CN107273407B - Constraint deduction-based discovery method for MOF (Metal-oxide-semiconductor) storage bank conflict operation - Google Patents

Abstract

The invention relates to a method for discovering MOF storage bank conflict operation based on constraint deduction, which is technically characterized by comprising the following steps of: defining internal activities that affect good format constraints and establishing their correspondence to MOF construction activities; simplifying good format constraints; establishing a discovery graph; marking the discovery map; obtaining internal activities; potential operations that violate the constraints are determined. The invention can deduce the constraint condition irrelevant to the given operation by deducing the internal activity which may violate the constraint condition and further deducing the potential operation which violates the constraint condition. The constraint conditions which are irrelevant to the given operation are removed from the constraint detection process after the operation is executed, so that the efficiency of the process can be obviously improved, and the method can be widely applied to the field of constraint detection.

Description

Constraint deduction-based discovery method for MOF (Metal-oxide-semiconductor) storage bank conflict operation

Technical Field

The invention belongs to the technical field of storage library systems, and particularly relates to a constraint deduction-based discovery method for MOF storage library conflict operations.

Background

Nowadays, meta-object facility MOF has become a standard for a metadata repository system that is generally accepted and adopted internationally. As an important component of the MOF repository system, good format constraints defined based on the object constraint language OCL specify the conditions that all states of the system must comply with. The contents of the repository system may be altered by the execution of the operation, and it must therefore be ensured that the state of the repository system after the execution of the operation does not violate the well-formed constraints in the meta-hierarchy.

This task however becomes quite difficult due to the following two reasons: in one aspect, the organization of metadata in a repository system presents a complex structure that is hierarchical, multi-level, and dynamically changing. Unlike database systems, the repository system introduces a layer of M3 that allows users to define a layer of M2. At run times M2, M1, and, M0 layers may all be dynamically modified; on the other hand, the MOF standard is not adequately specified to ensure good format constraints. MOFs provide several mechanisms for ensuring repository system consistency, including: first, the MOF defines a set of model constraints; secondly, the MOF defines a group of closure rules and calculation semantics for abstract mapping, and a JAVA metadata interface JMI defines calculation semantics for Jave mapping; thirdly, the MOF provides constraint model elements for describing domain rules; finally, MOFs and JMI define a set of repository interfaces. However, the above measures are aimed at ensuring the correctness of the grammar and not at restricting the good format.

The following relevant documents are found through retrieval: petrov et al (Petrov I, et al, on the notification in metadata retrieval Systems, LNCS 3520: Proc of the16th Int Confonn Advanced Information Systems engineering. Berlin: Springer,2004:90-104) discusses the concept of various consistencies in MOF-based metadata repository Systems and proposes policies and algorithms for enhancing consistency between the system state after execution of an operation and well-formed constraints. However, in this process they detect all constraints, making the detection process inefficient. In contrast, our approach can significantly improve the efficiency of the process, since only constraints that may actually be violated need to be considered in the detection process, and irrelevant constraints will be omitted. Takeshi et al (Takeshi A, et al. metadata: A wiki-based database for managing metadata of metadata analysis. front in biologics and computationbiology, 2015,15(38):1-12) also organizes metadata into different hierarchies when solving the metadata exchange problem, where a metadata hierarchy is organized not according to the semantic level of the metadata but according to the content and correlation degree of the metadata, and there is no semantic association between each hierarchy. The modification of the repository content by its operation does not affect the underlying hierarchy so that the assurance of constraints is relatively simple, but the framework is difficult to adapt to complex metadata structures that change dynamically. The Metadata frameworks proposed in the literature (Metadata management and sharing of distributed biological data. International journal of Metadata, Semantics and Ontologies,2014,9(1):42-57) also have similar problems.

