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Chapter 3 Data Normalization Get data properly tabled! PowerPoint Presentation
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Chapter 3 Data Normalization Get data properly tabled!

Chapter 3 Data Normalization Get data properly tabled!

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Chapter 3 Data Normalization Get data properly tabled!

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  1. University of ManitobaAsper School of Business3500 DBMSBob Travica Chapter 3 Data Normalization Get data properly tabled! Based on G. Post, DBMS: Designing & Building Business Applications Updated 2019

  2. Normalization – Technical Concept • The process of putting data into the format of relational databases or organizing data into correctly designed tables. • Tables should be designed so that • a) problems (anomalies) with insertion, deletion and modification of data are avoided • b) redundancy is reduced • c) data quality is preserved (accuracy, completeness, consistency)

  3. Normalization –Practitioner’s Concept • Tables should be designed so that • business entities and their classes are clearly differentiated into separate tables • master data are separated from transactional data • tables are properly connected • all variation in data is traceable. • Example: Student <> Course <> Registration You are already familiar with this!

  4. Attributes/ Properties Entity (Class): Employee Table: Employee Rows/Objects EmployeeID TaxpayerID LastName FirstName HomePhone Address 12512 888-22-5552 Cartom Abdul (603) 323-9893 252 South Street 15293 222-55-3737 Venetiaan Roland (804) 888-6667 937 Paramaribo Ln 22343 293-87-4343 Johnson John (703) 222-9384 234 Main Street 29387 837-36-2933 Stenheim Susan (410) 330-9837 8934 W. Maple Relational Database Terminology • Relational database: A collection of tables (relations). Tables store atomic data. • Table: A collection of columns (attributes, properties, fields) describing an entity (class). Table is also a collection of rows (records) each with the same number of columns. • Each row represents an object (an instance of a class).

  5. OrderItem OrderIDItemID Quantity 1 229 2 1 253 4 2 229 1 2 555 4 Relational Database Terminology – Primary Key • Every table has a primary key (key) – an attribute that uniquely identifies each row (e.g., EmployeeID on previous slide) • Primary key can span more than one column combined (combined, composite, concatenated) key. • Other attributes are called non-key columns. A non-key depends on key. • Primary key can be generated automatically by DBMS – surrogate key. Note: Watch for data types (e.g., number vs. text) and naming rules (arbitrary but consistent).

  6. Primary key is underlined Non-key columns Table name Relational Database Shorthand Notation Customer(CustomerID, LastName, FirstName, Address, City, State, ZipPostalCode, TelephoneNumber) * • Note: Telephone number can be used as a “backup key”, “secondary key”. But the question today is, which tel. number (landline, cell; which cell number?) May need a separate table for tel. numbers. * Shorthand notation is good for analysis but not for official diagrams. Do not use it in your assignments and exams.

  7. Schema – Normalized Tables Diagram Customer Customer Salesperson Salesperson 1 1 1 1 * * * * Order Order 1 * * OrderItem OrderItem * 1 * Item Item Transformation of Class Diagram into Schema Class Diagram (Non-Normalized) places handles contains

  8. Shorthand Notation for Schema: Foreign Key Creation Customer(CustomerID, Name, Address, City, Phone) Salesperson(EmployeeID, Name, DateHired) Order(OrderID, OrderDate, CustomerID, EmployeeID) OrderItem(OrderID, ItemID, Quantity) Item(ItemID, Description, ListPrice) • Foreign Key (FK) = Attribute that is a (primary) key in another table (e.g., CustomerID in Order); encircled. • Logic & naming of OrderItem: Replacing the Order-Item M:N relationship with two 1:M relationships. Also common name: OrderDetail. • The OrderItem key is a combination of FKs (OrderID+ItemID).


  10. Customer table Key: CustomerID Attributes: Name Address Phone Video table Key: VideoID Attributes : Title RentalFee Rating… RentalTransaction table Key: TransactionID Attributes : CustomerID Date VideoRented table Key: TransactionID + VideoID Attributes: Copy# Video Store *Transaction Processing System (VSTPS): Classes Transaction Data (“Dynamic” ) — Operations Entities (change more often) Master Data (“Static”)— Market & Inventory Entities (don’t change often) * Video refers to any storage tech, such as blu-ray disks, DVD games, etc.

