Database More database concepts and vocabulary CSC 240

Database More database concepts and vocabulary CSC 240 (Blum) 1

Database Categorizations I • How many users can use the database at a given time? – If one: It is a single-user database. – If more than one: It is a multi-user database. • If used by only several users in one department: It is a workgroup database. • If used by many departments across the entire company: It is an enterprise database. CSC 240 (Blum) 2

Database Categorizations II • How many computers are used for the database? (Note that this is distinct from how many computers do the users employ to access the database. ) – If one: It is a centralized database. – If more than one: It is a distributed database. • There are various scenarios for handling distributed databases, such as do the computers have distinct data or copies of the same data, etc. CSC 240 (Blum) 3

Database Categorizations III • How up-to-date is the data in the database? – If the data is the database is used for the normal daily operations, for example, what items are in stock, what has been purchased in the present quarter, etc. : It is a production database or transaction database. – If the data is more historical, for example, sales over the last ten years, which one will look back on to see trends, etc. : It is a data warehouse database. CSC 240 (Blum) 4

Different Points of View • One might think that integrating various department’s data might – Confuse the naïve user – Provide the sophisticated user with too much information (e. g. all the salary info, social security numbers, etc. ) • Views, however, can be used to provide a user with only a subset of the database – Simplifying life for the naïve user – Providing a level of security by limiting what the sophisticated user can access CSC 240 (Blum) 5

Database Design and Modeling • The database approach was a big step forward from the file-based approach. • There have been various steps within the database approach. One of the most important is the development of the Relational Model. • Models are simplified abstractions of real-world events or conditions. • Good models yield good database designs that are the basis for good applications. CSC 240 (Blum) 6

Relational Model • First developed by E. F. Codd. • The main ingredient of the Relational Database Management System is the “table” structure. • A table is a matrix, consisting of a series of rows and columns. • One goal of the Relational model is to integrate a lot of data, maintain the relationships within that data, and yet minimize the amount of redundancy. CSC 240 (Blum) 7

E. F. Codd Our Hero CSC 240 (Blum) 8

Relational Model • Advantages – Structural Independence • Can change the database’s structure (e. g. add new fields) without impairing the DBMS’s ability to access and manipulate the data. – Improved conceptual simplicity – Facilitates implementation, management and use • Good design is crucial – Ad hoc query capability • Can answer questions that come up after the database was designed. CSC 240 (Blum) 9

Basic Structure: Entities • Entity – A person, place or thing about which data are to be collected and stored. – Represented by a rectangle in the Entity-Relationship (ER) diagram. – Each entity is described by a set of attributes describing a particular characteristic of the entity. • Relationship – An association among data. Most relationships describe the associations between two entities. CSC 240 (Blum) 10

E. F. Codd’s Rules • Rule 1: The Information Rule. All data should be presented to the user in table form. • Rule 2: Guaranteed Access Rule. All data should be accessible without ambiguity. This can be accomplished through a combination of the table name, primary key, and column name. CSC 240 (Blum) 11

E. F. Codd’s Rules • Rule 3: Systematic Treatment of Null Values. A field should be allowed to remain empty. This involves the support of a null value, which is distinct from an empty string or a number with a value of zero. Of course, this can't apply to primary keys. In addition, most database implementations support the concept of a non- null field constraint that prevents null values in a specific table column. CSC 240 (Blum) 12

E. F. Codd’s Rules • Rule 4: Dynamic On-Line Catalog Based on the Relational Model. A relational database must provide access to its structure through the same tools that are used to access the data. This is usually accomplished by storing the structure definition within special system tables. • Rule 5: Comprehensive Data Sublanguage Rule. The database must support at least one clearly defined language that includes functionality for data definition, data manipulation, data integrity, and database transaction control. All commercial relational databases use forms of the standard SQL (Structured Query Language) as their supported comprehensive language. CSC 240 (Blum) 13

E. F. Codd’s Rules • Rule 6: View Updating Rule. Data can be presented to the user in different logical combinations, called views. Each view should support the same full range of data manipulation that direct-access to a table has available. In practice, providing update and delete access to logical views is difficult and is not fully supported by any current database. • Rule 7: High-level Insert, Update, and Delete. Data can be retrieved from a relational database in sets constructed of data from multiple rows and/or multiple tables. This rule states that insert, update, and delete operations should be supported for any retrievable set rather than just for a single row in a single table. CSC 240 (Blum) 14

