Relational Database for Forest Inventory Data structuring

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1 ENFI Relational Database for Forest Inventory Data structuring Dr. Fouad MOUNIR National Forest School of Engineers Salé Morocco 1

2 Objectives Understand definition of modern relational database Understand and be able to apply a practical method for designing databases Recognize and avoid common pitfalls of database design

3 Phases of Database Design Data Requirements Specification of requirements and results Conceptual Design Conceptual Schema Logical Design Logical Schema Physical Design Physical Schema Conceptual design begins with the collection of requirements and results needed from the database (ER Diag.) Logical schema is a description of the structure of the database (Relational, Network, etc.) Physical schema is a description of the implementation (programs, tables, dictionaries, catalogs 3

4 Models A data model is a collection of objects that can be used to represent a set of data and operations to manipulate the data Conceptual models are tools for representing reality at a very high-level of abstraction Logical models are data descriptions that can be processed by computers 4

5 What s a database? A collection of logically-related information stored in a consistent fashion The storage format typically appears to users as some kind of tabular list (table, spreadsheet)

6 What Does a Database Do? Stores information in a highly organized manner Manipulates information in various ways, some of which are not available in other applications or are easier to accomplish with a database Models some real world process or activity through electronic means Often called modeling a business process Often replicates the process only in appearance or end result

7 Databases and the Systems which manage them Modern electronic databases are created and managed through means of RDBMS: Relational DataBase Management Systems An individual data storage structure created with an RDBMS is typically called a database A database and its attendant views, reports, and procedures is called an application

8 Database Applications Database (the actual DB with its attendant storage structure) SQL Engine - interprets between the database and the interface/application Interface or application the part the user gets to see and use

9 Relational Database Management Systems Mid-level Microsoft Access, Lotus Approach, Borland s Paradox More or less total control of design allows custom builds High-end Oracle, Microsoft SQL Server, Sybase, IBM DB2 Professional level DBs: Banks, e-commerce, secure Amazon.com, Ebay.com, Yahoo.com

10 Conceptual design : Entity/Relationship model

11 Problems with Bad Design Early computers were slow and had limited storage capacity Redundant or repeating data slowed operations and took up too much precious storage space Poor design increased chance of data errors, lost or orphaned information

12 Benefits of Good Design Computers today are faster and possess much larger storage devices Rigid structure of modern relational databases helped codify problems and solutions Design problems are still possible, because the DBMS software won t protect you from poor practices Good design still increases efficiency of data processes, reduces waste of storage, and helps eliminate data entry errors

13 Modification Anomalies Customers_Orders_Inventory Customer OrderNum ItemNum Item General Tool Pentium Computer General Toll HP Printer General Tool Co monitor Totally Toys Pentium computer TOTALLY TOYS Hewlett-Packard Printer XYZ Inc Dot Matrix Printer A search for General Tool Co. would miss General Tool and General Toll. A case-sensitive search for Totally Toys would miss TOTALLY TOYS

14 Insertion Anomalies Customers_Orders_Inventory Customer OrderNum ItemNum Item General Tool Pentium Computer General Toll HP Printer General Tool Co monitor Totally Toys Pentium computer TOTALLY TOYS Hewlett-Packard Printer XYZ Inc Dot Matrix Printer How would you enter a new item into your inventory if no one had ordered it yet?

15 Deletion Anomalies Customers_Orders_Inventory Customer OrderNum ItemNum Item General Tool Pentium Computer General Toll HP Printer General Tool Co monitor Totally Toys Pentium computer TOTALLY TOYS Hewlett-Packard Printer XYZ Inc Dot Matrix Printer If you wanted to stop selling dot matrix printer and remove it from your inventory, you would have to delete the order and customer info for XYZ Inc.

