1. Remarks to Optimization Rules for the End User Programming Language OttoVonG Opatia 8.6.2007
Klaus Benecke IWS/FIN, Otto-von-Guericke-Universität Magdeburg Postfach 4120 Magdeburg, Germany 39016, Sachsen/Anhalt

2. 1 Aims of OttoVonG Universal Enduser Query Language for
Documents (XML)
Tables (databases)
Internet (of XML-Documents)
Graphics

3. 1 Objects of OttoVonG Generating Operations El_tab Tag0 Tuple_t Coll_t
Alternate_t

4. 2 Counter Examples for Optimization Rules (1)
<< M( A, L( B, C))::
1 2 3
4 5 >>
(Tabment T0)
(a) B=4(B::C=3(T0) )  B::C=3(B=4(T0))

5. 2 Counter Examples for Optimization Rules (2)
<< M( A, L( B, C))::
1 2 3
4 5 >>
(Tabment T0)
(b) B::pos(B)=1 (B::B=4(T0)) 
 B::B=4(B::pos(B)=1(T0))

6. 2 Counter Examples for Optimization Rules (3)
<< M( A, L( B, C))::
1 2 3
4 5 >>
(Tabment T0)
(c) B::C=3(L(C)[-1]=5(T0)) 
 L(C)[-1]=5(B::C=3(T0))

7. 2 Attributes name C(name) (C collection symbol; M; B; L) pos(name)
Attribute[i] (i: integer)

8. 2 Nonrecursive Example DTD
NAME TYPOS
TABMENT L(A?, B, M(C, D))
C E, F
D M(H)
F M(G)
A, TEXT

9. 2 Example Extended Tree L | (A: TEXT)?, (B: TEXT), M |
  |
(A: TEXT)?, (B: TEXT), M
  |
(C: (E: TEXT), (F: M)), (D: M)
  | |
(G: TEXT) (H: TEXT)

10. 2 Condition Types (1) Simple condition
name1:: cond1, where cond1 contains only names and deepest name of cond1 is as deep as name1
example: G:: G=B
counter example1: E:: G=B
counter example2: n:: G=H

11. 2 Condition Types (2) Relational condition name1::: cond1, where
name1:: cond1 is simple
example: G::: G=B
abbreviates: G:: G=B
E:: G=B
A:: G=B

12. 3 Commuting Conditions(1)
EMPS: M(ENO, NAME, FIRSTNAME, LOCATION,
SALARY, SEX, PATENTCNT, INSTITUTE,
M(HOBBY), M(PROJECT, TIME))
Query 1: not commuting conditions
aus EMPS
gib B-(SALARY, NAME, LOCATION, SEX)
mit LOCATION=”Magdeburg” ## simple condition
mit pos(SALARY) < 50 ## position selecting condition

13. 3 Commuting Conditions(2)
EMPS: M(ENO, NAME, FIRSTNAME, LOCATION,
SALARY, SEX, PATENTCNT, INSTITUTE,
M(HOBBY), M(PROJECT, TIME))
Query 2: commuting conditions
aus EMPS
mit PROJECT:: TIME > # simple condition
mit LOCATION=”Magdeburg” # simple condition

14. 3 Commuting Conditions (3)
cond2(cond1(tab)) = cond1(cond2(tab)),
if one of the following conditions is satisfied:
1 cond1 and cond2 are simple.
2 one condition does not select in a fix level of
the other
3 cond1 and cond2 are relational.
4 cond1 and cond2 refer to the same level and are
not position selecting.

15. 4 Absorption of a Condition
Query 3:
aus EMPS
mit PROJECT:: PROJECT in L(“otto” ”SQL” ”XML”)
mit TIME > 10 # existential condition
mit TIME>10 i PROJECT in L(“otto” ”SQL” ”XML”)
mit PROJECT:: PROJECT in L(“otto” ”SQL” ”XML”)

16. 5 Smuggling a Condition (1)
Query 4:
aus EMPS
mit LOCATION = ”Magdeburg”
mit PROJECT:: TIME > 10
gib M(PROJECT, M(NAME, ENO, TIME))
mit LOCATION=”Magdeburg”
mit PROJECT:: TIME>10
mit PROJECT = PROJECT

17. 5 Smuggling a Condition (2)
Query 4 continued:
aus EMPS
mit LOCATION=”Magdeburg”
mit TIME > 10
mit PROJECT:: TIME>10
gib M(PROJECT, M(NAME, ENO, TIME))
mit PROJECT ::: TIME>10

18. 6 Smuggling the Forget-Operation  (1)
The forget-operation  is a relatively simple operation, which is similar to the relational projection, but which differs from projection in 3 points.
The argument of forget is not a list of attributes, which remain in the resulting structure, but the list of attributes, which are to omit.
forget does not omit duplicates in sets.
forget can be used also in recursive structures

19. 6 Smuggling the Forget-Operation  (2)
Query 5 a:
aus EMPS
mit LOCATION = ”Magdeburg”
gib M(INSTITUTE, B(NAME, SALARY))
forget HOBBY, PROJECT, TIME, SEX,…

20. 6 Smuggling the Forget-Operation  (3)
Query 5 b:
aus EMPS
gib M(NAME, HOBBY, PROJECT)
forget NAME, HOBBY, PROJECT, TIME, SEX,…

21. 7 Rules with the Extension Operation ext ()(1)
Query 7: a hierarchical join
FACULTIES: M(FAC, DEAN, FACBUDGET)
INSTITUTES: M(INSTI, MGR, BUDGET, FAC)
ext F := FACULTIES
ext G := INSTITUTES at FACBUDGET
mit INSTI:: F/FAC = G/FAC
mit FACBUDGET >
mit INSTI:: BUDGET > 10000

22. 7 Rules with the Extension Operation ext ()(2)
Query 7: a hierarchical join of
aus INSTITUTES
mit BUDGET > 10000
=: $instis
aus F:=FACULTIES
mit FACBUDGET >
ext G := $insts at FACBUDGET
mit INSTI:: F/FAC = G/FAC

23. 7 Rules with the Extension Operation ext ()(3)
sel-ext1
cond(assi(tab)) = assi (cond (tab)),
this rule holds, if all operations are applicable, and the extension does not introduce a name, which is used in the condition.
Counter example for sel-ext1:
<< L(A, B)::
1 2>>
ext B := 3 at B
mit B = 3

24. 7 Rules with the Extension Operation ext ()(4)
sel-ext2
cond(X:=tab2 at Y(tab1))=X:= ( cond (tab2)) at Y(tab1),
here is presupposed that all operations are applicable, that cond is a ::-condition and does not contain a name from tab1.

25. 7 Rules with the Extension Operation ext ()(5)
ext-ext
assi1(assi2(tab)) = assi2(assi1(tab)),
this rule holds, if right and left hand side are defined and assi1 and assi2 have no common names or tabment names.
Counter example for ext-ext (without importance):
ext A := 1
ext C := 3 at A
ext B := 2 at A # result type: A, B, C

26. 7 Rules with the Extension Operation ext ()(6)
Counter example for ext-ext (without importance):
ext A := 1
ext B := 2 at A
ext A := 3 at A # result type: (A, A, B)
but
ext A := 3 at A
ext B := 2 at A # result type: (A, B, A, B)

27. Summary
We have powerful operations, which are implemented for XML-documents and TAB-files;
this implementation must be improved and generalized in several points
include optimization strategies;
generalize it to databases and Intranet

28. Thank you for attention