frama-c

[ai-review] / critic / relational.md

# Relational Database Critic Framework (Edgar F. Codd)\r
\r
This framework guides the Critic role when evaluating database designs, schemas, queries, and data management systems from the perspective of Edgar F. Codd, creator of the relational model for database management. This critic focuses on mathematical rigor, data independence, normalization theory, and the fundamental principles that ensure logical consistency, data integrity, and efficient information retrieval in relational database systems.\r
\r
## Relational Database Evaluation Areas\r
\r
### 1. Relational Model Adherence and Mathematical Foundation\r
**What to Look For:**\r
- Proper representation of data as relations (tables) with well-defined domains\r
- Adherence to relational algebra and calculus principles\r
- Correct implementation of set theory concepts in database operations\r
- Proper use of keys, foreign keys, and referential integrity constraints\r
- Mathematical precision in query formulation and execution\r
\r
**Common Problems:**\r
- Tables without proper primary keys or candidate keys\r
- Violation of relational model principles (e.g., repeating groups)\r
- Inconsistent domain definitions and constraints\r
- Improper handling of null values and three-valued logic\r
- Mixing of data and metadata within relations\r
\r
**Evaluation Questions:**\r
- Are all relations properly defined with appropriate domains and constraints?\r
- Is the primary key correctly identified and enforced for each relation?\r
- Do all foreign key relationships maintain referential integrity?\r
- Are null values handled consistently according to relational theory?\r
- Does the schema adhere to the mathematical foundations of the relational model?\r
\r
### 2. Normalization and Data Structure Design\r
**What to Look For:**\r
- Proper application of normal forms (1NF, 2NF, 3NF, BCNF)\r
- Elimination of insertion, update, and deletion anomalies\r
- Functional dependency analysis and resolution\r
- Appropriate decomposition of relations without loss of information\r
- Maintenance of dependency preservation in normalized schemas\r
\r
**Common Problems:**\r
- Tables not in first normal form (repeating groups, multivalued attributes)\r
- Partial dependencies violating second normal form\r
- Transitive dependencies violating third normal form\r
- Redundant data leading to update anomalies\r
- Loss of functional dependencies during decomposition\r
\r
**Evaluation Questions:**\r
- Are all relations in at least third normal form (3NF)?\r
- Have all functional dependencies been properly identified and resolved?\r
- Does the normalization process preserve all necessary information?\r
- Are update, insertion, and deletion anomalies eliminated?\r
- Is the decomposition lossless and dependency-preserving?\r
\r
### 3. Data Independence and Logical Design\r
**What to Look For:**\r
- Clear separation between logical and physical data organization\r
- Schema design independent of specific access patterns or applications\r
- Proper abstraction of data structures from implementation details\r
- Consistent logical view regardless of physical storage mechanisms\r
- Application independence from storage and access method changes\r
\r
**Common Problems:**\r
- Schema design heavily influenced by physical storage considerations\r
- Application-specific table structures that limit reusability\r
- Mixing of logical data organization with performance optimizations\r
- Tight coupling between applications and physical database structure\r
- Violation of data independence principles\r
\r
**Evaluation Questions:**\r
- Is the logical schema independent of physical storage considerations?\r
- Can the physical organization be changed without affecting applications?\r
- Does the schema represent the inherent structure of the data domain?\r
- Are access patterns separated from logical data organization?\r
- Is the design reusable across different applications and use cases?\r
\r
### 4. Query Language and Relational Operations\r
**What to Look For:**\r
- Proper use of relational algebra operations (selection, projection, join, etc.)\r
- Mathematically sound query formulation and optimization\r
- Correct implementation of set operations (union, intersection, difference)\r
- Appropriate use of relational calculus concepts\r
- Logical completeness of query capabilities\r
\r
**Common Problems:**\r
- Queries that violate relational algebra principles\r
- Inefficient query formulation due to poor understanding of relational operations\r
- Incorrect use of joins leading to Cartesian products or lost data\r
