IMS1907 Database Systems Summer Semester 2004/2005 Lecture 13.2 Unit Review

Monash University Basic Concepts Central concepts for understanding database systems –Database –Data –Information –Data vs Information –Metadata –DBMS

Monash University Basic Concepts File processing systems vs DBMS –data sharing –speed of access and retrieval –security –integrity, quality, consistency of data –data independence –maintenance, productivity –multiple users, complex data –backup and recovery

Monash University Basic Concepts The database system environment –DBMS –database –metadata (repository) –application software –CASE tools –user interfaces –users, developers, administrators

Monash University Personal Databases Workgroup Databases Department Databases Enterprise Databases Internet, Intranet, and Extranet Databases Data warehouses Types of Database Systems

Monash University Need for new, specialised personnel Installation cost and complexity Maintenance cost and complexity Conversion costs from legacy systems Critical need for explicit backup and recovery Organisational conflict and change Costs and Risks of Database Systems

Monash University Relational database systems organise the database as groups of related tables –table or relation –record –field –primary key –secondary key –foreign key –table structure Forms, reports, queries Relational DBMS Software

Monash University Database System Development Database development requires a focus on the information needs of a business Information Engineering (IE) is a popular, data-oriented methodology used to develop database systems –data are modelled in the organisational context, not in the usage, processing or technology context –business context changes slowly  stable databases –top-down planning

Monash University Database System Development Database systems planning –the three steps in the IE Planning phase identify strategic planning factors identify corporate planning objects develop an enterprise model Enterprise data model –needed for top-down plans and bottom-up requests –organisation-wide perspective

Monash University Enterprise modelling Database Development and the SDLC Initiation Analysis Design Implementation Review Maintenance Conceptual data modelling Logical database design Database implementation Physical database design and definition Database review Database maintenance

Monash University Enterprise modelling –the organisational perspective Conceptual data modelling –scope identification, ER modelling Logical database design –transform conceptual model into logical data model –start to specify logic for maintaining and querying database –populate repository Database Development and the SDLC

Monash University Physical database design and definition –define database for specific DBMS used –organisation of data, database processing programs Database implementation –install database and processing programs –develop procedures, load data, turn on! Database maintenance –tune and fix the database, keep it running and evolving Packaged data models – universal, industry specific Database Development and the SDLC

Monash University Data modelling Business rules ER modelling –entities or ‘things of interest’ –relationships –properties or attributes –rules and constraints affecting integrity of entities ER Modelling

Monash University Entities –strong, weak Relationships Associative entities Attributes –multi-valued, derived, composite Degree –unary, binary, ternary, n-ary ER Modelling

Monash University Cardinality –one-to-one, one-to-many, many-to-many Cardinality constraints –optional, mandatory Time dependent data Entity types and sub-types ER quality issues ER Modelling

Monash University Detailed data modelling Relational database theory –considers data structure, manipulation, integrity Relation Primary key Composite key Foreign key Integrity constraints –domain constraints, entity integrity, referential integrity Relational Database Theory

Monash University A well-structured relation –is robust, stable and flexible –contains a minimum amount of redundancy –allows users to insert, modify, and delete rows in a table without errors or inconsistencies Three types of anomaly are possible -insertion -deletion -modification Relational Database Theory

Monash University Representing entities and relationships as relations Normalisation is a process for converting complex data structures into simple, stable data structures in the form of relations Functional dependency Accomplished in stages, each of which corresponds to a “normal form” Normalisation

Monash University First normal form (1NF) –identify PK, identify and remove repeating groups Second normal form (2NF) –remove partial dependencies Third normal form (3NF) –remove transitive dependencies Merging relations Data structure diagrams (DSD) Normalisation

Monash University Has become de facto language for creating and querying relational databases Benefits and disadvantages of SQL The SQL environment –catalog –schema –data definition language (DDL) –data manipulation language (DML) –data control language (DCL) –data types Structured Query Language (SQL)

