In fact, integrity is an assertion expressed at the conceptual level Conceptual Model Data and then implemented at the physical level. This is called semantic rules commonly known: integrity constraints. An integrity constraint is the assurance of the data concordance with the system that database represents. A database is semantically integrated when the constraints of integrity is respected by the entire database. Several classifications of integrity constraints exist in the literature, these classifications depend on the classification criteria used [14].
The inclusion constraint that determines that an association is included in another association, that said, the occurrences of the identifier of an association exists as occurrence of the dependent association. The equality constraint which reflects a mutual inclusion between occurrences of the concerned associations.
Indeed, two associations are equal if the combinations of the primary key occurrences are identical in both associations. The exclusivity constraint: for two associations which constrained by the exclusivity, any occurrence of identifier present in an association cannot be present in the other. The generalization constraint which results from the presence of a master entity that contains the common properties factorization properties and child entities that contain their own properties.
In general, it allow to indicate a restriction or to provide complementary information about a model. It specifies the role and scope of the conceptual element to extend or clarify its semantics. A constraint is always attached to a conceptual element that constitute the context of the constraint.
Several types of constraints can be specified, we quote: The constraints of type that concern the type of properties, for each property, the set of values that can be attributed depends of a specific kind, The structural constraints: these are properties of a class and the different types of relations between classes Generalization, Aggregation, Composition, dependence and association Visibility is a mechanism of collecting the data and methods into a structure by concealing the location of bodies public, private, protected Generation of Conceptual Model from Relational Schema 2.
Description of the generation's rules Rule 1: generation of classes: Every relation of physical schema whose primary key contains no foreign key generate a class in the conceptual model.
Rule 2: generation of properties: Every attribute of the relation becomes a property in the class generated from the relation in question. Rule 3: generation of Associations: Generalization constraint: Every relation whose primary key is both foreign key will be a generalization link SubOf between the two classes.
Many to many associations : Every relation whose primary key is made entirely of foreign keys that reference multiple primary keys, generates a class association class whose properties are possibly the properties of the relation which not constitute the key.
One to many associations : for each relationship with a foreign key attribute that references no primary key an arity is generated. The domain of this arity is the class generated from the referenced relation.
The range is the generated class from the reference relationship. The role is the concatenation of the names of the domain and range. The maximum and minimum cardinalities are 1. Both arities generate an association. For each test, a constraint is generated with the Boolean value True or False. Those constraints express the nature of the participation of a class to a set of associations. This Rule focuses on the relation of the physical schema whose primary key is made entirely of foreign keys that reference multiple primary keys.
Description of the generation's algorithm The generation of the conceptual elements from the elements of the relational schema is based on the following general algorithm: We define : Hash listProp; : a Dictionary of properties created from foreign key's attributes. CreateClass ; aClass. CreateProperty ; aProperty.
CreateType ; aProperty. CreateConstraints Unique ; CreateArity ; anArity. SetDomain c1 ; anArity. SetRange c2 ; anArity. The generation algorithm. To test our plug-in we use the following relational database example: Fig. The RDB example. TypeImpl b idType: Integer Constraints : [mmconceptual. TypeImpl 9df6e3 idType: String Constraints : [mmconceptual. TypeImpl 12a54b idType: Integer Constraints: [mmconceptual.
ConstraintImpl a0c id: 0, Order name: key , mmconceptual. TypeImpl 1d idType: Integer Constraints: [mmconceptual. ConstraintImpl a64 id: 0, name: key , mmconceptual. This process is creating consistency, predictability, standardization in order to manage the entire resources that is applicable in business organization.
Data model provide framework for data used in the information system. The logical model consists of normalization model and having normal form that helps in creating relationship between two tables. This model includes elements that applicable in database system.
Logical data model is useful for creating database system because it can manage the entire data in effective manner. On the other hand, decentralized process is important process in database system which helps in physical database model.