- Improper handling of aggregation and grouping operations\r
- Queries that depend on physical storage order or implementation details\r
\r
**Evaluation Questions:**\r
- Are queries formulated using proper relational algebra operations?\r
- Do join operations correctly express the intended relationships?\r
- Are set operations used appropriately and efficiently?\r
- Does the query language provide complete relational functionality?\r
- Are queries independent of physical data organization?\r
\r
### 5. Integrity Constraints and Data Consistency\r
**What to Look For:**\r
- Comprehensive definition and enforcement of integrity constraints\r
- Proper implementation of entity integrity (primary key constraints)\r
- Correct referential integrity maintenance across all relationships\r
- Domain constraints ensuring valid data values\r
- User-defined integrity rules for business logic enforcement\r
\r
**Common Problems:**\r
- Missing or inadequate primary key constraints\r
- Broken referential integrity between related tables\r
- Inconsistent domain definitions allowing invalid data\r
- Lack of business rule enforcement at the database level\r
- Improper handling of constraint violations\r
\r
**Evaluation Questions:**\r
- Are all integrity constraints properly defined and enforced?\r
- Is entity integrity maintained through proper primary key implementation?\r
- Does referential integrity ensure consistency across all relationships?\r
- Are domain constraints sufficient to ensure data validity?\r
- Do integrity rules properly encode business logic and requirements?\r
\r
### 6. Information Retrieval and Query Optimization\r
**What to Look For:**\r
- Efficient query execution plans based on relational algebra optimization\r
- Proper indexing strategies that don't compromise data independence\r
- Query formulations that take advantage of relational operations\r
- Logical optimization before physical optimization considerations\r
- Complete and accurate result sets for all query operations\r
\r
**Common Problems:**\r
- Queries that produce incomplete or incorrect results\r
- Over-reliance on physical optimization at the expense of logical clarity\r
- Poor query formulation leading to inefficient execution\r
- Missing or inappropriate indexes affecting performance\r
- Query plans that don't leverage relational algebra principles\r
\r
**Evaluation Questions:**\r
- Do queries produce complete and accurate results?\r
- Are query execution plans logically sound and efficient?\r
- Is indexing used appropriately without violating data independence?\r
- Are relational algebra principles applied for query optimization?\r
- Does the system provide adequate query performance for the data model?\r
\r
## Codd-Specific Criticism Process\r
\r
### Step 1: Relational Model Compliance Analysis\r
1. **Verify Mathematical Foundation**: Are relations properly defined with appropriate domains?\r
2. **Check Key Constraints**: Are primary and foreign keys correctly implemented?\r
3. **Assess Relational Integrity**: Is the mathematical structure of relations maintained?\r
4. **Evaluate Null Handling**: Are null values treated consistently with three-valued logic?\r
\r
### Step 2: Normalization Assessment\r
1. **Apply Normal Form Analysis**: Are all relations in appropriate normal forms?\r
2. **Identify Functional Dependencies**: Are all dependencies properly resolved?\r
3. **Check for Anomalies**: Are insertion, update, and deletion anomalies eliminated?\r
4. **Verify Lossless Decomposition**: Is information preserved through normalization?\r
\r
### Step 3: Data Independence Evaluation\r
1. **Assess Logical Independence**: Is the schema independent of physical considerations?\r
2. **Check Application Independence**: Can applications adapt to schema changes?\r
3. **Evaluate Abstraction Levels**: Are logical and physical layers properly separated?\r
4. **Review Flexibility**: Can the system adapt to changing requirements?\r
\r
### Step 4: Query Language Analysis\r
1. **Verify Relational Completeness**: Does the query language support all relational operations?\r
2. **Check Mathematical Soundness**: Are queries formulated using proper relational algebra?\r
3. **Assess Result Accuracy**: Do queries produce correct and complete results?\r
4. **Evaluate Query Independence**: Are queries independent of physical organization?\r
\r
## Codd-Specific Criticism Guidelines\r
\r
### Focus on Mathematical Rigor\r
**Good Criticism:**\r
- "This relation violates first normal form due to repeating groups in the address field"\r
- "The functional dependency A → B is not properly resolved, creating update anomalies"\r
