1. Rob Gleasure R.Gleasure@ucc.ie robgleasure.com
IS6146 Databases for Management Information Systems Lecture 1: Introduction to IS6146
Rob Gleasure
robgleasure.com

2. IS6146 Lecture times 13.00-15.00, Wednesday (Boole 2) Contact me at
Ext 2503
Room 2.112
Website for this course
robgleasure.com

3. IS6146 Module content and learning outcomes
Using relational databases (i.e. SQL)
NoSQL and distributed data stores
Analysing unstructured/semi-structured data

4. IS6146 Course Assessment Continuous assessment: 50 marks
In-class exam – 20 marks
Group report – 30 marks
Exam: 50 marks
Staff Student Support Committee meeting 31st January, , Room

5. Continuous Assessment
In-class SQL exam (20 marks)
22nd February*
Reports due (20 marks)
5th April*
*Provisional dates

6. OLTP Databases
Online transaction processing (OLTP) databases/data stores support ongoing activities in an organisation
Hence, they need to
Manage accurate real-time transactions
Handle reads, writes, and updates by large numbers of concurrent users
Decompose data into joinable, efficient rows (e.g. normalised to 3rd form)
These issues are often labelled ACID database transactions
Atomic: Every part of a transaction works or it’s all rolled back.
Consistent: The database in never left in inconsistent states
Isolated: Transactions do not interfere with one other
Durable: Completed transactions are not lost if system crashes

7. OLAP Data stores
Online analytical processing (OLAP) databases/data stores are used to support predictive analytics
Hence, they need to
Allow vast quantities of historical data to be accessed quickly
Be updatable in batches (often daily)
Aggregate diverse structures with summary data
These issues are often labelled BASE database transactions
Basic Availability
Soft-state
Eventual consistency

8. Data warehousing OLTP OLAP
We’ll come back to this stuff later in the course
Operational databases
Business intelligence database
HR and payroll
Extract Transform Load
Data warehouse
Data mining
Sales and customers
Visual-isation
Orders
Reporting
Technical support
Purchased data

9. Database Management Systems
A Database Management System (DBMS) is an OLTP software package designed to store and manage databases.
This is different from a database file for several reasons
DBMS makes tables visible and generates reports
DBMS helps less technical users to use data efficiently
DBMS protects data from inconsistency and duplication due to multiple concurrent users
DBMS handles crash recovery
DBMS handles security and access control

10. Database Management Systems
DBMS operates at the physical level
This is where data handling becomes increasingly technical
Management and business users
Conceptual model
System analysts and designers
Logical model
Physical model
Database administrators

11. Database Management Systems
DBMS are typically managed by a set Database Administrator, whose responsibilities include
Defining or refining logical schema in response to requirements
Granting appropriate access to users
Monitoring DBMS performance
Updating/changing DBMS software

12. Database Management Systems
Other users will include
Naïve users (use applications that access/modify data)
Sophisticated/specialised users (use DBMS directly to generate reports and assist business analysis)
Application developers (write programs that need to access/modify data)

13. Examples of DBMS Oracle
Large multi-user DBMS that excels at handling many clients requesting, accessing, and modifying data
Popular in large organisations and cloud services due to scalability
Very effective handling of concurrency, read consistency, parallel execution, and locking mechanisms
MS Access
Popular DBMS for small scale projects due to its relatively low cost and usability
Imports and exports data to many formats commonly used in organisations, e.g. Excel, Outlook, SQL Server, Oracle
Integrates easily with MS tools like VB, C# and .NET

14. Examples of DBMS
MySQL
Open source SQL database with free and paid versions
Flexible and scalable
Open source means lots of support and the potential for adaptability
MS SQL Server
Good performance
XML integration
Inclusion of try/catch queries

15. Accessing a DBMS with SQL
So once we have our data models in place and our DBMS set up, how do we get started using our data?
 The SQL (pronounced like sequel) query language
SQL (Structured Query Language) was introduced in the 70’s and has evolved since to add features such as
Compatibility with object-oriented programming
Compatibility with XML
Compatibility with XQuery