To our knowledge, the problem of inferring the exact set of potentially conflicting active PEAs for a given constraint in a MOF repository system has not been studied internationally. Similar research exists in the fields of deductive databases and relational databases, and the adopted solutions mainly include converting OCL constraints into logical expressions or SQL expressions, and then designing algorithms on the basis of the logical expressions or SQL expressions to infer the exact PEA set of given constraints, such as Duboiset et al (Duboiset M, equivalent. integrating the calsulus-based approach OCL: Study of expression and code generation, Proc of the 18th Int Workshop on Database and expression systems thereof, Piscataway, NJ: IEEE,2007: 502. Ach 506), Elnet et al (Pinet F, Duboiset M, Demuth B, et al. conversion in modeling in Computing algorithm in Database, Advance modeling in analysis in concrete system, and resource C1. D. in simulation system of software, resource, and resource, demuth et al (Demuth B, Hussmann H, Loecher s. OCL as a specification language for business indexes applications, LNCS 2185: Proc of the 4th Conf on uml. berlin: Springer,2006: 104-. Although some algorithms improve this to support all mechanisms of OCL constraints, the high complexity of the processing logic again results in inefficiencies in the algorithms (regarding their limitations, see the discussion of Siva et al (Siva S, et al. structural processing in relational database systems: From the perspective to implementation, Proc of the 25th ACM Symp on Applied computing. New York: ACM,2010: 2066-.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a discovery method of MOF storage bank conflict operation based on constraint deduction, which has reasonable design, stable performance and high efficiency.

The invention solves the technical problems in the prior art by adopting the following technical scheme:

a method of discovering MOF repository conflict operations based on constraint deduction, comprising the steps of:

step 1, defining internal activities affecting good format constraints and establishing a corresponding relation between the internal activities and MOF construction activities;

step 2: simplifying good format constraints;

and step 3: establishing a discovery graph;

and 4, step 4: marking the discovery map;

and 5: obtaining internal activities;

step 6: potential operations that violate the constraints are determined.

The internal activities are eight internal activity relationships defined according to entity type and relationship type, including create instances and delete instances.

The specific implementation method of the step 2 comprises the following steps: firstly, using equivalent operations defined in an OCL standard library to reduce the number of operations appearing in an expression; the expression is then converted to an equivalent conjunctive normal form, the result of the conversion being that each constraint is represented as a conjunction of disjuncts; the resulting literal volume of the disjunct includes a forAll iterator, an arithmetic comparison, an object/set equivalence comparison, a boolean property, a not operator, an oclIsTypeOf operator, and an oclIsKindOf operator.

The step 3 is a discovery graph established according to the content-instance relationship between the good format constraint and the OCL meta-model.

The method for marking the discovery map in the step 4 comprises the following steps: the marking of each node indicates the interaction between the node and its successor, and includes four different types of marking information: "+" indicates that an increase in the value of an expression or an increase in the number of items may cause violation of a constraint; "-" indicates that a reduction in the value of an expression or a reduction in the number of items may cause violation of a constraint; "m" indicates that a change in the value of an expression or a change in the number of items may cause violation of a constraint; "nr" indicates that the node has no interaction with its successor.

The specific method of the step 5 comprises the following steps: and acquiring according to the processes of deducing and summarizing step by step from bottom to top, and deducing a PIA set of each constraint condition.

The specific method of the step 6 comprises the following steps: according to the PIA set of each constraint, converting into the construction activity of the MOF standard and obtaining the PEA set, and finally checking whether each operation has the violated constraint. .

The invention has the advantages and positive effects that:

the invention defines a group of MOF internal activities which are more accurate and flexible than the MOF construction activities and establishes the corresponding relation between the MOF internal activities and the MOF internal activities; inferring internal activities that may violate constraints; finally, potential actions that violate constraints can be inferred by comparing whether the construction actions corresponding to these internal actions appear in the operation specification. The principle is that each constraint finds a set of construction activities that may violate it, and then compares it to an operation specification, where one or more of the construction activities are associated with the operation specification, to conclude that the execution of the operation may violate the constraint. The present invention can significantly improve the efficiency of a given operation by inferring and removing constraints that are not relevant to the operation from the constraint detection process after the operation is performed. The invention can be widely applied to the field of constraint detection.

Drawings

FIG. 1 is an exemplary diagram of a hierarchy of a metadata model;

FIG. 2 is a good format constraint example diagram in the MOF repository system M1 layer;

FIG. 3 is a diagram of an example of an operation of modifying the contents of a repository system;

FIG. 4 is a discovery graph corresponding to the constraint OldTeacher;

FIG. 5 is a plot of the results after labeling of the finding corresponding to the constraint OldTeacher;

FIG. 6 is a process diagram of inferring the potentially conflicting intra-activity PIA of the constraint OldTeach.

Detailed Description

The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:

a method of discovering MOF repository conflict operations based on constraint deduction, comprising the steps of:

step 1: define internal activities that affect good format constraints and establish their correspondence to MOF fabric activities.