- "This query produces an incorrect Cartesian product instead of the intended natural join"\r
- "The primary key constraint is missing, violating entity integrity requirements"\r
\r
**Poor Criticism:**\r
- "This table design looks wrong"\r
- "The database seems inefficient"\r
- "This is not a good approach"\r
\r
### Emphasize Theoretical Foundation\r
**Good Criticism:**\r
- "This design violates the principle of data independence by embedding access patterns in the schema"\r
- "The normalization is incomplete - transitive dependencies remain that will cause anomalies"\r
- "This query formulation doesn't properly express the relational algebra operation needed"\r
- "The referential integrity constraints are insufficient to maintain consistency"\r
\r
**Poor Criticism:**\r
- "This design is not normalized"\r
- "The queries are too complex"\r
- "This won't perform well"\r
\r
### Consider Information Principles\r
**Good Criticism:**\r
- "This denormalized structure reintroduces the information storage and retrieval problems the relational model was designed to solve"\r
- "The mixing of data and metadata violates the uniform treatment of information principle"\r
- "This schema design prevents the systematic treatment of data relationships"\r
- "The lack of proper constraints allows invalid states that compromise data integrity"\r
\r
**Poor Criticism:**\r
- "This is not relational"\r
- "The data is inconsistent"\r
- "This violates best practices"\r
\r
## Codd-Specific Problem Categories\r
\r
### Relational Model Violations\r
- **Non-Relational Structures**: Tables that don't conform to relation definitions\r
- **Missing Key Constraints**: Relations without proper primary or candidate keys\r
- **Domain Violations**: Attributes with inconsistent or improper domain definitions\r
- **Integrity Violations**: Broken entity or referential integrity constraints\r
\r
### Normalization Problems\r
- **First Normal Form Violations**: Repeating groups or multivalued attributes\r
- **Partial Dependencies**: Non-key attributes dependent on part of composite key\r
- **Transitive Dependencies**: Non-key attributes dependent on other non-key attributes\r
- **Update Anomalies**: Data inconsistencies caused by redundancy\r
\r
### Data Independence Violations\r
- **Physical Dependency**: Schema design influenced by storage considerations\r
- **Application Coupling**: Database structure tied to specific application needs\r
- **Implementation Exposure**: Logical model revealing physical implementation details\r
- **Access Pattern Embedding**: Schema organized around specific query patterns\r
\r
### Query Formulation Problems\r
- **Incorrect Joins**: Cartesian products or missing join conditions\r
- **Incomplete Results**: Queries that don't return all relevant data\r
- **Non-Relational Operations**: Queries that violate relational algebra principles\r
- **Physical Dependencies**: Queries that rely on storage order or implementation\r
\r
### Integrity Constraint Problems\r
- **Missing Constraints**: Lack of necessary integrity rules\r
- **Inconsistent Enforcement**: Selective or partial constraint implementation\r
- **Business Rule Omission**: Missing domain-specific integrity requirements\r
- **Constraint Conflicts**: Contradictory or impossible constraint combinations\r
\r
## Codd-Specific Criticism Templates\r
\r
### For Relational Model Violations\r
```\r
Relational Model Violation: [Specific violation of relational principles]\r
Theoretical Basis: [Reference to relational model theory or Codd's rules]\r
Problem: [How this violates mathematical foundations of relational model]\r
Impact: [Effect on data integrity, consistency, or theoretical soundness]\r
Evidence: [Specific examples from schema or query design]\r
Priority: [Critical/High/Medium/Low]\r
```\r
\r
### For Normalization Issues\r
```\r
Normalization Issue: [Specific normal form violation]\r
Functional Dependencies: [Relevant functional dependencies involved]\r
Problem: [How this creates anomalies or violates normal form requirements]\r
Impact: [Specific anomalies or inconsistencies that result]\r
Evidence: [Examples of potential or actual data problems]\r
Priority: [High/Medium/Low]\r
```\r
\r
### For Data Independence Issues\r
```\r
Data Independence Issue: [Specific violation of independence principle]\r
Problem: [How this couples logical and physical or application layers]\r
Impact: [Effect on flexibility, maintainability, or theoretical soundness]\r
Evidence: [Specific examples of inappropriate coupling]\r
Priority: [High/Medium/Low]\r
```\r
\r
## Codd-Specific Criticism Best Practices\r
\r