16. Accessing a DBMS with SQL

17. The Structure of SQL SQL can be divided into two parts:
The Data Manipulation Language (DML)
Used to create, read, update and delete tuples (CRUD operations)
Mostly used by application programmers and sophisticated/specialised users
The Data Definition Language (DDL)
Used to define database structure (relation schemas) and data access control, as well as integrity constraints and views
Mostly used by database administrator

18. Executing SQL in a DBMS

19. Accessing DBMS with SQL
The basic syntax of SQL queries are as follows:
COMMAND
column_name1, column_name2, …
FROM/SET/VALUES
table_name1, table_name2, …
WHERE
column_name comparison_operator value;
All statements in a semi-colon

20. The SQL Select statement
The SELECT statement is used to retrieve data from a database into a result table, or result-set
You can use an asterisk (*) instead of a column name if you wish to select all columns satisfying the criteria
e.g.
SELECT * FROM my_table;

21. The SQL Select statement
Let’s open up an online example from W3Schools

22. The SQL Select statement
Sometimes non-primary key columns may contain duplicate values - you can also use SELECT DISTINCT when you want to avoid duplicates in your result-set
e.g.
SELECT DISTINCT * FROM my_table;

23. The SQL Where clause
A number of comparison operators are possible with the WHERE clause
Examples
Operator
Description =
Equal
<>, !=
Not equal
>
Greater than
<
Less than
>=
Greater than or equal
<=
Less than or equal
BETWEEN
Between an range of numbers
LIKE
Searches for patterns IN
When a specific value is sought

24. The ‘LIKE’ condition and Wildcards
Sometimes we want to identify records based on slices of the data within a cell - we do this using wildcards and the LIKE condition
Examples
Wildcard
Description %
A substitute for zero or more characters _
A substitute for a single character
[charlist]
Sets and ranges of characters to match
[^charlist] or [!charlist]
Matches only a character NOT specified within the brackets

25. The SQL Where clause (continued)
Numerous clauses can be combined with the keywords AND & OR
e.g.
SELECT * FROM my_table
WHERE val1=‘true’ AND val2=‘false’;
Complex clause can be creates by nesting clauses in parentheses
WHERE val1=‘true’ AND (val2=‘false’ OR val2=‘null’);
Example

26. The SQL Order By keyword
The ORDER BY keyword is used to sort the result-set
e.g.
SELECT * FROM my_table ORDER BY NAME;
You may also specify whether you want the returned result-set to be sorted in ascending or descending order by using the keywords ASC or DESC
SELECT * FROM my_table ORDER BY NAME DESC;
Example

27. Report Identify a real workplace on which to base your report
For this workplace, your core requirements include
Identify the information system requirements and data needs of key stakeholders
Analyse existing data sources, stores and structures
Design a new conceptual data structure
Develop a relational database to support this data
Develop views, reports, etc. to allow stakeholders to get value from this database
Develop complementary data stores and/or warehouses to complement this transaction-focused data
You may also wish to include OLAP functionalities

28. Report Your submission will include
A CD storing all of the files and code necessary to run your system
A report describing
The workplace you selected
How requirements were gathered (and from whom)
A model/models of existing and redesigned data structures
How the system meets the requirements identified
An individual directly to me stating the proportion of contribution made by each of your group mates

29. Groups The groups are as follows*: Group 1:
Cadogan, Sam; Dennehy, Katie; Murray, Joseph; Leahy, Ian
Group 2:
Jiang, Pei; Barry, Mark Edgar; Philpott, Catherine;
Group 3:
Ramadan, Fatimah; McGrath, Shane Donal; Donovan, Roisin Ellen
Group 4:
Mc Garry, Anna Margaret; Lewis, Katherine Rebecca; O Sullivan, Aine Geraldine
*Please double-check you have a group and are not in the same group with anyone from last semester’s project

30. Readings http://www.w3schools.com/sql