E. F. Codd’s Rules • Rule 8: Physical Data Independence. The user is isolated from the physical method of storing and retrieving information from the database. Changes can be made to the underlying architecture (hardware, disk storage methods) without affecting how the user accesses it. • Rule 9: Logical Data Independence. How a user views data should not change when the logical structure (tables structure) of the database changes. This rule is particularly difficult to satisfy. Most databases rely on strong ties between the user view of the data and the actual structure of the underlying tables. CSC 240 (Blum) 15

E. F. Codd’s Rules • Rule 10: Integrity Independence. The database language (like SQL) should support constraints on user input that maintain database integrity. This rule is not fully implemented by most major vendors. At a minimum, all databases do preserve two constraints through SQL. No component of a primary key can have a null value. (see rule 3). If a foreign key is defined in one table, any value in it must exist as a primary key in another table. CSC 240 (Blum) 16

E. F. Codd’s Rules • Rule 11: Distribution Independence. A user should be totally unaware of whether or not the database is distributed (whether parts of the database exist in multiple locations). • Rule 12: Nonsubversion Rule. There should be no way to modify the database structure other than through the multiple row database language (like SQL). Most databases today support administrative tools that allow some direct manipulation of the datastructure. CSC 240 (Blum) 17

Field Versus Data • In the Olympics database modeling homework, a possible confusion is between a field/attribute and data that might be entered into that field. • For example, a confused designer might list figure skating or alpine skiing as event fields. Rather event fields should be something like Event. Category and Event. Name. Then skating is an example of an Event. Category and Women’s Figure Skating is an example of an Event. Name. CSC 240 (Blum) 18

Entity Type/Occurrence • Type versus occurrence – The entity type/occurrence distinction is similar to the class/object distinct in object-oriented programming. – An entity type is a template for an entity occurrence. • Dog is an entity type (class), whereas Lassie is an entity occurrence (object). CSC 240 (Blum) 19

Entity Type/Occurrence • The entity type (like a class) is a more abstract gathering of associated data. – E. g. Customer is an entity type that gathered together properties such as First. Name, Last. Name, etc. • The entity occurrence (like an object) has specific values – E. g. John Smith is an entity occurrence with First. Name of John, last. Name of Smith, etc. CSC 240 (Blum) 20

When thinking of an entity type, think of a table in design view. CSC 240 (Blum) 21

When thinking of an entity occurrence, think about a specific row in Data. Sheet View CSC 240 (Blum) 22

Entities: Strong and Weak • Entities are sometimes categorized as strong and weak. • A strong entity has an independent existence, whereas the weak entity depends on some other entity. The strong to weak relationship among entities can be called – Parent – child – Owner – dependent – Dominant – subordinate CSC 240 (Blum) 23

Strong-weak entity example Event Strong Parent Owner Dominant CSC 240 (Blum) Trial Weak Child Dependent Subordinate 24

Relationship Type/Occurrence • A relationship type is some association between entity types. Like the entity type, the relationship type is an abstract template. – E. g. “Places” is a relationship type between the Customer and Order entity types. • A relationship occurrence is a specific association between specific entity occurrences. – E. g. John Smith placed Order ORD 0004 is a relationship occurrence. CSC 240 (Blum) 25

When thinking of a relationship type, think of the lookup wizard in design CSC 240 (Blum) 26

When thinking of a relationship occurrence, think of choosing a foreign key from a dropdown list CSC 240 (Blum) 27

Express Relationships as Verbs • Relationships are generally expressed as verbs. For example, – Athlete comes from Country – Athlete competes in Event • The relationship can be represented by a line between the two rectangles representing the participating entities. The verb is written on the line. In the Chen model, the verb is placed in a diamond. CSC 240 (Blum) 28

Chen modeled relationship Event Athlete. ID Athlete. FName … CSC 240 (Blum) Competes in Event. ID Event. Category … 29

Basic Structure: Relationships • Relationships are said to have a multiplicity. • Relationships are categorized by how many things are related to how many things. – 1: M (one-to-many) – M: N (many-to-many) – 1: 1 (one-to-one) CSC 240 (Blum) 30

Relationship Examples • One-to-many – A country will be represented by many athletes, but each athlete represents only one country. • Many-to-many – An athlete may compete in many events, and an event has many athletes competing in it. • One-to-one – Each country has one athlete serve as flag bearer in the opening ceremony. CSC 240 (Blum) 31