  11. Customer VideoTitle 1 * includes has VideoCopy Rented * * RentalTransaction Business Rules and Class Diagram for VSTPS • Business Rules for multiplicity: • A customer can have many rental transactions, each being for a specific customer. • A transaction can include many video titles, and a title is in many transactions. • A transaction can include only one copy of a video title (specific copy number).

  12. Schema for VSTPS You can create a schema almost entirely based on your knowledge of data analysis and multiplicity; Table 3 may be a bit tricky to figure out. 1 Tbl 1 Customer(CustomerID, LastName, FirstName, Address, City, …) * RentalTransaction(TransID, RentDate, CustomerID) Tbl 2 1 Transaction data * VideoRented(TransID, VideoID, Copy#) Tbl 3 * 1 Tbl 4 Video(VideoID, Title, RentalFee)

  13. To arrive at a class diagram and then schema, start with the system requirements task (talk with users, understand output that exists and that is needed in the future). Which classes does this electronic form indicate? What are the possible class associations, attributes, keys? Normalization Process • Classes indicated (min.): • CUSTOMER • TRANSACTION • VIDEO • These classes translate into • 3 tables. Put together, • it is a big chunk of data • shown below in a mock-up • (incorrect) table. RentalForm(TransID, RentDate, ((CustomerID, Name, Address, City, State, …), (VideoID, Copy#, Title, RentalFee))

  14. Why Normalize – Avoiding Data Anomalies • How to get to proper tables using normalization logic? Why not use that one table RentalForm? RentalForm(TransID, RentDate, ((CustomerID, Name, Address, City, State, …), (VideoID, Copy#, Title, RentalFee)) Poor design because: • Master data (Customer, Video) repeat for each transaction - highredundancy. • Deletion of transaction data causes deletion of master data and reverse – deletion anomaly: Cannot delete target data but more (or less) than wanted. • A new customer can’t be added without adding a new video and reverse – insertion anomaly: Data can’t be added without corrupting (faking) other data. • To change customer name, all records must be rewritten – • update anomaly: Data can’t be updated just in a single master record. • Conclusion: From the normalization perspective, data must be properly designed in order to avoid CRUD* anomalies and reduce redundancy.

  15. Normalization Process • A process of creating tables that are free from data anomalies. • Practically, create master and transactional tables, and connect them via PK-FK associations. • Each M:N relationship must be replaced by two 1:M relationships. • A split & relate process. RentalForm (Customer, Video, RentalTransaction) Split in 3 tables A rental transaction contains M videos, each video rented M times. Tracking physical copies is necessary. RentalTransaction * * Relating data preserved via keys (e.g., CustomerID exported from RentalForm to Customer, and to Video). 1 * Video Customer M, N = “Many”

  16. Normalization Process (cont.) - To track each copy of a video video, RentalTransaction is further split. - New table VideoRented stays connected by exporting RentalTranscationID into VideoRented. - Changes in multiplicity (encircled 1s). One rental transaction can include M videos, but just 1 copy of each. RentalTransaction * VideoRented (copy#) A rental transaction contains a particular copy of each video rented. * * 1 Customer Video 1 1

  17. There are three normal forms we study: First normal form (1NF) Second normal form (2NF) Third normal form (3NF) The goal of the normalization process is to arrive at 3NF that suffers no data anomaly. Tables in 3NF are normalized (except for some very rare cases). Another way of thinking about normalization: In each normalized table, there is a fullfunctional dependence of the values of the non-key columns on the values of the key column (PK). Three Normal Forms

  18. Functional Dependence Between the Key and Other Columns • Functional Dependence: • The key column must be sufficient for determining values of the non-key columns. • An attribute depends on another attribute if the change of its value is caused by a change of that other attribute’s value. • From this perspective, the goal of the normalization process is to establish the Full Functional Dependence between the key and non-key columns, driving out other dependencies (Partial Functional Dependence, and Transitive Dependence). determines Full Functional Dependence (Key determines non-key values) Non-key columns Non-key columns Non-key columns Key Key Key determines Partial Functional Dependence (Part of Key determines some non-key values) determines Transitive Dependence (Non-key attribute determines another non-key)