Monash University DDL –CREATE statements database, table, view, …. assigning constraints –DROP statements database, table, view, …. –ALTER statements database, table, view, column, …. Structured Query Language (SQL)

Monash University DML –INSERT, LOAD DATA statements to populate tables –SHOW, DESCRIBE statements to view structures –retrieving data – queries SELECT …. FROM …. WHERE …. –aggregate operators COUNT, SUM, AVG, MIN, MAX, DISTINCT GROUP BY ordering query results with ORDER BY Structured Query Language (SQL)

Monash University DML –matching patterns with LIKE –joining tables –sub-queries –outer joins using LEFT JOIN –query format –How joins are processed Cartesian product Structured Query Language (SQL)

Monash University Views Schema ANSI/SPARC three-schema architecture standard –external schema user views –conceptual schema single, coherent definition of enterprise data –internal schema physical storage structures Database Systems Architecture

Monash University Data independence –logical –physical Network architecture –client–server tiered architecture –distributed databases Database Systems Architecture

Monash University Database Systems Performance Issues The ultimate measures of database performance are –response time to queries –the speed of updates We also need to consider –data accessibility, security, integrity –usability –recoverability Physical database design translates conceptual and external schemas into physical designs aimed at storing data in a way that provides adequate performance

Monash University Guided by the nature of the data and its intended use Tuning the database is often performed during operation but good performance starts with a strong physical design Critical decisions during physical design –choice of storage format – data type –grouping of attributes into physical records –arranging similarly structured records in secondary memory –indexes, clusters, architectures –strategies for query handling based on indexes, records Physical Database Design

Monash University Data volume and usage analysis – workloads Choice of data types Designing physical records –page size, blocking factor Denormalisation –combining attributes into a single table –partitioning a table into several physical records Physical file organisation –sequential, indexed, hashed Clusters, indexes Improving file access - RAID Physical Database Design

Monash University Database Systems Performance Choosing an appropriate database architectures –hierarchical database model –network database model –relational database model –object-oriented database model –multidimensional database model Optimising query performance –good query design

Monash University Data is viewed as a corporate asset As with any asset, management is essential to exploit the resource to the maximum benefit Effective management of data provides support for operations and decision making at all organisational levels The roles of data administration and database administration have evolved to meet the complex task of –achieving effective management of data resources –leveraging those resources to the greatest advantage Information Resource Management

Monash University Information Resource Management There are three major roles in information resource management –data administration planning, analysis –database administration physical design and operational use –application development systems design and implementation

Monash University Information Resource Management Ineffective data administration leads to poor data utilisation New technologies and trends are driving the evolution of the roles of data administrator and database administrator Roles of the –data administrator –database administrator Evolving roles of the DA and DBA

Monash University Final Exam 3 hour exam, 10 minute reading time Ten questions –1 question consisting of ten short answer questions (10 x 1 mark) –6 short to medium length questions (1 x 5 marks, 6 x 10 marks) –ER modelling (10 marks) –normalisation (15 marks) –Attempt all questions!

Monash University Exam Strategy Know the date, time and location of your exam – it’s your responsibility! Know your seat number Make sure you have your student ID card with you Get to the exam early Ensure you have adequate writing materials with you No text books or notes allowed Relax – there’s really not much to worry about –whatever you have to do (within reason) to help you relax is ok

Monash University Exam Strategy 3 hour, 10 minute reading time, 100 marks –you’ve got 180 minutes to earn 100 marks! Convert marks to minutes  1.8 minutes/mark Calculate time available for each question It is a guide to the amount of effort I expect you to spend on each question Once the available time for a question is up, stop writing! If you finish a question within the available time, return to any incomplete answers Make sure you understand the questions!

Monash University Study Strategy Give yourself sufficient time for revision –don’t wait till the day before the exam to start studying Study all topics covered in lectures Re-read lecture notes, your notes, text books, tutorial notes Do all exercises especially revision exercises Attempt previous exams Consult tutors or lecturer before exam Get plenty of sleep, drink lots of water, eat green vegetables –it’s not quite time to party yet!