16 The Fix Order_Items OrderNum ItemNum Customers CustomerNum Customer 7822 XYZ Inc Totally Toys 9123 General Tool Co. Orders CustomerNum OrderNum Products ItemNum Item 0446 Dot Matrix Printer 2246 Pentium Computer 3145 Hewlett-Packard printer monitor

17 Database Modeling Refers to various, more-or-less formal methods for designing a database Some provide precision steps and tools Ex.: Entity-Relationship (E-R) Modeling Widely used, especially by high-end database designers who can t afford to miss things Fairly complex process Extremely precise

18 Entity/Relationship model It is mainly based on three fundamental concepts. Entity type Attribute Relationship Be sure to Limit the Scope of the database.

19 Purpose of E/R Model The E/R model allows us to sketch the design of a database informally. Designs are pictures called entityrelationship diagrams. Fairly mechanical ways to convert E/R diagrams to real implementations like relational databases exist. 19

20 Entity Type Entity = thing or object. Entity type = collection of similar entities. Similar to a class in object-oriented languages. Attribute = property of an entity type. Generally, all entities in a set have the same properties. Attributes are simple values, e.g. integers or character strings. 20

21 Attribut Attribute = property of an entity type. Generally, all entities in a set have the same properties. Attributes are simple values, e.g. integers or character strings. 21

22 Types of Attributes Simple Each entity has a single atomic value for the attribute. For example, forest_name. Composite The attribute may be composed of several components. For example, Address (Apt#, House#, Street, City, State, ZipCode, Country) or Name (FirstName, MiddleName, LastName). Composition may form a hierarchy where some components are themselves composite. Multi-valued An entity may have multiple values for that attribute. For example, authors of a book. 22

23 Example Forests Name : String Nbr: Numeric Entity Forest Forest has two attributes, name and nbr (number). Each Forest entity has values for these two attributes, e.g. (Maamora, 15) 23

24 E/R Diagrams In an entity-relationship diagram, each entity type is represented by a rectangle. Each attribute of an entity type is a string representing the name if the attribute. It is located in the second part of the rectangle representing the entity type. 24

25 Identify the Key Fields Primary Key(s) Can never be Null; must hold unique values Automatically indexed in most RDBMSs Values rarely (if ever) change Try to include as few fields as possible Multi-field Primary Key Combination of two or more fields that uniquely identify an individual record Candidate Key Field or fields that qualify as a primary key Important in Third and Boyce-Codd Normal Forms

26 Relationships A relationship connects two or more entity sets. It is represented by a diamond or an oval form, with lines to each of the entity sets involved. 26

27 Example Foret num_foret forest_name area manage_plan periode_ameneg beging_mana climat Commente <pi> Number (4) Text (25) Decimal (6,2) Characters (1) Number (2) Number (4) Text Text (500) <M> 0,n Divided Forest is divided into parcels. Identifier_1... <pi> 1,n composed Subdivisée 1,1 num_parcelle area status appelation Identifier_1 Parcelle <pi> <pi> Number (4) Decimal (6,2) Text (10) Text (40) 1,n num_cellule area objet_carto Identifier_1 1,1 Cellule <pi> Number (4) Decimal (6,2) Number (2) <pi> Parcel is composed of some celles. 1,1 Contains 27

28 Identify Entities type Relationships Based on business rules being modeled Examples: each customer can place many orders all employees belong to a department each TA is assigned to one course

29 Relationship Terminology Relationship Type One-to-one: expressed as 1:1 One-to-Many: expressed as 1:N or 1:M or 1: Many-to-Many: expressed as N:N or M:M Primary or Parent Table Table on the left side of 1:N relationship Related or Child Table Table on the right side of 1:N relationship Relational Schema Diagram of table relationships in database

30 Many-Many Relationships Think of a relationship between two entity type, such as Composed between Forests and Parcelle. In a many-many relationship, an entity of the first set can be connected to many entities of the other set. E.g., a Parcel can be composed of many Species; a specie can be in the composition of many Parcels. 30