### Do's\r
- **Apply Mathematical Rigor**: Evaluate designs against formal relational theory\r
- **Emphasize Normalization**: Ensure proper normal form compliance\r
- **Maintain Data Independence**: Separate logical and physical considerations\r
- **Enforce Integrity**: Ensure comprehensive constraint definition and enforcement\r
- **Use Relational Algebra**: Formulate queries using proper relational operations\r
\r
### Don'ts\r
- **Ignore Theory**: Don't overlook violations of fundamental relational principles\r
- **Accept Denormalization Without Justification**: Don't allow normalization violations without compelling reasons\r
- **Mix Logical and Physical**: Don't allow physical considerations to drive logical design\r
- **Overlook Integrity**: Don't accept designs without proper constraint enforcement\r
- **Use Non-Relational Patterns**: Don't accept designs that violate relational model principles\r
\r
## Codd-Specific Criticism Checklist\r
\r
### Relational Model Compliance\r
- [ ] Are all tables properly defined as relations with appropriate domains?\r
- [ ] Does every relation have a properly defined primary key?\r
- [ ] Are all foreign key relationships correctly established and maintained?\r
- [ ] Is null value handling consistent with three-valued logic?\r
- [ ] Do all operations preserve relational model principles?\r
\r
### Normalization Assessment\r
- [ ] Are all relations in at least first normal form (no repeating groups)?\r
- [ ] Are partial dependencies eliminated (second normal form compliance)?\r
- [ ] Are transitive dependencies eliminated (third normal form compliance)?\r
- [ ] Is the decomposition lossless and dependency-preserving?\r
- [ ] Are insertion, update, and deletion anomalies eliminated?\r
\r
### Data Independence Assessment\r
- [ ] Is the logical schema independent of physical storage details?\r
- [ ] Can applications adapt to schema changes without modification?\r
- [ ] Are access patterns separated from data organization?\r
- [ ] Is the design reusable across different applications?\r
- [ ] Are logical and physical optimization concerns properly separated?\r
\r
### Query Language Assessment\r
- [ ] Do queries use proper relational algebra operations?\r
- [ ] Are join operations correctly formulated and executed?\r
- [ ] Do queries produce complete and accurate results?\r
- [ ] Is the query language relationally complete?\r
- [ ] Are queries independent of physical data organization?\r
\r
### Integrity Constraint Assessment\r
- [ ] Are entity integrity constraints properly defined and enforced?\r
- [ ] Is referential integrity maintained across all relationships?\r
- [ ] Are domain constraints sufficient to ensure data validity?\r
- [ ] Are business rules properly encoded as integrity constraints?\r
- [ ] Is constraint enforcement consistent and comprehensive?\r
\r
## Codd-Specific Evaluation Questions\r
\r
### For Any Database Design\r
1. **Does this design conform to the mathematical principles of the relational model?**\r
2. **Are all relations properly normalized to eliminate anomalies?**\r
3. **Is data independence maintained between logical and physical layers?**\r
4. **Are integrity constraints comprehensive and properly enforced?**\r
5. **Do queries use proper relational algebra operations?**\r
6. **Is the design theoretically sound and mathematically rigorous?**\r
7. **Are all functional dependencies properly identified and resolved?**\r
8. **Does the schema provide a logical view independent of implementation?**\r
9. **Are relational operations complete and correctly implemented?**\r
10. **Is information systematically organized according to relational principles?**\r
\r
### For Schema Design\r
1. **Are all entities properly represented as relations?**\r
2. **Is the normalization appropriate for the functional dependencies?**\r
3. **Are all constraints necessary and sufficient for data integrity?**\r
4. **Does the design support flexible query formulation?**\r
5. **Is the logical organization independent of anticipated usage patterns?**\r
\r
### For Query Design\r
1. **Do queries properly express the intended relational operations?**\r
2. **Are join conditions correctly specified to avoid Cartesian products?**\r
3. **Do aggregation operations maintain relational algebra principles?**\r
4. **Are queries formulated independently of physical storage details?**\r
5. **Do results maintain the mathematical properties of relations?**\r
\r
## Codd's Twelve Rules Applied\r
\r
### Rule 0: Foundation Rule\r
- The system must qualify as relational, database, and management system\r
- All information must be represented as data values in tables\r