This process is performing different task and implementing the queries in applications Deneuvy and et. For example- data modelling including tables and columns and also contain the table-spaces, hardware and partitions. System requirement- System requirement is an essential for organization to design an effective and efficient database system that is useful for user as well as company. Software and hardware requirement- Software and hardware requirement are necessary part for developing the relational database system.
These are main part of system creating an effective relationship between different tables. User interface: In this system, database administrator provides the authority for user to access the data and information of driving school and check the timing, schedule etc. All the details and information available on their driving school database system.
Of course, I hope the book also will be a useful reference for researchers already working in the area. I am grateful to Jeff Ullman for an advance copy of the bibliography to the second edition of Principles of Database Systems. My class at Stony Brook suffered bravely through the draft of the first eight chapters. Al Croker was the champion typo finder.
Ron Fagin also offered many suggestions, particularly on multivalued and join dependencies. Joyce also served as technical reader for the manuscript. Stony Brook April Formalization of Relations Updates to Relations Bibliography and Comments Boolean Operations The Project Operator.
The Join Operator Properties of Join The Divide Operator Constant Relations. Renaming Attributes. The Equijoin Operator Extensions for Other Comparisons on Domains Extending Selection The Theta-Join Operator Relational Algebra Algebraic Expressions as Mappings Restricting the Set of Operators The Split Operator The Factor Operator Inference Axioms Applying the Inference Axioms Completeness of the Inference Axioms Derivations and Derivation DAGs RAP-Derivation Sequences Derivation DAGs More about Derivation DAGs Covers and Equivalence Nonredundant Covers Extraneous Attributes Canonical Covers The Structure of Nonredundant Covers Minimum Covers Direct Determination Computing Minimum Covers Optimal Covers Annular Covers and Compound Functional Dependencies Databases and Database Schemes Normal Forms for Databases First Normal Form Anomalies and Data Redundancy Second Normal Form Third Normal Form Normalization through Decomposition Shortcomings of Normalization through Decomposition Normalization through Synthesis.
Preliminary Results for the Synthesis Algorithm Developing the Synthesis Algorithm Correctness and Other Properties of the SynthesisAlgorithm Refinements of the Synthesis Algorithm. Avoidable Attributes Boyce-Codd Normal Form Problems with Boyce-Codd Normal Form Multivalued Dependencies. Properties of Multivalued Dependencies Multivalued Dependencies and Functional Dependencies Inference Axioms for Multivalued Dependencies Multivalued Dependencies Alone Functional and Multivalued Dependencies Completeness of the Axioms and Computing Implications..
Fourth Normal Form Fourth Normal Form and Enforceability of Dependencies Join Dependencies Project-Join Normal Form.
Embedded Join Dependencies. Project-Join Mappings. Tableaux as Mappings Representing Project-Join Mappings as Tableaux Tableaux Equivalence and Scheme Equivalence Containment Mappings.
Equivalence with Constraints The Chase The Finite Church-Rosser Property.. Equivalence of Tableaux under Constraints Testing Implication of Join Dependencies Testing Implication of Functional Dependencies.. Computing a Dependency Basis Tableaux as Templates Computational Properties of the Chase Computation.. Notions of Adequate Representation. Data-Equivalence of Database Schemes.
P Specified by Functional Dependencies Only P Specified by Functional and Multivalued Dependencies Testing Data-Equivalence Equivalence and Completeness Tuple Relational Calculus Tuple Calculus Formulas Types, and Free and Bound Occurrences Tuple Calculus Expressions Limited Interpretation of Tuple Calculus Formulas Domain Relational Calculus Reduction of Tuple Calculus to Domain Calculus Reduction of Domain Calculus to Relational Algebra Tableau Queries Single Relation Tableau Queries Tableau Queries for Restricted Algebraic Expressions.
