1. Introduction to Computing Lecture # 13

2. Outline Managing Files: Basic Concepts Database Definition
Database Management Systems
Database Models
Data Mining

3. Managing Files: Basic Concepts
Data storage hierarchy - levels of data stored in a computer:
Database
Files
Records
Fields
Characters (bytes)
Bits

4. Managing Files: Basic Concepts
Database – an organized collection of integrated files.
File – a collection of related records.
Record – a collection of related fields. Often called a row.
Field – a unit (individual piece) of data consisting of one or more characters (bytes). Often called a column.
Character (byte) – a letter, number, or special character.
Key field – a field that is chosen to uniquely identify a record so that it can be easily retrieved and processed.

5. Managing Files: Basic Concepts
Field Name
Record
Field

6. Database Definition
Structured set of data held in a computer. (Pocket Oxford Dictionary)
An organized collection of related (integrated) files. (Williams and Sawyer)
A database is a collection of related data or facts. (Peter Norton)

7. Database Management Systems
Database management system (DBMS) – programs that control the structure of a database and access to the data. (Williams and Sawyer)
DBMS is a collection of programs that control the database. (Peter Norton)
Advantages of DBMSes:
File sharing
Reduced data redundancy
Data redundancy – situation in which the same data fields appear in many different files and often in different formats.
Improved data integrity
Data integrity – measure of how accurate, consistent, and up-to-date data is.
Increased security

8. Database Models
Just as files can be organized in different ways, so databases can be organized in ways to best fit their use.
The four most common arrangements are:
Hierarchical
Network
Relational
Object-oriented

9. Database Models
Hierarchical database – fields or records are arranged in related groups, resembling a family tree, with child (lower-level) records subordinate to parent (higher-level) records.

10. Database Models
Network database – similar to a hierarchical database, but each child record can have more than one parent record.

11. Database Models
Relational database – a database which relates (connects) data in different files through the use of a key field, or common data element.

12. Database Models
SQL (Structured Query Language) – the standard language used to create, modify, maintain, and query relational databases.
SQL is pronounced as “sequel.”
How did this acronym get such an unlikely pronunciation?
The first structured query language was developed by IBM in the 1970s; its product name was “Sequel2.”
E. F. Codd is considered the “father” of relational database management systems – the most common model of databases.
His article entitled “A Relational Model of Data for Large Shared Data Banks” was published in the June 1970 “Communications of the ACM.”

13. Database Models
Object-oriented database – database which uses “objects” (software written in small, reusable chunks) as elements within database files
An object consists of:
Data in any form, and
Instructions on the actions to take on the data

14. Survey of Database Systems
Databases for individuals
Manage aspects of your life
Organizes hobbies for school
Microsoft Access is the most popular
Common Corporate DBMS
Oracle
DB2
Microsoft SQL Server
MySQL

15. Survey of Database Systems
Oracle
Most popular enterprise-level DBMS
Very flexible storage system
Can be very complex
Platform independent
Offers a wide range of solutions
DB2
Venerable IBM database
Only database using pure SQL

16. Survey of Database Systems
Microsoft SQL Server
Fastest growing DBMS
Only runs on Microsoft platforms
Eight different versions exist
Extremely scalable architecture
Software can grow with the data
MySQL
Leading DBMS for Linux
Very inexpensive
Features are those needed in business
Often faster than other DBMS
Platform independent
An interesting article contrasting the strengths and weaknesses of these products can be found at

17. Data Mining
Data mining (DM) – the computer-assisted process of sifting through and analyzing vast amounts of data in order to extract meaning and discover new knowledge.
Searches for trends and patterns
Makes predictions on events
Supplies ideas for improving business
Data mining begins with acquiring data and preparing it for what is known as the data warehouse by the following steps:
Data sources
Data fusion and cleansing
Data and meta-data
Data warehouse
Sifting:The act of separating

18. Data Mining Data sources Data may come from a number of sources:
Point-of-sale transactions in flat files on mainframes;
Databases of all kinds;
Other, e.g., news articles, online articles, etc.; and
Data from data warehouses

19. Data Mining Data fusion and cleansing
Data from diverse sources must be fused\join together, then put through a process known as data cleansing, or scrubbing.
The data may be of poor quality, full of errors and inconsistencies
Putting together the data from various sources and then “scrubbing” the data to eliminate errors and inconsistencies.
Fusion=An occurrence that involves the production of a union
Scrubbing=The act of cleaning

20. Data Mining Data and meta-data
Cleaned-up data and meta-data (data about data)
The cleansing process yields both the cleaned-up data and a variation of it called meta-data.
Meta-data shows the origins of the data, the transformations it has undergone, and summary information about it, which makes it more useful than the cleansed but unintegrated, unsummarized data.

21. Data Mining Data warehouse
A special database of cleaned up data and meta-data.
Both the data and the meta-data are sent to the data warehouse.

22. Data Mining Some applications of data mining: Marketing: Health:
Marketers use data mining tools to mine point-of-sale databases of retail stores, which contains facts for thousands of products in hundreds of geographic areas.
By understanding customer preferences and buying patterns, marketers hope to target consumers’ individual needs.
Health:
A coach in the U.S. Gymnastics Federation used a data mining system called IDIS to discover what long-term factors contributed to athletes’ performance, so as to know what problems to treat early on.

23. End
Questions ?