### TABLE 1 Proportion of Subjects in Each Condition Whose Data Were Best Fit by Rule-Based Versus Information-Integration Models

### TABLE 2 The four classes of meta-relations imply four classes of integrity rules govern- ing the processing of the metadata instances. The rules are listed below; the first three are analogous to the usual relational integrity rules [5], but they are more exact because of the meaning of the OER model itself.

in Information Resources Management in Heterogeneous, Distributed Environments: A Metadatabase Approach

1991

Cited by 26

### Table II. Elements of the Mae Integrated System Model Concept Modeling Capability Components, connectors, interfaces Types and instances Compatibility rules amp; expectations Behaviors and constraints Hierarchical composition Subarchitectures

2004

Cited by 2

### Table 1. Level and Methodology of Integration

"... In PAGE 5: ... Agent- based integration utilizes contextual meta-knowledge represented in the form of facts and rules while workflow-based integration utilizes procedural knowledge represented in the form of process models. Because user interface level integration is a technique for opening up legacy systems, this level may participate equally within both methodology categories (see Table1 ). In our work, the focus on cross- infrastructure interaction has led us to adopt a brokered approach.... ..."

### Table 1: Inductive rules for EMPA integrated interleaving semantics

1998

"... In PAGE 8: ..., and i(M) to denote the multiset obtained by projecting the tuples in multiset M on their i-th component. Thus, e.g., ( 1(PM2))( lt;a; gt;) in the fth part of Table1 denotes the multiplicity of tuples of PM2 whose rst component is lt;a; gt;.... In PAGE 9: ... 3(c) is exactly the result of the application to E of the rules in Table 1 equipped with the auxiliary functions mentioned above. The formal de nition of the integrated interleaving semantics for EMPA is based on the transition relation ???!, which is the least subset of G Act G satisfying the inference rule in the rst part of Table1 . This rule selects the potential moves that have the highest priority level (or are passive), and then merges together those having the same action type, the same priority level and the same derivative term.... In PAGE 9: ... The rst operation is carried out through functions Select : Mu n(PMove) ?! Mu n(PMove) and PL : Act ?! APLev, which are de ned in the third part of Table 1. The second operation is carried out through function Melt : Mu n(PMove) ?! P n(PMove) and partial function Min : (ARate ARate) ?! o ARate, which are de ned in the fourth part of Table1 . We recall that function Melt, whose introduction is motivated by the drawback cited in the example above, avoids burdening transitions with auxiliary labels as well as keeping track of the fact that some transitions may have multiplicity greater than one.... In PAGE 11: ...in the second part of Table1 according to the intuitive meaning of operators explained in Sect.... In PAGE 11: ... The normalization operates in such a way that applying Min to the rates of the synchronizations involving the active action gives as a result the rate of the active action itself, and that each synchronization is assigned the same execution probability. This normalization is carried out through partial function Norm : (AType ARate ARate Mu n(PMove) Mu n(PMove)) ?!o ARate and function Split : (ARate R I ]0;1]) ?! ARate, which are de ned in the fth part of Table1 . Note that Norm(a; ~ 1; ~ 2; PM 1; PM 2) is de ned if and only if min(~ ; ~ ) = , which is the condition on action rates we have required in Sect.... In PAGE 27: ... To solve the problem, we follow the proposal of [BBK96] by introducing a priority operator \ ( ) quot;: priority levels are taken to be potential, and they become e ective only within the scope of the priority operator. We thus consider the language L generated by the following syntax E ::= 0 j lt;a; ~ gt;:E j E=L j E[ apos;] j (E) j E + E j E kS E j A whose semantic rules are those in Table1 except that the rule in the rst part is replaced by ( lt;a; ~ gt;; E0) 2 Melt(PM (E)) E a;~ ???! E0 and the following rule for the priority operator is introduced in the second part... In PAGE 33: ...ollowing the guideline of Sect. 3.2, we de ne the transition relation ???! as the least subset of Mu n(V) ActMufin(V) Mu n(V) generated by the inference rule reported in the rst part of Table 2, which in turn is based on the multiset PM (Q) 2 Mu n(ActMufin(V) Mu n(V)) of potential moves of Q 2 Mu n(V) de ned by structural induction in the second part of Table 2. These rules are strictly related to those in Table1 for the integrated interleaving semantics of EMPA terms. The major di erences are listed below and are clari ed by the corresponding upcoming examples: 1.... In PAGE 34: ...6 Consider term E lt;a; ~ gt;:0k; lt;b; ~ gt;:0 whose decomposition is given bydec(E) = fj lt;a; ~ gt;:0 k; id; id k; lt;b; ~ gt;:0jg By applying the rules in Table 2, we get the two independent transitions fj lt;a; ~ gt;:0 k; id jg norm( lt;a;~ gt;; lt;a;~ gt;:0k; id;1) ????????????????????! fj 0k; id jg fj id k; lt;b; ~ gt;:0 jg norm( lt;b;~ gt;;id k; lt;b;~ gt;:0;1) ????????????????????! fj id k; 0 jg as expected. If we replaced the three rules for the parallel composition operator with a single rule similar to that in Table1 , then we would get instead the two alternative transitions dec(E) norm( lt;a;~ gt;; lt;a;~ gt;:0k; id;1) ????????????????????! fj 0k; id; id k; lt;b; ~ gt;:0jg dec(E) norm( lt;b;~ gt;;id k; lt;b;~ gt;:0;1) ????????????????????! fj lt;a; ~ gt;:0k; id; id k; 0 jg which are not consistent with the fact that the two subterms of E are independent, thereby resulting in a violation of the concurrency principle (see Sect. 7:4).... In PAGE 49: ... The tool driver, which is written in C [KR88] and uses Lex [Les75] and YACC [Joh75], includes routines for parsing EMPA speci cations and performing lexical, syntactic, and static semantic (closure, guardedness, niteness) checks on the speci cations. The integrated kernel, which is implemented in C, currently contains only the routines to generate the integrated interleaving semantic model of EMPA speci cations according to the rules of Table1 : this kernel will be extended by implementing a EMB checking algorithm. The functional kernel, which is written in C, is based on a version of CWB-NC [CS96] that was retargeted for EMPA using PAC-NC [CMS95].... ..."