Tableau Queries that Come from Algebraic Expressions Tableau Queries for Multirelation Databases Tableau Set Queries Conjunctive Queries Levels of Information in Query Modification Simplifications and Common Subexpressions in Algebraic Expressions Optimizing Algebraic Expressions Query Decomposition The Query Decomposition Algorithm Tableau Query Optimization Tableau Query Equivalence Simple Tableau Queries Extensions for Multiple-Relation Databases Tableau Set Query Equivalence Optimizing Conjunctive Queries Query Modification for Distributed Databases Fragments of Relations Bibliography and Index Functional Dependencies and Nulls Constraints on Nulls Relational Algebra and Partial Relations Possibility Functions Generalizing the Relational Operators.
Specific Possibility Functions.. Partial Information and Database Semantics Universal Relation Assumptions. Placeholders and Subscheme Relations. Database Semantics and Window Functions. A Window Function Based on Joins Weak Instances A Further Condition on Window Functions. Properties of Database Schemes Existence of a Full Reducer.. Equivalence of a Join Dependency to Multivalued Dependencies Unique 4NF Decomposition Pairwise Consistency Implies Total Consistency Small Intermediate Joins. Syntactic Conditions on Database Schemes..
Acyclic Hypergraphs Join Trees.. The Running Intersection Property Equivalence o Conditions Graham Reduction Finding Join Trees Logic and Data Dependencies. The World of Two-Tuple Relations. Equivalence of Implication for Logic and Functional Dependencies Adding Multivalued Dependencies Nonextendability of Results More Data Dependencies Template Dependencies Examples and Counterexamples for Template Dependencies A Graphical Representation for Template Dependencies Testing Implication of Template Dependencies Generalized Functional Dependencies Closure of Satisfaction Classes Under Projection Limitations of Relational Algebra Computed Relations An Example Testing Expressions Containing Computed Relations ABOUT THE BOOK This remarkably comprehensive new book assembles concepts and results in relational databases theory previously scattered through journals, books, conference proceedings, and technical memoranda in one convenient source, and introduces pertinent new material not found elsewhere.
The material covered includes relational algebra, functional dependencies, multivalued and join dependencies, normal forms, tableaux and the chase computation, representation theory, domain and tuple relational calculus, query modification, database semantics and null values, acyclic database schemes, template dependencies, and computed relations.
The final chapter is a brief survey of query languages in existing relational systems. Each chapter contains numerous examples and exercises, along with bibliographic remarks.
All data is viewed as being stored in tables, with each row in the table having the same format. Each row in the table summarizes some object or relationship in the real world. Whether the corresponding entities in the real world actually possess the uniformity the relational model ascribes to them is a question that the user of the model must answer.
It is a question of the suitability of the model for the application at hand. Whether or not the relational model is appropriate for a particular set of data shall not concern us. There are plenty of instances where the model is appropriate, and we always assume we are dealing with such instances.
So much for philosophy. Let us consider an example. Every flight listed has certain characteristics. It is a flight from an origin to a destination.
It is scheduled to depart at a specific time and arrive at a later time. It has a flight number. Part of an airline schedule might appear as in Table 1. What do we observe about this schedule? Each flight is summarized as a set of values, one in each column. There are restrictions on what information may appear in a given column. Finally, since each flight has a unique number, no flight is represented by more than one row.
The schedule in Table 1. This set is called the domain of the attribute name. Each row in the relation is a set of values, one from the domain of each attribute name. The rows of this relation are called 5-tuples, or tuples in general. The tuples of a relation form a set, hence there are no duplicate rows. Such a subset is called a key for the relation. For the relation in Table 1. We now formalize the definitions of the last section and add a couple of new ones.
Corresponding to each attribute name A i is a set D i, 1 :s; i :s; n, called the domain of A;. We also denote the domain of A; by dom A;. The domains are arbitrary, non-empty sets, finite or countably infinite.
The mappings are called tuples. Example 1. The domains for each attribute name might be:. The relation in Table 1. Where did the mappings come from? What happened to tables and rows? As we noted in the last section, such an ordering adds nothing to the information content of a relation.