31 Example num_foret forest_name area manage_plan periode_ameneg beging_mana climat Commente Identifier_1... <pi> Foret <pi> Number (4) Text (25) Decimal (6,2) Characters (1) Number (2) Number (4) Text Text (500) <M> 1,n Divided 1,n Strata str strat_name date_photointer nbr_unite Identifier_1 <pi> <pi> Number (3) Text (19) Date Number (2) 31

32 Many-One Relationships Some binary relationships are many -one from one entity type to another. Each entity of the first set is connected to at most one entity of the second set. But an entity of the second set can be connected to zero, one, or many entities of the first set. 32

33 Example Divided, from Forests to Parcels is many-one. A Parcel belongs to at most one specific Forest. But a Forest can be divided to any number of Parcels, including zero. 33

34 Example num_foret forest_name area manage_pl an peri ode_ameneg begi ng_mana climat Commente Identi fi er_1... <pi> Foret <pi > Number (4) Text (25) Deci mal (6,2) Characters (1) Number (2) Number (4) Text Text (500) 1,n <M> composed num_parcel l e area status appelation 1,1 Parcelle Identi fi er_1 <pi > <pi > Number (4) Deci mal (6,2) Text (10) Text (40) 34

35 One-One Relationships In a one-one relationship, each entity of either entity set is related to at most one entity of the other set. Example: Relationship Responsible-of between entity sets Forests and Managers. A Forest cannot be managed by more than one manager, and no manager can have more than one forest under his responsibility. 35

36 Example Code name degree adress phone_nbr Managers Number (8) Text Text Text Number 1,1 Responsibleof 1,1 num_foret forest_name area manage_plan periode_ameneg beging_mana climat Commente Identifier_1... <pi> Foret <pi> Number (4) Text (25) Decimal (6,2) Characters (1) Number (2) Number (4) Text Text (500) <M> 36

37 In Pictures: many-many many-one one-one 37

38 Representing Multiplicity Show a many-one relationship by an arrow entering the one side or use a cardinality that is a couple of number (n,n). Show a one-one relationship by just a line entering both entity sets use a cardinality that is a couple of number (1,1). In some situations, we can also assert exactly one, i.e., each entity of one set must be related to exactly one entity of the other set. To do so, we use a rounded arrow. 38

39 Example num_foret forest_name area manage_plan periode_ameneg beging_mana climat Commente Identifier_1... <pi> Foret <pi> Number (4) Text (25) Decimal (6,2) Characters (1) Number (2) Number (4) Text Text (500) 1,n <M> composed 1,1 num_parcelle area status appelation Identifier_1 Parcelle <pi> <pi> Number (4) Decimal (6,2) Text (10) Text (40)

40 Naming Conventions Rules of thumb Table names must be unique in DB; should be plural Field names must be unique in the table(s) Clearly identify table subject or field data Be as brief as possible Avoid abbreviations and acronyms Use less than 30 characters, Use letters, numbers, underscores (_) Do not use spaces or other special characters

41 Weak Entity type Occasionally, entities of an entity type need help to identify them uniquely. Entity type E is said to be weak if in order to identify entities of E uniquely, we need to follow one or more many-one relationships from E and include the key of the related entities from the connected entity type. 41

42 Example number is almost a key for parcel, but there might be two with the same number. number is certainly not a key, since parcels on two forests could have the same number. But number, together with the Forest related to the parcel by Divided should be unique. 42

43 In E/R Diagrams number name number Parcels Divided-to Forests Double diamond for supporting many-one relationship. Double rectangle for the weak entity type. 43

44 Weak Entity-Type Rules A weak entity type has one or more many-one relationships to other (supporting) entities type. Not every many-one relationship from a weak entity type need be supporting. The key for a weak entity type is its own underlined attributes and the keys for the supporting entity sets. E.g., parcel-number and forest-name is a key for Parcels in the previous example. 44