To infer the set of potentially conflicting construction activities PEA of good format constraints on OCL, we need to investigate which construction activities are executed that may violate the constraints. This step is therefore mainly to define internal activities and to establish their correspondence with MOF structuring activities. What is actually involved in the inference process is the internal activity. The reason why the inference is made on internal rather than structural activities is chosen is based on two reasons: firstly, our method is to realize the inference of PEA by establishing and reasoning on the discovery graph instance by virtue of the characteristic that the good format constraint is the instance of the OCL meta-model, so that it is a convenient choice to infer the activity that may affect the instance of the OCL meta-model, and on the contrary, constructing the activity is MOF model-oriented, and reasoning directly on it (although theoretically possible) brings inconvenience; secondly, there are many existing metadata repository standards such as MOF, IRDS, PCTE, etc. in the world, each defining the construction activities within its own framework. We would like to propose a general method of inferring PEA, since once the correspondence between the internal activities proposed by the present invention and the construction activities of other repository frameworks is established, the method of the present invention can be adapted to other repository frameworks. This will greatly improve the applicability of our proposed method.

The following is an analysis of the OCL meta-model element types and properties. Constraint meta-model elements include entity type and relationship type categories on which two activities, building and deleting instances, respectively, may cause violation of the constraint. In addition, generalization and specialization of entity types and modification of instance properties and activities at the associated end can lead to violation of constraints, so there are 8 internal activities. Table 1 lists these internal activities and their correspondence to MOF building activities. Although there is no one-to-one correspondence between internal activities and the fabric activities of MOFs, they are more convenient for inference of PEA than MOF fabric activities and can be converted to fabric activities by the simple mapping of table 2. For example, if it is inferred that the internal activity assignobjection above an instance may violate a constraint, and the instance is neither an association class nor adjusted by other instances, it is converted into a construct activity createobject action above the instance, and if the assignobjection is caused by a change in the association end, the construct activity addstructurelfeatureaction above the instance is added. Other internal activity transitions are similar and will not be described further herein.

TABLE 1 internal Activity and its correspondence to MOF architectural Activity

Step 2: simplifying fine-format constraints

The OCL expression defining good format constraints is reduced equivalently. The number of operations appearing in the expression is first reduced using the equivalent operations defined in the OCL standard library, followed by conversion of the expression to an equivalent conjunctive normal form. The result of the transformation is that each constraint is represented as a conjunction of disjuncts. The resulting literal volume of the disjunct includes the forAll iterators, arithmetic comparisons, object/set equivalence comparisons, boolean attributes, not operators, oclIsTypeOf operators, and oclIsKindOf operators, among others. Obviously, to satisfy the constraint, the repository system must satisfy each disjunct. A disjunct being satisfied requires at least one of its literal sizes to be satisfied.

After the reduction of the number of operations for the 4 constraints in FIG. 2, the constraints OldTeach and UniqueName become (the other constraints remain unchanged):

context School inv OldTeacher:

self.teacher->select(t|t.age>60)->size()>0

context Teacher inv UniqueName:

Teacher.allInstances()->forAll(t₁,t₂|not t₁＝t₂implies not t₁.name＝t₂.name)

after further conversion to conjunctive normal form, the constraint UniqueName becomes (other constraints remain unchanged):

context Teacher inv UniqueName:

Teacher.allInstances()->forAll(t₁,t₂|t₁＝t₂or not t₁.name＝t₂.name)

and step 3: and establishing a discovery graph according to the content-instance relation between the good format constraint and the OCL meta-model.

In this step, each node of the graph is found as an instance of a meta-class in the OCL meta-model, representing an atomic subset of the OCL expression. The1 st successor of a node is the portion of the OCL expression preceding the node. If the node represents a 2-way operation (e.g., ">", "+", etc.), then the 2 nd successor of the node is a parameter of the operation; if the node represents a loop expression (e.g., fork, select, etc.), then the 2 nd successor of the node is the body of the iterator.

Fig. 4 gives a finding graph built from the constraint OldTeacher (self. teacher- > select (t | t.age >60) - > size () > 0). An example of the meta class OperationCallExp corresponding to the operation ">" is the initial node, whose 1 st successor is the expression (self. teacher- > select (t | t.age >60) - > size ()) preceding ">" and the next 1 successor is the parameter of the operation (integer constant 0). The1 st successor node of the initial node is the operation size, which has only 1 child select. The1 st successor of node select is the association end teacher and the 2 nd successor is the operation ">" between attribute age and integer constant 60.

