261446 Information Systems

1. 261446 Information Systems Dr. Kenneth Cosh Lecture 4

2. Review • Hardware • Input, Output devices, Processors, Memory

3. Client/Server Networking • The Micro computer is called the client, while midrange computers are often servers. • Some processing is performed on the server, and some on the client; • Thin-client model • In a thin-client model, all of the application processing and data management is carried out on the server. The client is simply responsible for running the presentation software. • Fat-client model • In this model, the server is only responsible for data management. The software on the client implements the application logic and the interactions with the system user.

4. Thin and Fat Clients

5. Peer 2 Peer (P2P) • In a fat client model where all the processing and data is stored on the client, a P2P network can emerge, where servers are removed and clients communicate directly with each other. • Grid Computing, still being researched and developed, but an approach where the processing power of any machine on the network can be used and shared by others.

6. Hardware Trends • Convergence of Hardware & Telecommunications • Telephones with cameras, television, browser etc. • Internet telephony, Skype • Nanotechnology • It’s all getting smaller • & Mobile • Edge Computing • Load balancing across web servers • Autonomic Computing • Systems which can configure and optimise themselves

7. Languages • Computers only understand ‘0’s and ‘1’s. • Programming with only ‘0’s and ‘1’s would be very boring and very error prone. • Low level programming languages allow us to translate some basic instructions into a more readable english code; • add x y z • High level programming languages allow us to use a larger subset of language with a tight syntax and semantics

8. Software Trends • Less concern with machine efficiency • Cost per instruction is falling, but personnel costs continue to rise. • Hence more concerned with human efficiency than machine efficiency • Tools to support computer professional efficiency (query languages, OOP, CASE) • Tools to support executives (voice recognition, Natural language interfaces) • More OOP • Quicker development & more attractive applications.

9. Software Trends • More Purchased Applications? • Quicker implementation • Less organisational re-engineering • More User Development? • 4GLs allow anyone to code • Easy one time customisations • More Web based applications • Available everywhere

10. This Weeks Topics • The ‘Data’ Resource • Organising Data • Databases

11. Organisational Obstacles • Implementing new data models requires re-examining the role of data within an organisation, • Who has access to what data, and when? • Changing the allocation (or sharing) of data can impact on current power relationships, and so is often met by political resistance. • Traditionally data was stored in file format, with each department having a selection of files. • More recently databases and DBMS allow data to be shared across multiple departments

12. So What’s the Problem? • Systems within systems (subsystems), interfacing systems and adaptive systems • Each system tends to grow and adapt independently. • Functional units develop systems isolated from other units. • Each functional unit develops many databases; personnel has personnel, payroll, medical insurance, pensions, mailing file….

13. Problems • Data Redundancy and Confusion • Duplicate Data in multiple data files. • The same data can have different names, different meanings, different related data in different places. • The same name might be used for different data in different places. • Database confusion makes implementing a SCM, CRM or Enterprise wide system difficult.

14. Problems 2 • Program-Data Dependence • There is a tight relationship between the data in files and the programs using them. • Any changes to the data, results in necessary changes to the programs that use the data. • Maintaining data becomes costly. • Lack of Flexibility • Scheduled reports can easily be generated from the data. • Ad Hoc reports however are costly to generate. While the information is somewhere in the system getting it out is tricky.

15. Problems 3 • Poor Security • Or poor control. • There is now a lot of data in a lot of databases throughout the organisation. It is difficult to control or manage the data – who is accessing what data? • Lack of Data Sharing & Availability • With poor control over data, its difficult to share data between functions. • Accounts might benefit from some data that manufacturing has, etc.

16. DBMS • The DBMS sits between the actual data and the applications which use the data. • This saves the user from needing to understand the actual physical way the data is stored, instead presenting a logical view of it. • The user doesn’t need to know the data definition language, but instead could use a data manipulation language such as SQL. • In reality often the manipulation language is hidden within an application.

17. DBMS Creating & Changing the logical structure of a database Data Definition Database Querying & making changes to the information Data Manipulation Menus, data entry screens, reports and application software Application Generation Who can see what information; methods for backup and recovery Data Administration

18. Hierarchical Database Employee ROOT Compensation Job Assignment Benefits FIRSTCHILD Performance Ratings Salary History Pension History Life Insurance Health SECONDCHILD

19. Hierarchical Data • Suppose from the previous data structure, we wanted to access the salary history for all people with the job title “Assistant”, accessing that data would not be easy. • While certain scheduled reports can be generated, ad hoc reports are not as flexible.

21. Relational Databases • Data is organised into tables, which could be visualised as a spreadsheet. In each table data is organised into rows / records (or tuples). • Any piece of data from any table can be linked to any piece of data in another table, so long as they have a common data element (field).

22. Object-Oriented DB • Hierarchical and Relational databases assume that data is in character or numerical form. • Some databases store data which can’t easily be represented in files and tables (such as graphics, sounds, java applets or any other multimedia). • O-O databases are designed to deal with these diverse data types, however they tend to be a lot slower than relational databases.

23. Data Warehouse • Logical collection of information gathered from many different operational databases. • Used to create business intelligence, assist with analysis and decision making. • Multi-dimensional ‘hypercube’ of information.

24. Data Mining • Query and Reporting Tools • Intelligent Agents • Multidimensional Analysis Tools • Statistical Tools