We do not want to restrict tuples to be sequences of values in a certain order. Rather, a tuple is a set of values, one for each attribute name in the relation scheme. In either case, it makes sense, given a tuple t, to discuss the value oft on attribute A, alternatively called the A-value of t. Considering t as a mapping, the A-value oft is t A. Interpreting t as a row in a table, the A-value oft is the entry oft in the column headed by A.
Since tis a mapping, we can restrict the domain oft. Let X be a subset of R. We have been treating relations as static objects. However, relations are supposed to abstract some portion of the real world, and this portion of the world may change with time.
We consider that relations are time-varying, so that tuples may be added, deleted, or changed. In Table 1. Henceforth, when dealing with a relation, we shall think of it as a sequence of relations in the sense already defined, or, in some cases, as potential sequences that the relation might follow, that is, possible states the relation may occupy. We shall discuss restrictions on the states a relation may assume, although nearly all of these restrictions will be memoryless: they will depend only on the current state of the relation and not on its history of previous states.
That is, no two tuples have the same value on all attributes in K. Hence, it is sufficient to know the K-value of a tuple to identify the tuple uniquely. Let us formulate some notation for relations, schemes, and keys. To denote the key of a relation, we underline the attribute names in the key. Any relation r R is restricted to have AC as a key. If we wish to specify more than one key for a scheme or relation, we must list the keys separately, since the underline notation will not work.
The keys explicitly listed with a relation scheme are called designated keys. There may be keys other than those listed; they are implicit keys. Sometimes we distinguish one of the designated keys as the primary key. Updates to Relations 5.
Our definition of key is actually a bit too broad. We shall restrict our definition slightly. Definition 1. K is a superkey of r if K contains a key of r. The new definition of superkey is the same as the former definition of key.
We shall still use the old definition of key in designated key, that is, a designated key may be a superkey. There are some subtleties with keys. As we mentioned in the last section, we consider relations to be time-varying. Different states of the relation may have different keys. Thus, in determining keys for a relation scheme, we look across all states a relation on the scheme may assume. Keys must remain keys for. However, it is likely that there could be two flights between the same origin and destination, although they would undoubtedly leave at different times.
We shall mainly concern ourselves with keys and superkeys of relation schemes, thinking in terms of all permissible states of a relation on the scheme. What is and is not a key is ultimately a semantic question.
Now that we have relations, what can be done with them? Suppose we wish to put more information into a relation. The intent of the add operation is clear, to add the tuple described to the relation specified. The result of the operation might not agree with the intent for one of the following reasons:.
The tuple described does not conform to the scheme of the specified relation. Some values of the tuple do not belong to the appropriate domains. The tuple described agrees on a key with a tuple already in the relation. In any of these cases, we consider ADD r; d 1 , d 2 ,.
We must be able to undo what we do, which calls for a delete operation. On a relation r as above, the delete operation takes the form. Actually, we do not need to give so much information to identify uniquely the tuple to be removed. Specifying the values on some key will suffice.
The result of the delete operation is as expected. There is no restriction on removing the last tuple from a relation; the empty relation is allowed. Instead of adding or deleting an entire tuple, we may want to modify only part of a tuple. Modification is achieved with the change operation. The change operation is mainly a convenience. Construct a relation on R based on the following information. Roberts, Ruskin, and Raphael are all ticket agents. Rayburn is a baggage handler. Rice is a flight mechanic.
Price manages all ticket agents. Powell manages Rayburn. Porter manages Rice, Price, Powell and himself. Powell is head of ground crews and Porter is chief of operations. Roberts just started work, Ruskin and Raphael have worked for a year and a half, and Rayburn has worked for 2 years. Ruskin and Raphael complete their second year. Rice quits. Powell quits. His duties are assumed by Porter.