And 4, step 4: tagging discovery graphs

To infer a PEA set for a given constraint, it is not sufficient to consider each part of the OCL expression alone. For example, to infer whether the constraint OldTeacher may be violated by the 2 activities of assigning teachers to schools or releasing teaching relationships, one cannot consider only the sub-expression self. In practice, all 2 activities may change the value of the sub-expression, however, since the size operation is followed by a ">" operation, only a release of the pedagogical relationship may lead to a violation of the constraint, whereas if the size is followed by a "< ═ operation, only an assignment of a classroom to a school may lead to a violation of the constraint. The above analysis shows that the set of PEAs for a given constraint can only be correctly inferred by comprehensively considering the context information of each OCL atom subset, and therefore needs to be labeled with the context information of each node in the discovery graph.

The marking for each node indicates the information of the interaction between the node and its successor. Based on the overall analysis of various structures of OCL, we conclude about 4 different types of labeled information: 1) "+" indicates that an increase in the value of an expression or an increase in the number of items may cause violation of a constraint; 2) "-" indicates that a reduction in the value of an expression or a reduction in the number of items may cause violation of a constraint; 3) "m" indicates that a change in the value of an expression or a change in the number of items may cause violation of a constraint; 4) "nr" indicates that the node has no interaction with its successor.

Each of these labels is exemplified below. For "+" and "-", considering the ">" operation, there are 2 cases to violate constraint A > B: the decrease in the value of a or the increase in the value of B, so that the1 st successor of node ">" should be marked with "-", and the 2 nd successor with "+". For the label "m", considering the select operation, not only adding or deleting objects to or from the candidate set may affect the result of select, but even replacing some objects of the candidate set by an equal amount may affect the result of select, so that the label should be "m" on the successor node of the node select. Regarding the label "nr", considering and operation, since the activity violating a and B is the same as the activity violating a and B alone, i.e. there is no interaction between node and its successors, the successors of node and above should all be labeled "nr".

The process of tagging the discovery graph requires a breadth first traversal of the discovery graph since it requires first considering the tagging information on the current node and the type of node to determine the tagging information on its successor nodes table 2 summarizes the tagging methods for the various node types table 2 if there are more than 1 tag symbol on a certain node in the cell of table 2, each tag should be applied separately to determine the tag on its successor node the content "×" of the cell represents a constraint that there is no such combination.

TABLE 2 summary of labeled information for discovery graphs

The results of labeling the finding map corresponding to the constraint OldTeacher are shown in FIG. 5. In addition, information for marking the cells of Table 2 used by the node is added to each node, wherein (X, Y) represents the cells of the X row and the Y column.

The process of marking starts first from the initial node, since ">" has no predecessor, so no mark is needed on it "-" according to cell (2, 1), the1 st successor of the initial node should be marked as "-" and the 2 nd successor should be marked as "+". then according to cell (11, 3), we mark the successor of node size (node select) as "-". according to cell (10, 3), the mark on the1 st successor of node select is "-m" and the mark on the 2 nd successor is "nr". referring back to cells (9, 3) and (9, 4), the successor of node teacher self should be marked as "-m". The labels of the other parts are analogized and limited to the space, and are not repeated herein.

And 5: obtaining internal activity

After the discovery graph is labeled well, the next step can infer a potentially conflicting intra-activity PIA for a given constraint from that. Since the labeled graph has explicitly labeled the type of OCL atomic subset expression and how the expression changes can violate the overall constraint, the process of inferring the PIA is a process of inferring and summarizing from bottom to top, i.e., the inverse process of breadth-first traversal of the graph.

Table 3 summarizes sets of PIAs corresponding to different combinations of node types and marker information. c1+ c2 indicates that the PIA set for this node is the union of the PIA sets for its successor nodes. c1 applies to the case where the node has only 1 successor. An empty cell means that the node does not affect the inference summary of the PIA. C is to be specifically mentioned₁+ X and opp (X). c. C₁+ X denotes that the node adds an active X to the PIA set. For example, if the associated end is marked "+", the establishment of its dependent associated link instance may result in violation of the constraint, so the node should add AssignLink activity, i.e., c, to the PIA collection₁+ AssignLink. opp (x) indicates that the node's PIA is the opposite activity of the PIA returned by its successor, e.g., not operation. The opposite activities of the internal activities are as follows: opp (assist object) corresponds to RomoveObject, opp (modify property), opp (RomoveObject) corresponds to asmovelink, opp (RemoveLink) corresponds to asigned link, and opp (modify end) corresponds to modifeend.