  19. 1NF: A table is in 1NF if it does not haverepeating sections. Normalization Procedure: Remove repeating sections by splitting the initial table into new tables. Similar to recognizing classes and transforming them into tables. Ask after removing repeating sections: Can values of non-key attributes be predicted form the key values? Customer(CustomerID, Phone, Name, Address, City, State) RentalTransaction(TransID, RentDate) + CustomerID (FK) Reminder of table RentalForm New tables Video(VideoID, Copy#, Title, RentalFee) First Normal Form (1NF) ?* New tables Video(VideoID, Title, RentalFee) Reminder of table Video VideoRented(VideoID, Copy#) + TransID (FK)

  20. Anomalies with Repeating Sections RentalForm(TransID, RentDate, ((CustomerID, Phone, Name, Address, City, State, …), (VideoID, Copy#, Title, RentFee)) Repeating sections TransID RentDate CustomerID LastName Phone Address VideoID Copy# Title RentFee 1 4/18/02 3 Washington 502-777-7575 95 Easy Street 1 2 2001: A Space Odyssey $1.50 1 4/18/02 3 Washington 502-777-7575 95 Easy Street 6 3 Clockwork Orange $1.50 2 4/30/02 7 Lasater 615-888-4474 67 S. Ray Drive 8 1 Hopscotch $1.50 2 4/30/02 7 Lasater 615-888-4474 67 S. Ray Drive 2 1 Apocalypse Now $2.00 2 4/30/02 7 Lasater 615-888-4474 67 S. Ray Drive 6 1 Clockwork Orange $1.50 • Repeating groups cause • high redundancy • update anomaly (must run through all records to make the update) • insertion anomaly causes wrong data (fake CustomerID if new video added) • - deletion anomaly (can’t delete just wanted data but also unwanted data) • If there are repeating sections, the table is not in the first normal form (1NF).

  21. Second Normal Form (2NF) • 2NF: A table is in 2NF if it is (a) is 1NF and (b) non-key columns depend on the entire key. • The 2NF test applies only to tables with the key that has two or more attributes – combined (concatenated) key. • Suppose that table Video is like below. Combined to determine (fits 2NF) Video(TransID, VideoID, Copy#, Title, RentalFee) Sufficient to determine (violates 2NF) • The Copy# can be predicted from a combination of TransID and VideoID – Full Functional Dependence. • The Title and RentalFee can be predicted from VideoID. Therefore, there is Partial Functional Dependence – violation of 2NF.

  22. If any non-key column depends just on a part of the key there is partial functional dependence and the table is not in 2NF. Second Normal Form (2NF), cont’d • Solution: Split tables and get results as on slide 16. Video(VideoID, Title, RentalFee) VideoRented(TransID,VideoID, Copy#)

  23. 3 NF: Table is in 3NF if it is (a) in 2NF, and (b) each non-key attribute depends on the key only (on the key and nothing but the key). If any non-key depends on some other non-key, there is Transitive Dependence and the table is not in 3NF. Our design is already in 3NF! Check it: Customer(CustomerID, LastName, FirstName, Address, City, …) RentalTransaction(TransID, RentDate, CustomerID) VideoRented(TransID, VideoID, Copy#) Video(VideoID, Title, RentalFee) Third Normal Form (3NF) See footnote

  24. 3NF Violation Examples Tables in 2NF but there is transitive dependence: • RentalTransaction(TransID, RentDate, CustomerID, CustomerStanding) Solution: put CustomerStanding in tbl Customer • Sale(SaleID, CustomerID, SalespersonID, SalespersonRank…) • Solution: split Sale(SaleID, CustomerID, SalespersonID) Salesperson(SalespersonID, SalespersonRank) • Forms beyond the 3rd are very rare and therefore reaching 3NF is sufficient for most of practical purposes. • When we say “create schema”, we mean “create tables that are in 3NF”.

  25. 1) If a table has repeating sections, there is huge redundancy, different classes are mixed together, and all anomalies occur. Split the table, so that classes are clearly differentiated. Result: 1NF. Summary of Normal Forms (Must know by heart!) 1NF: A table is in 1NF if it does not haverepeating sections. 2) If a table has a combined key, non-key columns may depend on just a part of the primary key, and so there is partial functional dependency. Split the table so that in new tables non-keys depend on the entire key. Result: 2NF. 2NF: A table is in 2NF if it is in 1NF and non-key columns depend on the entire combined key. 3) If a non-key depends on another non-key, there is transitive dependency. Split the table so that in new tables each non-key depends on the key and nothing but the key. Result: 3NF. 3NF: A table is in 3NF if it is in 2NF and all non-key columns depend on the key only.