45 How to construct a conceptual model The construction of a conceptual model can be done as follow: Identify the list of entities type For each entity type: Establish the list of it s attribute; From this list, identify the entity identifier. Determine the relationships between the entities type; For each relationship: Write down the list of it s attributes; Determine the dimension of the relationship (binary, multi-way, ); Establish the cardinalities; Verify the obtained model: Eliminate the transitivity; Be sure that all the entities in the schema are connected; Be sure that it satisfy the questions. Validate with the users. 45

46 Design Techniques 1. Avoid redundancy. 2. Limit the use of weak entity sets. 3. Don t use an entity set when an attribute will do. 46

47 Avoiding Redundancy Redundancy occurs when we say the same thing in two different ways. Redundancy wastes space and (more importantly) encourages inconsistency. The two instances of the same fact may become inconsistent if we change one and forget to change the other, related version. 47

48 Logical design : logical model

49 Logical Database Design Based upon the conceptual data model Four key steps 1. Develop a logical data model for each known user interface for the application using normalization principles. 2. Combine normalized data requirements from all user interfaces into one consolidated logical database model (view integration). 3. Translate the conceptual E-R data model for the application into normalized data requirements. 4. Compare the consolidated logical database design with the translated E-R model and produce one final logical database model for the application.

50 What Is Logical Data Modeling Translating conceptual data models into a format consistent with the architecture used by the data management software to be used with the application Normalization analysis of functional dependencies between data items to result in a structure of data that is simple, stable, and fundamental

51 Functional Dependency For a relation (table), attribute A depends on attribute B if for every valid row the value of B determines the value of A B A E.g. Student ID Student name Order No + Product No Quantity ordered

52 Normalization Normal Forms (NF): design standards based on database design theory Normalization is the process of applying the NFs to table design to eliminate redundancy and create a more efficient organization of DB storage. Each successive NF applies an increasingly stringent set of rules

53 Normal Forms First normal form No multi-valued attributes. Every attribute value is atomic. Second normal form 1NF and every non-key attribute is fully functionally dependent on the primary key. Every non-key attribute must be defined by the entire key, not by only part of the key. No partial functional dependencies. Third normal form 2NF and no transitive dependencies (functional dependency between non-key attributes.)

54 Sample 1NF Violation - 1 Employee_Projects_Time EmployeeID Name Project Time EN1-26 Sean O Brien T3, T3, T3 EN1-33 Amy Guya T3, TC, T3 EN1-35 Steven Baranco T3, TC 0.25, 0.40, , 0.35, , 0.80

55 Tables in 1NF Employees *EmployeeID LastName FirstName EN1-26 O Brien Sean EN1-33 Guya Amy EN1-35 Baranco Steven Employees_Projects *ProjNum EmployeeID Time TC EN T3 EN T3 EN

56 Sample 2NF Violation Employees_Projects *EmpID Lname Fname *ProjNum ProjTitle EN1-25 O Brien Sean T3 STAR Manual EN1-25 O Brien Sean T3 ISO Procedures EN1-25 O Brien Sean T3 Employee Handbook EN1-33 Guya Amy T3 STAR Manual EN1-33 Guya Amy TC Web site

57 Tables in 2NF Employees *EmployeeID LastName FirstName EN1-26 O Brien Sean EN1-33 Guya Amy Employees_Projects *EmployeeID *ProjNum EN T3 EN T3 Projects *ProjNum Title T3 STAR manual T3 ISO procedure

58 Sample 3NF Violation Projects_Managers *ProjNum ProjTitle ProjMgr Phone T3 STAR Manual Garrison T3 ISO Procedures Jacanda TC Web Site Friedman T3 STAR prototype Garrison TC Order System Jacanda 2954

59 Tables in 3NF Projects *ProjNum ProjTitle Manager T3 STAR manual Garrison T3 ISO procedures Jacanda Project Managers *Manager Phone Garrison 2846 Jacanda 2756

60 Transforming E-R Diagrams into Relations 1. Map Regular Entities to Relations. Composite attributes: Use only their simple, component attributes. Multi-valued Attribute - Becomes a separate relation with a foreign key taken from the superior entity.