TABLE 3 PIA set corresponding to node type and tag information

Table 3 was applied to the constraint OldTeacher and the results are shown in FIG. 6. Node self is processed first. Since the labels above the node self are "-" and "m", the node is known not to add any PIA according to the cells (17, 3), (17, 4) of table 3. The associated end, Teacher, is then processed with reference to cells (9, 3), (9, 4) to add the activity assignObject (school), RemoveLink (TeachesIn), ModifyEnd (TeachesIn-Teacher) to the set of PIAs. The 2 nd successor of processing node select (t.age >60), which adds the active modifyproperty (age). After this node select is processed, referring to cell (10, 3), the PIA set of node select is the union of the PIA sets of its successors. Node size, integer constant node 0, are then processed and end with initial node ">", none of which adds any activity. The set of PIAs eventually returned by the initial node ">" is the set of PIAs for the entire constraint expression. From this step, we can see that there are ModifyProperty (age), ModifyEnd (Teaches In-Teacher), RemoveLink (Teaches In), AssignObject (School) which may violate the constraint OldTeacher internal activities.

Step 6: determining potential operations violating constraints

After deducing the set of PIA for each constraint according to step 5, it is necessary to convert it into a set of corresponding PEA and check if they are present in the operating specification. Taking OldTeacher as an example, ModifyEnd (Teachhei-Teacher) directly translates to AddStructoralFeatureAction above the associated end TEACHER, ModifyProperty (age) directly translates to AddStructoralFeatureAction above the attribute age, RemoveLink (Teachhei) translates to DestroyLinkAction above the associated TEAchhei, and for AssigObject (School), since School is neither adjusted by an associated class nor by other instances, and AssigObject is not caused by changes in the associated end, it only needs to translate to CreateObjectAction above School.

We therefore obtained the following results:

(1) set of PIA for each constraint:

①OldTeacher—ModifyProperty(age)，ModifyEnd(TeachesIn-Teacher)，RemoveLink(TeachesIn)，AssignObject(School)；

②NotHeadmasterParttimeteacher—SubReclassify(Parttimeteacher)，AssignLink(Manages)，ModifyEnd(Manages-Headmaster)；

③UniqueName—AssignObject(Teacher)，ModifyProperty(Name-Teacher)；

④ValidWorkinghours—AssignObject(Parttimeteacher)，ModifyProperty(Workinghours)；

(2) conversion to the construction activities of the MOF standard, the resulting PEA set:

① OldTeacher-AddStreuctFeatureAction above Attribute, AddStreuctFeatureAction above Association terminal TEACHER, DestroyLinkAction above Association TEACHEsIn, CreateObjectAction above School;

② NotHeadmaster Parttimetecacher-modifies the class element to which the full-time teacher belongs into ReclassifiyObjectAction of Parttimetecacher, CreateLinkAction above managers, AddStrectturalFeatureAction above the associated end headmaster;

③ CreateObjectAction over UniqueName-Teacher, CreateObjectAction over Parttimetecher, AddStreuctFeatureAction over attribute name;

④ ValidWorkinghorn-CreateObjectAction on Parttimeeater, AddStreuctFeatureAction on attribute workkinghorn;

(3) constraints that each operation may violate:

① EmployParttimieThecher may violate ValidWorkinghours and UniqueName, while the other 2 constraints will not violate;

② DismissTeacher is only possible in violation of OldTeacher;

③ DeleteAssociation may only violate OldTeacher.

With the knowledge inferred above, it is not necessary to detect all 4 constraints after each operation is performed, only 2 constraints after the employee partner is performed and only 1 constraint after the disissteacher and DeleteAssociation are performed. It can be seen that by narrowing the set of constraints to be detected, our method can significantly improve the efficiency of good-format constraint detection.