Cited by 25

### Table 1. Inductive rules for EMPAr integrated interleaving semantics

1997

"... In PAGE 5: ... The integrated semantics of EMPAr terms can be de ned by exploiting again the idea of potential move: the multiset 1 of the potential moves of a given term is inductively computed, then those potential moves having the highest priority level are selected and appropriately merged. The formal de nition is based on the transition relation ???!, which is the least subset of Gr Actr Gr satisfying the inference rule reported in the rst part of Table1 . This rule selects the potential moves having the highest priority level, and then merges together those having the same action type, the same priority level and the same 1 We use \fj quot; and \jg quot; as brackets for multisets, \ quot; to denote multiset union, Mu n(S) (P n(S)) to denote the collection of nite multisets (sets) over set S, M(s) to denote the multiplicity of element s in multiset M, and i(M) to denote the multiset obtained by projecting the tuples in multiset M on their i-th component.... In PAGE 5: ...g., ( 1(PM2))( lt;a; ; 0 gt;) in the fth part of Table1 denotes the multiplicity... In PAGE 7: ...Mu n(Actr Gr) ?! Mu n(Actr Gr) and PLr : Actr ?! PLevel, which are de ned in the third part of Table1 . The second operation is carried out through function Meltr : Mu n(Actr Gr) ?! P n(Actr Gr) and partial function Min : (ARate ARate) ?!o ARate, which are de ned in the fourth part of Table 1.... In PAGE 7: ...Mu n(Actr Gr) ?! Mu n(Actr Gr) and PLr : Actr ?! PLevel, which are de ned in the third part of Table 1. The second operation is carried out through function Meltr : Mu n(Actr Gr) ?! P n(Actr Gr) and partial function Min : (ARate ARate) ?!o ARate, which are de ned in the fourth part of Table1 . Observe that function Meltr sums the rewards of the potential moves to merge: this is consistent with the additivity assumption about rewards.... In PAGE 7: ... Observe that function Meltr sums the rewards of the potential moves to merge: this is consistent with the additivity assumption about rewards. The multiset PM r(E) 2 Mu n(Actr Gr) of potential moves of E 2 Gr is de ned by structural induction in the second part of Table1 . The normalization of rates and rewards of potential moves resulting from the synchronization of an action with several independent or alternative passive actions is carried out through partial functions Normr;rate : (AType ARate ARate Mu n(Actr Gr) Mu n(Actr Gr)) ?!o ARate and Normr;reward : (AType AReward AReward Mu n(Actr Gr) Mu n(Actr Gr)) ?! o AReward, and function Split : (ARate R I ]0;1]) ?! ARate, which are de ned in the fth part of Ta- ble 1.... In PAGE 7: ... Such an equivalence was de ned according to the idea of probabilistic bisimulation [8] on the inte- grated semantic model, and we proved that it is necessary to de ne it on the integrated semantic model in order for the congruence property to hold. For the sake of convenience, we can extend EMB to EMPAr since it disregards rewards, provided that like in [1, 2] we introduce a priority operator \ ( ) quot; and we con- sider the language Lr; generated by the following syntax E ::= 0 j lt;a; ~ ; r gt;:E j E=L j E[ apos;] j (E) j E + E j E kS E j A whose semantic rules are those in Table1 except that the rule in the rst part is replaced by ( lt;a; ~ ; r gt;; E0) 2 Meltr(PM r(E)) E a;~ ;r ???! E0... ..."