Randolph is hired as a ticket agent. When does the expression t X Y make sense? When it does make sense, how can it be simplified?
The maximum number of superkeys? Is this change operation necessarily legal? If the order of the operations is changed in E, will the result necessarily be the same when Econsists of a only add operations? For background in database systems and the place of the relational model in the scheme of things, the reader is directed to the books by Cardenas [], Date [ , Tsichritzis and Lochovsky [], Ullman [], and Wiederhold [].
Chapter 2. The update operations defined in the last chapter are not so much operations on relations as operations on tuples. First, we see how the usual Boolean operations on sets apply to relations, and second, we consider three operators particular to relations: select, project, and join. Thus, Boolean operations can be applied to two such relations.
Ifrand sare relations on the scheme R, then r s, r U sand r - s are all the obvious relations on R. The set r s is the relation q R containing all tuples that are in both r ands, r U s is the relation q R containing all tuples that are in either r or s, and r - s is the relation q R containing those tuples that are in r but not ins.
However, if any attribute A in R has an infinite domain, rwill also be infinite and not a relation in our sense. Let adom R, r be the set of all tuples over the attributes of R and their active domains relative to r. Note that r is always a relation. It is difficult to imagine a natural situation where the complement of a relation would be meaningful, except perhaps for a unary one-attribute relation. Active complement might arise naturally. Suppose a company has a training program that has a group of employees working two weeks in each department.
The relation done would tell who had not completed training in what department, provided every employee in the program and every department is mentioned in done. Active complement can also be used as a storage compression device, when the active complement of a relation has fewer tuples than the relation itself. Select is a unary operator on relations. When applied to a relation r, it yields another relation that is the subset of tuples of r with a certain value on a specified attribute.
Let r be a relation on scheme R, A an attribute in R, and a an element of dom A. Example 2. Table 2. Select operators commute under composition. See Exercise 2. Select is distributive over the binary Boolean operations:. The order of selection and complementation does make a difference in the result see Exercise 2.
Project is also a unary operator on relations. Where select chooses a subset of the rows in a relation, project chooses a subset of the columns. If two projections are performed in a row, the latter subsumes the former: If 7! Similarly, for a string of projections, only the outermost need be considered for evaluation. This identity does not hold when A is not an element of X see Exercise 2. The connection between projection and Boolean operations is treated in Exercises 2.
Suppose our imaginary airline maintains a list of which types of aircraft may be used on each flight and a list of the types of aircraft each pilot is certified to fly. We want a list showing which pilots can be used for each flight. Options is shown in Table 2. In general, join combines two relations on all their common attributes.
Thus, every tuple in q is a combination of a tuple from r and a tuple from s with equal R n S -values. Returning to Table 2. Actually, the Cartesian product of two relations would be a set of ordered pairs of tuples.
By Cartesian product we shall mean Cartesian product followed by the natural isomorphism from pairs of R-tuples and S-tuples to RS-tuples.
C1 d1 C2 d1 C2 d2. There are more properties of join than we have room to list. We shall give some of them here, use others for exercises, and leave the rest for the reader to discover. The join operation can be used to simulate selection. The intersection of Rand A is A, so. We can also manufacture a generalized select operation using join.
Let s A now be a relation with k tuples, t1, t2,.. Properties of Join There are other variations of selection available by adding columns and tuples to s. It can be seen that the join operator is commutative from the symmetry in its definition. It is also associative. Given relations q, r, and s,. Hence, we can write an unparenthesized string of joins without ambiguity.
We introduce some notation for multiple joins. Tuple t is the result of joining t1 , t 2, Using the associativity of the join and induction, it is straightforward but tedious to prove the following result.
Not evecy tuple of evecy relation may enter into the join. Tuple a 1 b 2 of s 1 and tuple b 2 c 1 of s2 are left out of the join, for instance. The scheme for q is RS. Is there any connection between rand r '? Containment can also become equality without r and s joining completely see Exercise 2.
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