61 Transforming E-R Diagrams Into Relations 2. Map Weak Entities Becomes a separate relation with a foreign key taken from the superior entity.

62 Transforming E-R Diagrams Into Relations 3. Map Binary Relationships One-to-Many - Primary key on the one side becomes a foreign key on the many side Many-to-Many - Create a new relation with the primary keys of the two entities as its primary key One-to-One - Primary key on the mandatory side becomes a foreign key on the optional side

63 Transforming E-R Diagrams Into Relations 4. Map Associative Entities Identifier Not Assigned Default primary key for the association relation is composed of the primary keys of the two entities Identifier Assigned It is natural and familiar to end-users. Default identifier may not be unique.

64 Transforming E-R Diagrams Into Relations 5. Map Unary Relationships One-to-Many - Recursive foreign key in the same relation Many-to-Many - Bill-of-materials: Two relations: One for the entity type. One for an associative relation in which the primary key has two attributes, both taken from the primary key of the entity

65 Transforming E-R Diagrams Into Relations 6. Map Ternary (and n-ary) Relationships One relation for each entity and one for the associative entity

66 That s it for Table Design Watch for repeating values and fields Check against the Normal Forms Make new tables when necessary Re-check all tables against the NFs Remember the business rules Use common sense, but check anyway!

67 Ensuring Data Integrity Placing constraints on how and when and where data can be entered Done after or along with table design Part of design process because many constraints are established at the database and table levels

68 Methods of Controlling Data Integrity Default Value A value a field will assume unless an explicit value is entered for that field Range Control Limits range of values that can be entered into field Referential Integrity An integrity constraint specifying that the value (or existence) of an attribute in one relation depends on the value (or existence) of the same attribute in another relation Null Value A special field value, distinct from 0, blank, or any other value, that indicates that the value for the field is missing or otherwise unknown

69 Referential Integrity True relational databases support Referential Integrity: every non-null foreign key value must match an existing primary key value. In other words, every record in a related table must have a matching record in the primary table. Preserves the validity of foreign key values. Enforced at database level.

70 Cascading Updates When a primary key value changes, Cascade Update changes the corresponding values in the related records, so no records get orphaned. Usually only one level deep Foreign key is not usually primary key of related table (except in 1:1 relationships) hence no other tables are usually related to it

71 Cascade Deletes When a primary table record is deleted, all matching records in any related table are also deleted Can propagate through multiple tables if Cascade Delete is turned on in all relationships between those tables Another protection against orphan records, only this time by eradicating them instead!

72 Levels of Enforcement Referential Integrity enforced at database level because it affects relationship between two tables. Many other business rules enforced at field and table level to ensure data integrity. Business rule implementation should be documented: how and where it is enforced in the design. Some rules can t be enforced at table or field level; must be enforced in the application level.

73 Testing of Business Rules Always test business rule implementation What happens when rule is met? What happens when rule is violated? Not much good as a data entry constraint if it doesn t constrain properly Good application or interface design will provide feedback when user violates a constraint or rule

74 Field Level Integrity Constraining by use of field properties Data type: text, number, Yes/No, Date/Time Field size Formats Entry and editing constraints Required Indexed, with or without duplicates Input masks Default value Validation Rule

75 Table Level Integrity Field Comparisons Compare value in one field to value in another Comparison performed before record is saved Violations could display an error message or force constraint of available values Validation or Lookup Tables Store generally static set of values Stored values used to populate new records to ensure accuracy of data entry

76 Documentation A good design deserves good documentation Data Dictionary for database/table design Table and field names Table and field properties Relationships, including primary and foreign keys Indexes Provide reasons for design features, especially if they intentionally violate normal design principles

77 Physical design : physical model according to the RDBMS chosen

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