In order to evaluate the effectiveness of the present invention, the metadata model processed by the present invention is exemplified, which includes a model structure of metadata, 4 good format constraints of OCL at M1 level, and 3 operations for modifying the contents of the repository system. For simplicity, we ignore constraints in other layers. The model structure is shown in fig. 1. The M1-layer metadata, which is an example of the M2-layer metadata, describes information of schools and teachers. The teachers are divided into full-time teachers and part-time teachers. The 4 constraints in the M1 layer are shown in fig. 2, and are used to ensure that: each school has at least 1 teacher over 60 years old (OldTeacher constraint); the captain cannot be a part-time teacher (notheadmaster parttimetecher constraint); the names of 2 teachers cannot be the same (UniqueName constraint); the work-week time of the part-time teacher must be between 10h and 40h (ValidWorkinghours constraint). 3 operations are shown in figure 3. After each specific activity we add an equivalent MOF construction activity. The employee employpartimetactor was operated to hire 1 new part-time teacher for a given school. It builds 1 new object instance of the class element Parttimeteecher, performs initialization assignment on the object instance and associates the object instance with the school. Operate the disissteacher to free 1 teacher (full or part) and delete its association with the school. And operating the deleteAssociation to delete 1 association, and simultaneously deleting the association end, the relationship between the association end and the relationship between the association end and the class. Employpartimeter and disissteacher only modify content in the M0 layer, while DeleteAssociation modifies content in the M1 layer while propagating changes to the M0 layer, i.e., content in the M0 layer will also be modified. (since changes to content in a high level must propagate to all levels below it, e.g., deleting 1 class while all instances of that class in all levels below must be deleted.)

By applying the method, we can conclude that EmployParttimedeacher can only violate the constraints ValidWorkinghours and UniqueName, and the other two can not violate, while DismissTeach and DeleteAssociation can only violate the constraint OldTeach, and the other three can not violate. Therefore, after each operation is executed, all 4 constraint conditions do not need to be detected, after the EmployParttimieSeacher is executed, only 2 constraints need to be detected, and after the DismissTeacher and the DeleteAssociation are executed, only 1 constraint needs to be detected. It can be seen that our method can greatly improve the efficiency of good format constraint detection.

It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.

Claims (6)

1. A method for discovering MOF repository conflict operations based on constraint deduction, comprising the steps of:

step 1, defining internal activities affecting good format constraints and establishing a corresponding relation between the internal activities and MOF construction activities;

step 2: simplifying good format constraints;

and step 3: establishing a discovery graph;

and 4, step 4: marking the discovery map;

and 5: obtaining internal activities;

step 6: determining potential operations that violate a constraint;

the specific implementation method of the step 2 comprises the following steps: firstly, using equivalent operations defined in an OCL standard library to reduce the number of operations appearing in an expression; the expression is then converted to an equivalent conjunctive normal form, the result of the conversion being that each constraint is represented as a conjunction of disjuncts; the resulting literal volume of the disjunct includes a forAll iterator, an arithmetic comparison, an object/set equivalence comparison, a boolean property, a not operator, an oclIsTypeOf operator, and an oclIsKindOf operator.

2. The method of discovery of MOF repository conflict operations based on constraint deduction of claim 1, wherein: the internal activities are eight internal activity relationships defined according to entity type and relationship type, including create instances and delete instances.

3. The method of discovery of MOF repository conflict operations based on constraint deduction of claim 1, wherein: the step 3 is a discovery graph established according to the content-instance relationship between the good format constraint and the OCL meta-model.

4. The method of discovery of MOF repository conflict operations based on constraint deduction of claim 1, wherein: the method for marking the discovery map in the step 4 comprises the following steps: the marking of each node indicates the interaction between the node and its successor, and includes four different types of marking information: "+" indicates that an increase in the value of an expression or an increase in the number of items may cause violation of a constraint; "-" indicates that a reduction in the value of an expression or a reduction in the number of items may cause violation of a constraint; "m" indicates that a change in the value of an expression or a change in the number of items may cause violation of a constraint; "nr" indicates that the node has no interaction with its successor.

5. The method of discovery of MOF repository conflict operations based on constraint deduction of claim 1, wherein: the specific method of the step 5 comprises the following steps: and acquiring according to the processes of deducing and summarizing step by step from bottom to top, and deducing a PIA set of each constraint condition.

6. The method of discovery of MOF repository conflict operations based on constraint deduction of claim 1, wherein: the specific method of the step 6 comprises the following steps: according to the PIA set of each constraint, converting into the construction activity of the MOF standard and obtaining the PEA set, and finally checking whether each operation has the violated constraint.

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