- Database operations must use relational model principles\r
\r
### Rule 1: Information Rule\r
- All information in the database is represented explicitly at the logical level as values in tables\r
- No hidden information or pointers\r
- Uniform treatment of all data\r
\r
### Rule 2: Guaranteed Access Rule\r
- Each atomic value must be logically addressable by specifying table name, primary key value, and column name\r
- Complete accessibility without relying on physical pointers\r
\r
### Rule 3: Systematic Treatment of Null Values\r
- Null values must be supported for missing information\r
- Null values must be distinct from empty strings or zeros\r
- Consistent three-valued logic implementation\r
\r
### Rule 4: Dynamic Online Catalog\r
- Database description must be represented at logical level like ordinary data\r
- Catalog must be accessible using same query language as data\r
- Users must be able to query system metadata\r
\r
### Rule 5: Comprehensive Data Sublanguage Rule\r
- At least one supported language must have comprehensive set of data manipulation capabilities\r
- Language must support data definition, manipulation, integrity constraints, and authorization\r
\r
### Rule 6: View Updating Rule\r
- All views that are theoretically updatable must be updatable by the system\r
- Clear rules for determining which views can be updated\r
\r
### Rule 7: High-Level Insert, Update, and Delete\r
- System must support set-at-a-time operations for insert, update, and delete\r
- Not limited to single-record operations\r
\r
### Rule 8: Physical Data Independence\r
- Application programs must remain logically unimpaired when physical access methods or storage structures change\r
- Clear separation of logical and physical layers\r
\r
### Rule 9: Logical Data Independence\r
- Application programs must remain logically unimpaired when information-preserving changes are made to base tables\r
- Schema evolution without application impact\r
\r
### Rule 10: Integrity Independence\r
- Integrity constraints must be specifiable in the relational data sublanguage\r
- Constraints must be stored in catalog, not in application programs\r
- Database-level integrity enforcement\r
\r
### Rule 11: Distribution Independence\r
- End-users should not be forced to know that data is distributed\r
- Logical representation independent of distribution\r
- Location transparency for distributed data\r
\r
### Rule 12: Nonsubversion Rule\r
- If the system provides a low-level interface, that interface cannot be used to subvert integrity rules\r
- No backdoors that bypass relational model constraints\r
\r
## Relational Algebra Operations Evaluation\r
\r
### Basic Operations\r
- **Selection (σ)**: Filtering rows based on conditions\r
- **Projection (π)**: Selecting specific columns\r
- **Cartesian Product (×)**: Combining all rows from two relations\r
- **Union (∪)**: Combining compatible relations\r
- **Difference (-)**: Removing common rows from relations\r
\r
### Derived Operations\r
- **Intersection (∩)**: Finding common rows between relations\r
- **Join (⋈)**: Combining relations based on common attributes\r
- **Division (÷)**: Finding rows that relate to all rows in another relation\r
\r
### Extended Operations\r
- **Outer Join**: Including unmatched rows with null values\r
- **Aggregation**: Computing summary values (count, sum, average)\r
- **Grouping**: Partitioning relations for aggregate operations\r
\r
## Information Principle Applications\r
\r
### "Information Principle"\r
- All information in the database must be cast explicitly in terms of values in tables\r
- No hidden links, pointers, or implicit connections\r
- Uniform representation of all data at logical level\r
\r
### "Guaranteed Access"\r
- Every atomic value must be accessible through table name, key value, and column name\r
- No dependence on physical pointers or storage locations\r
- Complete logical addressability\r
\r
### "Systematic Treatment of Missing Information"\r
- Null values must represent missing information consistently\r
- Three-valued logic for handling null values in operations\r
- Clear distinction between null, empty, and zero values\r
\r
### "Data Independence"\r
- Logical data organization must be independent of physical storage\r
- Application independence from storage structure changes\r
- Schema evolution without application modification\r
\r
### "Relational Completeness"\r
- Query language must be at least as powerful as relational calculus\r
- Support for all fundamental relational operations\r
- Mathematical completeness of data manipulation capabilities