### Table 1. Overview of inheritance preserving transformation rules.

"... In PAGE 7: ... In both a Petri-net and a process-algebraic setting we have developed a comprehen- sive set of inheritance preserving transformation rules (cf. Table1 ). A detailed descrip- tion of these rules is beyond the scope of this paper.... ..."

### Table 2. Lossless and FD Preserving Decompositions

"... In PAGE 15: ... Consider the ternary relationship R(X, Y, Z) with cardinality M:1:1 (case #8), and binary impositions M:1 (X, Y) and 1:1 (Y, Z). According to Table2 this relation can be lossless and FD preserving, decomposed to its binary equivalent of S(X, Y) and T(Y,Z). An example of the original table and decomposition, with appropriate instances is shown in Example 3 and Example 4, below.... In PAGE 15: ... Example 3 shows data as stored in the structure derived from a ternary relationship. Example 4 shows the data stored in the decomposed, functional dependency preserving alternative structure (see also Table2... In PAGE 18: ... Implicit Semantic Constraint Enforcement Case # Ternary Cardinality (X:Y:Z) Binary Impositions Potential Lossless Decomposition Potential FD Preserving Decomposition Enforces Semantic Constraints on Insertions Enforces Semantic Constraints on Deletions 1 1:1:1 (X:Y) = (M:1) (XY)(XZ) None No No 2 1:1:1 (X:Y) = (1:1) (XY)(XZ) -or- (XY)(YZ) (XY)(XZ) -or- (XY)(YZ) Yes Yes 3 1:1:1 (X:Y) = (M:1) (Z:Y) = (M:1) (XY)(XZ) -or- (XZ)(ZY) (XY)(XZ) -or- (XZ)(ZY) Yes Yes 4 1:1:1 (X:Y) = (M:1) (X:Z) = (1:1) (XY)(XZ) -or- (XZ)(ZY) (XY)(XZ) -or- (XZ)(ZY) Yes Yes 5 M:1:1 (X:Y) = (M:1) (XY)(XZ) (XY)(XZ) Yes Yes 6 M:1:1 (Y:Z) = (M:1) (XY)(YZ) None No No 7 M:1:1 (Y:Z) = (1:1) (XY)(YZ) -or- (XZ)(ZY) (XY)(YZ) -or- (XZ)(ZY) Yes No 8 M:1:1 (X:Y) = (M:1) (Y:Z) = (1:1) (XY)(YZ) -or- (XZ)(ZY) -or- (XY)(XZ) (XY)(YZ) -or- (XZ)(ZY) Yes No 9 M:1:1 (X:Y) = (M:1) (Y:Z) = (1:M) (XZ)(ZY) -or- (XY)(XZ) (XZ)(ZY) Yes No 10 M:N:1 (X:Z) = (M:1) (XY)(XZ) (XY)(XZ) Yes No 11 M:N:1 (X:Z) = (M:1) (Y:Z) = (M:1) (XY)(XZ) -or- (XY)(YZ) None No No 12 M:N:P Not Allowed None None No No 2.4 Exhaustive analysis Table 3 below (which extends Table2 ) provides an exhaustive analysis of all ternary ... ..."

### TABLE IV TRANSFORMED SAMPLE TO PRESERVE PRIVACY

### Table 3. Elapsed time for analysis and transformation.

2001

"... In PAGE 12: ... Elapsed time for analysis and transformation. Elapsed Time of Program Transformation The elapsed time for analysis and transformation of the bytecode transformer is shown in Table3 . In every case, analysis takes more time than transformation.... ..."

Cited by 11

### Table 3 Analysis of Transformation/What

"... In PAGE 9: ...Table 4 Analysis of Who (Clients, Actors, Owners) The entries in Table3 are fairly self-explanatory. Systems are typically large - the exception being Case 3 - RSP.... ..."

### Table 1. Semantics-preservation properties of presented transformations.

2007

"... In PAGE 12: ... Finally, eliminating and de- creasing transformations allow for metamodel destruction. Table1 groups the transformations presented in this section by this classification. It also gives semantics-preservation properties and inverse transformations.... ..."

Cited by 3

### Table 5: Transformation rules for RML

1998

"... In PAGE 1: ...g., see Table5 #29. This is particularly attractive for functional language com- pilers #28e.... In PAGE 5: ... The syntax of this restriction of RML is presented in Table 4. Table5 describes a set of meaning preserving source-to- source transformation rules for RML. The transformations are intended to improve the performance of programs either directly #28e.... In PAGE 8: ...3 Contexts A useful class of rules are those whose left-hand sides do not match a #0Cxed pattern but match a top pattern and some in- ner patterns which occur in contexts. For instance, consider the #28Inline#29 and #28Select#29 rules in Table5 . Contexts can also be implemented with the where clause.... In PAGE 9: ... The module imports several auxiliary modules that are de#0Cned in the appendices. Observe that the speci#0Ccation of the rules is very close to the original rules in Table5 . The main di#0Berence is that the inline rule has been split into two rules.... ..."

Cited by 114

### Table 5. HARE analysis of the transformed coronary heart disease data. cases with missing all cases, two values deleted covariates deleted Basis function

1995

"... In PAGE 18: ... Keeping this in mind, we applied HARE to the transformed data, with the missing cases deleted. The HARE model is summarized in Table5 . As it turned out, none of the covariates SBP, age, serum cholesterol and smoking that ended up in the model have missing values.... In PAGE 18: ... This allows us to apply HARE to the complete dataset using just the six covariates that have no missing values. This model is also summarized in Table5 . As can be seen, both models are linear proportional hazards models and they are nearly identical to each other, the only di erence being one extra basis function when the cases with missing values are deleted.... ..."

Cited by 4

### Table 2: Case analysis in the proof of Lemma 36.

1996

"... In PAGE 24: ... This case reduces to the previous one by an appeal to Lemma 21. The above case analysis is summarized in Table2 . In all cases we obtain 1 from by applying one or more transformations [o] 1, [i], [d] 2, [v], [t] together with an additional application of swap in case (1)(d) and (3).... In PAGE 28: ... Proof. According to Table2 we have to consider cases (2) and (3) in the proof of Lemma 36. We consider here only case (2).... ..."

Cited by 44

### Table 2 Business transformation projects portfolio used in electronics case study

in N. Abe

"... In PAGE 12: ... First, we describe the results of the metric correlation analysis. Metric correlation analysis The transform regression technique was used to develop a regression model for the expected improvement in each of the financial metrics listed in Table2 , using the dependency tree tool to help choose the explanatory variables whenever appro- priate. More specifically, the modeling was done to predict the expected annual percentage change for each financial metric in the following year, as a function of the percentage changes in the opera- tional and on demand metrics in the current year.... In PAGE 14: ... Each project incurs a cost for implementation and requires an estimated combination of the two resource types that are required to execute the project (financial and operational head count). The duration of each project along with its costs and resource require- ments are summarized in Table2 . The available budget is treated as a consumable resource; the unconsumed amounts at the end of each quarter are assumed to carry forward to the beginning of the next quarter.... In PAGE 16: ... As a first step toward incorporating the variance in the financial improvements that are delivered by the various projects, we developed a Monte Carlo analysis in which the deterministic optimization analysis is carried out for each stochastically sampled set of financial improvement metrics. We assumed that the expected values of the financial improvement predictions in Table2 follow a normal distribution, and chose a uniformly weighted linear combination of the financial metric values as the proxy of the financial optimization objective in each deterministic optimization run. A total of 1000 Monte Carlo simulations of randomized financial improvement predictions were run through the deterministic portfolio optimizer.... ..."