Degree of a Relationship • Relationships are said to have a degree (the number of entity types involved). – Binary: involves two entities – Ternary: involves three entities – Quaternary: involves four entities • Even if not using the Chen model, ternary and higher degree relationships are represented using a diamond. CSC 240 (Blum) 32

Ternary Relationship Example Stock. Holder No arrows in relationships with degree higher than 2. CSC 240 (Blum) Buy/Sell Stock. Broker A Stock. Holder buys or sells a stock through a Stock. Broker. 33

Quaternary Relationship Example Producer Actor Make a movie Director Writer CSC 240 (Blum) 34

Recursive Relationship • If an entity (type) has a relationship with itself, that relationship is called recursive. • The entity occurrences in the relationship may be distinct. • Since the subject and object are of the same entity type, the “roles” the occurrences are playing in the relationship may be added to the diagram. CSC 240 (Blum) 35

Recursive Relationship • A typical example here is if one employee serves as the supervisor of another employee. – While the relationship involves different entity occurrences (i. e. two different employees), it involves only one entity type. Thus as far as entity type goes, the Employee entity has a relationship with itself. CSC 240 (Blum) 36

Role names are used to clarify situations with multiple relationships Teacher Teaches Faculty Advisor CSC 240 (Blum) Student Advises Student Advisee 37

Attributes • Attributes are the properties of an entity. – E. g. the attributes of a Customer are First. Name, Last. Name, etc. • Relationships can also have properties. – E. g. a stock is bought or sold on a particular date (at a particular price). – (One may consider introducing a new entity called a transaction. ) CSC 240 (Blum) 38

Attribute Domain • An attribute’s domain is the set of values that a property is allowed to take on. • For example, – – The quantity of items ordered is 0 The price paid is 0 Gender would be ‘M’ or ‘F’ (or perhaps NULL) Phone numbers consist of numbers only. One can also specify the number of digits or a range thereof. CSC 240 (Blum) 39

Self-documenting attribute names • When it comes time to implement the database and one is turning attributes into the corresponding fields, resist the temptation to use abbreviated field names. • If you use descriptive field names, your implementation will be self-documenting – in that many people will know what you mean simply by the name you have used. – Some designers suggest that the first part of a field name refer to the table/entity it belongs to. This can be especially useful with common fields like IDs and names. CSC 240 (Blum) 40

Attributes: Simple or Composite • A property that takes on a value that cannot be broken down into pieces is called simple, (aka “atomic”) – E. g. quantity, price, gender • A property that can be broken into constituent properties is called composite. – E. g. address street, city, state, zipcode – name first. Name, last. Name CSC 240 (Blum) 41

Attributes: Single-valued or Multi-valued • Single-valued: a property that takes on only one value at a time for a given entity occurrence. – E. g. date. Of. Birth, you only have one • Multi-valued: a property that can take on more than one value at the same time for a given entity occurrence – E. g. phone. Number (home and cell number) – E. g. beneficiary CSC 240 (Blum) 42

Attributes: Derived • If a property can be determined from other properties, it is said to be derived. – E. g. age or Age. Category (20 -29, 30 -39, etc. ) can be derived from date. Of. Birth – E. g. tax. Bracket can be derived from gross. Income and deductions – E. g. city might be derivable from zipcode CSC 240 (Blum) 43

Keys • A candidate key is a minimal set of properties that uniquely determine each entity occurrence (record, row, tuple). • A candidate key may be composite, i. e. consisting of more than one property. • Minimal above means that if one property is removed from the set, the set no longer uniquely determines an occurrence. CSC 240 (Blum) 44

Keys (Cont. ) • There can be more than one candidate key. – In the school’s database (banner), a person can be identified by his or her • social. Sec. Number • id. Number • pidm • The primary key is the candidate key that is selected to identify the entity occurrence internally (within the database). • A candidate not chosen to be the primary is sometimes called an alternate key. CSC 240 (Blum) 45

References • • Database Systems Rob and Coronel Database Systems, Connolly and Begg SQL for Dummies, Taylor http: //www. metacard. com/wp 1 a. html • http: //www. oracle. com/glossary/index. html? axx. html • http: //www. itworld. com/nl/db_mgr/05072001/ • Concepts of Database Management, Pratt and Adamski CSC 240 (Blum) 46