Cited by 21

### Table 1. Inductive rules for EMPA integrated interleaving semantics

1998

"... In PAGE 9: ...the transitions of the corresponding state and their rates. The formal definition of the integrated interleaving semantics for EMPA is based on the transition relation ???!, which is the least subset of G Act G satisfying the inference rule reported in the first part of Table1 . This rule selects the potential moves having the highest priority level, and then merges together those having the same action type, the same priority level and the same derivative term.... In PAGE 9: ... The first operation is carried out through functions Select : Mu n(Act G) ?! Mu n(Act G) and PL : Act ?! PLevel, which are defined in the third part of Table 1. The second operation is carried out through function Melt : Mu n(Act G) ?! P n(Act G) and partial function Min : (ARate ARate) ?!o ARate, which are defined in the fourth part of Table1 . The name Min should recall the adoption of the race policy: the minimum of a set of random variables has to be computed.... In PAGE 9: ... The rationale behind the use of Melt and Min is thus the possibility of producing standard LTSs as integrated semantic models, without the need to decorate transitions with auxiliary labels like in [16] nor the need to take into account the multiplicity of transitions like in [18]. The multiset PM (E) 2 Mu n(Act G) of potential moves of E 2 G is defined by structural induction in the second part of Table1 . The normalizationof the rates of potential moves resulting from thesynchronization of the sameactive actionwith several independent or alternativepassive actions is carried out through partial function Norm : (AType ARate ARate Mu n(Act G) Mu n(Act G)) ?!o ARate and function Split : (ARate R I ]0;1]) ?! ARate, which are defined in the fifth part of Table 1.... In PAGE 9: ... The multiset PM (E) 2 Mu n(Act G) of potential moves of E 2 G is defined by structural induction in the second part of Table 1. The normalizationof the rates of potential moves resulting from thesynchronization of the sameactive actionwith several independent or alternativepassive actions is carried out through partial function Norm : (AType ARate ARate Mu n(Act G) Mu n(Act G)) ?!o ARate and function Split : (ARate R I ]0;1]) ?! ARate, which are defined in the fifth part of Table1 . Note that Norm(a; ~ 1; ~ 2; PM 1; PM2) is defined if and only if min(~ ; ~ ) = , which is the condition on action rates we have required in Section 2.... In PAGE 23: ... To solve the problem, we follow the proposal of [2] by introducing a priority operator ( ) : priority levels are taken to be potential, and they become actual only inside the scope of the priority operator. We thus consider the language L generated by the following syntax E ::= 0 j lt;a; ~ gt;:E j E=L j E[ apos;] j (E) j E + E j E kS E j A whose semantic rules are those in Table1 except that the rule in the first part is replaced by ( lt;a; ~ gt;; E0) 2 Melt(PM (E)) E a;~ ???! E0 and the following rule for the priority operator is introduced in the second part PM ( (E)) = Select(PM (E)) It is easily seen that EMPA coincides with the set of terms f (E) j E 2 Lg. Definition 5.... ..."

Cited by 74

### Table 1. Rule Set for Integrating Multiple Fault Reports

2005

"... In PAGE 4: ... Since the AHU fault would be repaired first even if both faults were reported to the operator, the methodology effectively prioritizes unrelated fault reports from equipment at different levels in the hierarchy. The rule set was generated by applying these general principles to the hierarchical subsystems in an HVAC system (see Table1 ). A software implementation combining the hierarchical deci- sion-making framework and the rule set was developed using the algorithm illustrated in Figure 2.... In PAGE 4: ... Less severe faults affecting equipment lower in the hierarchy may result in fault reports from the affected equipment that should be suppressed. Effective values for the thresholds used in Rules 8 through 17 of Table1 were deter- mined through trial and error. RESULTS AND DISCUSSION A dynamic simulation model of a three-story office building and its HVAC system was devel- oped using the HVACSIM+ simulation tool (Park et al.... ..."

### Table 1: Proposals for the general model of reasoning operators 3.1 FRMs integrating all fuzzy rules The rst group of FRMs being an alternative to the classical one is formed by those that use all red fuzzy rules for deriving conclusions from a set of fuzzy if-then rules and a pattern. This idea is represented graphically in Figure 3. R1 R2

"... In PAGE 5: ...Table1... ..."