1. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Lecture 13 of 42 Wednesday, 19 September 2007 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: http://snipurl.com/va60http://snipurl.com/va60 Course web site: http://www.kddresearch.org/Courses/Fall-2007/CIS560http://www.kddresearch.org/Courses/Fall-2007/CIS560 Instructor home page: http://www.cis.ksu.edu/~bhsuhttp://www.cis.ksu.edu/~bhsu Reading for Next Class: Section 6.1 – 6.2, Silberschatz et al., 5 th edition Database Design Overview Notes: Relational Calculi, PS3

2. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Modification of the Database – Deletion Deletion of tuples from a relation is expressed by use of a D. command. In the case where we delete information in only some of the columns, null values, specified by –, are inserted. Delete customer Smith Delete the branch_city value of the branch whose name is “Perryridge”.

3. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Deletion Query Examples Delete all loans with a loan amount greater than $1300 and less than $1500.  For consistency, we have to delete information from loan and borrower tables

4. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Deletion Query Examples (Cont.) ‏ Delete all accounts at branches located in Brooklyn.

5. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Modification of the Database – Insertion Insertion is done by placing the I. operator in the query expression. Insert the fact that account A-9732 at the Perryridge branch has a balance of $700.

6. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Modification of the Database – Insertion (Cont.) ‏ Provide as a gift for all loan customers of the Perryridge branch, a new $200 savings account for every loan account they have, with the loan number serving as the account number for the new savings account.

7. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Modification of the Database – Updates Use the U. operator to change a value in a tuple without changing all values in the tuple. QBE does not allow users to update the primary key fields. Update the asset value of the Perryridge branch to $10,000,000. Increase all balances by 5 percent.

8. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Microsoft Access QBE Microsoft Access supports a variant of QBE called Graphical Query By Example (GQBE)‏ GQBE differs from QBE in the following ways  Attributes of relations are listed vertically, one below the other, instead of horizontally  Instead of using variables, lines (links) between attributes are used to specify that their values should be the same.  Links are added automatically on the basis of attribute name, and the user can then add or delete links  By default, a link specifies an inner join, but can be modified to specify outer joins.  Conditions, values to be printed, as well as group by attributes are all specified together in a box called the design grid

9. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts An Example Query in Microsoft Access QBE Example query: Find the customer_name, account_number and balance for all accounts at the Perryridge branch

10. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts An Aggregation Query in Access QBE Find the name, street and city of all customers who have more than one account at the bank

11. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Aggregation in Access QBE The row labeled Total specifies  which attributes are group by attributes  which attributes are to be aggregated upon (and the aggregate function).  For attributes that are neither group by nor aggregated, we can still specify conditions by selecting where in the Total row and listing the conditions below As in SQL, if group by is used, only group by attributes and aggregate results can be output

12. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Banking Example branch (branch_name, branch_city, assets ) customer (customer_name, customer_street, customer_city ) account (account_number, branch_name, balance ) loan (loan_number, branch_name, amount )‏ depositor (customer_name, account_number )‏ borrower (customer_name, loan_number )‏

13. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Chapter 6: Entity-Relationship Model Design Process Modeling Constraints E-R Diagram Design Issues Weak Entity Sets Extended E-R Features Design of the Bank Database Reduction to Relation Schemas Database Design UML

14. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Modeling A database can be modeled as:  a collection of entities,  relationship among entities. An entity is an object that exists and is distinguishable from other objects.  Example: specific person, company, event, plant Entities have attributes  Example: people have names and addresses An entity set is a set of entities of the same type that share the same properties.  Example: set of all persons, companies, trees, holidays

15. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Entity Sets customer and loan customer_id customer_ customer_ customer_ loan_ amount name street city number

16. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Relationship Sets A relationship is an association among several entities Example: HayesdepositorA-102 customer entityrelationship setaccount entity A relationship set is a mathematical relation among n  2 entities, each taken from entity sets {(e 1, e 2, … e n ) | e 1  E 1, e 2  E 2, …, e n  E n } where (e 1, e 2, …, e n ) is a relationship  Example: (Hayes, A-102)  depositor

17. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Relationship Set borrower

18. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Relational Database Design Features of Good Relational Design Atomic Domains and First Normal Form Decomposition Using Functional Dependencies Functional Dependency Theory Algorithms for Functional Dependencies Decomposition Using Multivalued Dependencies More Normal Form Database-Design Process Modeling Temporal Data

19. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts The Banking Schema branch = (branch_name, branch_city, assets)‏ customer = (customer_id, customer_name, customer_street, customer_city)‏ loan = (loan_number, amount)‏ account = (account_number, balance)‏ employee = (employee_id. employee_name, telephone_number, start_date)‏ dependent_name = (employee_id, dname)‏ account_branch = (account_number, branch_name)‏ loan_branch = (loan_number, branch_name)‏ borrower = (customer_id, loan_number)‏ depositor = (customer_id, account_number)‏ cust_banker = (customer_id, employee_id, type)‏ works_for = (worker_employee_id, manager_employee_id)‏ payment = (loan_number, payment_number, payment_date, payment_amount)‏ savings_account = (account_number, interest_rate)‏ checking_account = (account_number, overdraft_amount)‏

20. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Combine Schemas? Suppose we combine borrow and loan to get bor_loan = (customer_id, loan_number, amount )‏ Result is possible repetition of information (L-100 in example below)‏

21. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts A Combined Schema Without Repetition Consider combining loan_branch and loan loan_amt_br = (loan_number, amount, branch_name)‏ No repetition (as suggested by example below)‏

22. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts What About Smaller Schemas? Suppose we had started with bor_loan. How would we know to split up (decompose) it into borrower and loan? Write a rule “if there were a schema (loan_number, amount), then loan_number would be a candidate key” Denote as a functional dependency: loan_number  amount In bor_loan, because loan_number is not a candidate key, the amount of a loan may have to be repeated. This indicates the need to decompose bor_loan. Not all decompositions are good. Suppose we decompose employee into employee1 = (employee_id, employee_name)‏ employee2 = (employee_name, telephone_number, start_date)‏ The next slide shows how we lose information -- we cannot reconstruct the original employee relation -- and so, this is a lossy decomposition.

23. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts A Lossy Decomposition

24. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts First Normal Form Domain is atomic if its elements are considered to be indivisible units  Examples of non-atomic domains:  Set of names, composite attributes  Identification numbers like CS101 that can be broken up into parts A relational schema R is in first normal form if the domains of all attributes of R are atomic Non-atomic values complicate storage and encourage redundant (repeated) storage of data  Example: Set of accounts stored with each customer, and set of owners stored with each account  We assume all relations are in first normal form (and revisit this in Chapter 9)‏

25. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Microsoft Access QBE Microsoft Access supports a variant of QBE called Graphical Query By Example (GQBE)‏ GQBE differs from QBE in the following ways  Attributes of relations are listed vertically, one below the other, instead of horizontally  Instead of using variables, lines (links) between attributes are used to specify that their values should be the same.  Links are added automatically on the basis of attribute name, and the user can then add or delete links  By default, a link specifies an inner join, but can be modified to specify outer joins.  Conditions, values to be printed, as well as group by attributes are all specified together in a box called the design grid

26. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Modeling A database can be modeled as:  a collection of entities,  relationship among entities. An entity is an object that exists and is distinguishable from other objects.  Example: specific person, company, event, plant Entities have attributes  Example: people have names and addresses An entity set is a set of entities of the same type that share the same properties.  Example: set of all persons, companies, trees, holidays

27. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Entity Sets customer and loan customer_id customer_ customer_ customer_ loan_ amount name street city number

28. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Relationship Sets A relationship is an association among several entities Example: HayesdepositorA-102 customer entityrelationship setaccount entity A relationship set is a mathematical relation among n  2 entities, each taken from entity sets {(e 1, e 2, … e n ) | e 1  E 1, e 2  E 2, …, e n  E n } where (e 1, e 2, …, e n ) is a relationship  Example: (Hayes, A-102)  depositor

29. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Relationship Set borrower

30. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Relationship Sets (Cont.) ‏ An attribute can also be property of a relationship set. For instance, the depositor relationship set between entity sets customer and account may have the attribute access-date

31. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Degree of a Relationship Set Refers to number of entity sets that participate in a relationship set. Relationship sets that involve two entity sets are binary (or degree two). Generally, most relationship sets in a database system are binary. Relationship sets may involve more than two entity sets. Relationships between more than two entity sets are rare. Most relationships are binary. (More on this later.)‏  Example: Suppose employees of a bank may have jobs (responsibilities) at multiple branches, with different jobs at different branches. Then there is a ternary relationship set between entity sets employee, job, and branch

32. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Attributes An entity is represented by a set of attributes, that is descriptive properties possessed by all members of an entity set. Domain – the set of permitted values for each attribute Attribute types:  Simple and composite attributes.  Single-valued and multi-valued attributes  Example: multivalued attribute: phone_numbers  Derived attributes  Can be computed from other attributes  Example: age, given date_of_birth Example: customer = (customer_id, customer_name, customer_street, customer_city ) loan = (loan_number, amount )‏

33. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Composite Attributes

34. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Mapping Cardinality Constraints Express the number of entities to which another entity can be associated via a relationship set. Most useful in describing binary relationship sets. For a binary relationship set the mapping cardinality must be one of the following types:  One to one  One to many  Many to one  Many to many

35. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Mapping Cardinalities One to oneOne to many Note: Some elements in A and B may not be mapped to any elements in the other set

36. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Mapping Cardinalities Many to oneMany to many Note: Some elements in A and B may not be mapped to any elements in the other set

37. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Keys A super key of an entity set is a set of one or more attributes whose values uniquely determine each entity. A candidate key of an entity set is a minimal super key  Customer_id is candidate key of customer  account_number is candidate key of account Although several candidate keys may exist, one of the candidate keys is selected to be the primary key.

38. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Keys for Relationship Sets The combination of primary keys of the participating entity sets forms a super key of a relationship set.  (customer_id, account_number) is the super key of depositor  NOTE: this means a pair of entity sets can have at most one relationship in a particular relationship set.  Example: if we wish to track all access_dates to each account by each customer, we cannot assume a relationship for each access. We can use a multivalued attribute though Must consider the mapping cardinality of the relationship set when deciding the what are the candidate keys Need to consider semantics of relationship set in selecting the primary key in case of more than one candidate key

39. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts E-R Diagrams Rectangles represent entity sets. Diamonds represent relationship sets. Lines link attributes to entity sets and entity sets to relationship sets. Ellipses represent attributes Double ellipses represent multivalued attributes. Dashed ellipses denote derived attributes. Underline indicates primary key attributes (will study later)‏

40. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts E-R Diagram With Composite, Multivalued, and Derived Attributes

41. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Relationship Sets with Attributes

42. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Roles Entity sets of a relationship need not be distinct The labels “manager” and “worker” are called roles; they specify how employee entities interact via the works_for relationship set. Roles are indicated in E-R diagrams by labeling the lines that connect diamonds to rectangles. Role labels are optional, and are used to clarify semantics of the relationship

43. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Cardinality Constraints We express cardinality constraints by drawing either a directed line (  ), signifying “one,” or an undirected line (—), signifying “many,” between the relationship set and the entity set. One-to-one relationship:  A customer is associated with at most one loan via the relationship borrower  A loan is associated with at most one customer via borrower

44. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts One-To-Many Relationship In the one-to-many relationship a loan is associated with at most one customer via borrower, a customer is associated with several (including 0) loans via borrower

45. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Many-To-One Relationships In a many-to-one relationship a loan is associated with several (including 0) customers via borrower, a customer is associated with at most one loan via borrower

46. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Many-To-Many Relationship A customer is associated with several (possibly 0) loans via borrower A loan is associated with several (possibly 0) customers via borrower

47. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Participation of an Entity Set in a Relationship Set Total participation (indicated by double line): every entity in the entity set participates in at least one relationship in the relationship set E.g. participation of loan in borrower is total  every loan must have a customer associated to it via borrower Partial participation: some entities may not participate in any relationship in the relationship set Example: participation of customer in borrower is partial

48. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Alternative Notation for Cardinality Limits Cardinality limits can also express participation constraints

49. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts E-R Diagram with a Ternary Relationship

50. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Cardinality Constraints on Ternary Relationship We allow at most one arrow out of a ternary (or greater degree) relationship to indicate a cardinality constraint E.g. an arrow from works_on to job indicates each employee works on at most one job at any branch. If there is more than one arrow, there are two ways of defining the meaning.  E.g a ternary relationship R between A, B and C with arrows to B and C could mean 1. each A entity is associated with a unique entity from B and C or 2. each pair of entities from (A, B) is associated with a unique C entity, and each pair (A, C) is associated with a unique B  Each alternative has been used in different formalisms  To avoid confusion we outlaw more than one arrow

51. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Design Issues Use of entity sets vs. attributes Choice mainly depends on the structure of the enterprise being modeled, and on the semantics associated with the attribute in question. Use of entity sets vs. relationship sets Possible guideline is to designate a relationship set to describe an action that occurs between entities Binary versus n-ary relationship sets Although it is possible to replace any nonbinary (n-ary, for n > 2) relationship set by a number of distinct binary relationship sets, a n-ary relationship set shows more clearly that several entities participate in a single relationship. Placement of relationship attributes

52. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Binary Vs. Non-Binary Relationships Some relationships that appear to be non-binary may be better represented using binary relationships  E.g. A ternary relationship parents, relating a child to his/her father and mother, is best replaced by two binary relationships, father and mother  Using two binary relationships allows partial information (e.g. only mother being know)‏  But there are some relationships that are naturally non-binary  Example: works_on

53. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Converting Non-Binary Relationships to Binary Form In general, any non-binary relationship can be represented using binary relationships by creating an artificial entity set.  Replace R between entity sets A, B and C by an entity set E, and three relationship sets: 1. R A, relating E and A 2.R B, relating E and B 3. R C, relating E and C  Create a special identifying attribute for E  Add any attributes of R to E  For each relationship (a i, b i, c i ) in R, create 1. a new entity e i in the entity set E 2. add (e i, a i ) to R A 3. add (e i, b i ) to R B 4. add (e i, c i ) to R C

54. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Converting Non-Binary Relationships (Cont.) ‏ Also need to translate constraints  Translating all constraints may not be possible  There may be instances in the translated schema that cannot correspond to any instance of R  Exercise: add constraints to the relationships R A, R B and R C to ensure that a newly created entity corresponds to exactly one entity in each of entity sets A, B and C  We can avoid creating an identifying attribute by making E a weak entity set (described shortly) identified by the three relationship sets

55. Computing & Information Sciences Kansas State University Wednesday, 19 Sep 2007CIS 560: Database System Concepts Weak Entity Sets An entity set that does not have a primary key is referred to as a weak entity set. The existence of a weak entity set depends on the existence of a identifying entity set  it must relate to the identifying entity set via a total, one-to-many relationship set from the identifying to the weak entity set  Identifying relationship depicted using a double diamond The discriminator (or partial key) of a weak entity set is the set of attributes that distinguishes among all the entities of a weak entity set. The primary key of a weak entity set is formed by the primary key of the strong entity set on which the weak entity set is existence dependent, plus the weak entity set’s discriminator.