233907668 -Priora 2007 83 2 INTERVIEWEES (R2) INTERVIEW_RN GROUP_NAE LOCATION PRIORITY 233956770 22-55 00 23395677 7-25 00 233956772 55 00 233956773 22-55 80 233956774 7-25 80 233956775 55 80 233956776 22-55 70 233956777 7-25 70 233956778 55 70 3 PREFERENCES (R3) PREFERENCES CAR_RN (FK) INTERVIEW_RN (FK) PREFERENCE_NAE VARCHAR2(80); EVALUATE NUBER(); INTERVIEWEES INTERVIEW_RN (PK) GROUP_NAE VARCHAR2(80); LOCATION VARCHAR2(80); PRIORITY NUBER(7,2); CAR_RN INTERVIEW_RN PREFERENCE_NAE EVALUATE 233907660 233956770 0,68 233907660 233956772 0,8 233907668 23395677 0,45 233907668 233956774 0,55 233907660 233956776 0 233907668 23395677 0,93 233907668 233956773 0,75 233907660 233956777 0,63 233907668 233956772 0,52 233907668 233956775 0,84 233907668 23395677 00. 233907668 23395677 0,35 233907668 233956777 0,57 233907668 233956772 R R3: g( R, R3 ) = ( R[ ARKA]; R3[ PREFERENCE _ NAE]; R[ CAR _ RN = CAR _ RN] R3 ) " Cars Pr eference". 4., 5, R2 R3: 42
g( R, R ) = ( R [ GROUP _ NAE, LOCATION ]; R [ PREFERENCE _ NAE]; R [ INTERVIEW _ RN = INTERVIEW _ RN] R ) 2 2 3 2 3 " Interviewees Pr eference". R R2 0,5. 4 «Cars-Preferences» - -20 0,45 0.5 0.5 0.5 0.5 0.5 0,37 0.5-220 0,8 0,64 0.5 0,73 0.5 0.5 0.5 0,59-20 0.5 0.5 0,47 0.5 0.5 0,23 0.5 0.5-2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0,62-22 0.5 0,66 0,22 0.48 0,56 0.5 0.5 0.5-23 0,67 0.5 0.5 0.5 0.5 0.5 0.5 0,52-24 0,92 0.5 0.5 0,64 0,77 0.5 0,7 0,70-25 0,78 0,33 0,65 0.5 0.5 0.5 0.5 0,68-22 0,34 0,57 0.5 0,83 0.5 0,4 0.5 0,8 -Priora 0,98 0,99 0,88 0.5 0,69 0,85 0.5 0,92 5 «Interviewees-Preferences» 3-22-55 ( - ) 7-25 ( ) 55 ( - ) - 22-55 ( - ) 7-25 ( ) 55 ( - ) - 22-55 ( - ) 7-25 ( ) 55 ( - ) - - 0,64 0.5 0.5 0,89 0.5 0,75 0,54 0,80 0,96 0,84 0,96 0,9 0,88 0.5 0.5 0.5 0,34 0.5 0.5 0,67 0.5 0,84 0.5 0,96 0,57 0,2 0.5 0,78 0.5 0.5 0.5 0,83 0,90 0.5 0,94 0,96 0,9 0.5 0.5 0.5 0.5 0.5 0.5 0,60 0.5 0,86 0,33 0,93 0,62 0,40 0,67 0,86 0.5 0.5 0.5 0,84 0,88 0,47 0,99 0,89 0,89 0.5 0.5 0.5 0.5 0,8 0.5 0.5 0.5 0,79 0.5 0,92 [2]... - : < " Cars Pr eferences" > < " Interviewees Pr eferences" > [3]. -,, - [4]. -, 43
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