Data Processing

Computer and Information Technology – Core Module – Data Processing

Computer & Information Technology Data Processing Data vs Information 1. Data: Collection of raw and unorganized facts 2. Information: Organized / Processed / Analyzed / Meaningful Data Data Processing Cycle 1. Data Collection 2. Data Preparation 3. Data Input 4. Data Processing 5. Information Output e.g. Survey on the heights of F.4 students 1. Data Collection: Collect the heights of all students in F.4  2. Data Preparation: Group the heights by class  Check if there is any wrong data  3. Data Input: Input the data into Excel  4. Data Processing: Sort the heights in ascending order  Find the max., min. and mean height of each class  5. Information Output: Show the result on the screen  Print the result on a sheet of paper  Data Control 1. To check if the data to be processed is correct 2. Need of data control: Error Garbage-In-Garbage-Out (GIGO)  Source of Errors  Type Data Source Error Transcription Error

Source Incorrect data from source Misread or mistyped data

Transposition Error

Swapping of characters

Example Intended fake data from interviewee “u” read as “v” “m” typed as “n” “12” typed as “21”

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Computer and Information Technology – Core Module – Data Processing

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Computer and Information Technology – Core Module – Data Processing

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Two types of data control: Data Validation  Check if the data complies with a a set of rules  Common data validation methods: 

Method Presence Check Length Check

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Description Check if the data exists Check if the input data is of a particular length

Tel. no.: 8 numbers

Range Check

Check if the numeric/character data lies within a range

Class: 2 characters Class no: 1 – 40

Format Check

Check if the data fulfill a prescribed format

M.C.: A – D Date: dd/mm/yyyy

Calculate a number by putting the data into a function.

Email: [email protected] HKID Number

Check Digit

Example

The check digit make the data self-checking. i.e. The data can present its validity

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Data Verification Check if the data input matches with the ones on the source document  Common data verification methods:  Method Double entry Input data twice

Description Enter the same data set by two operators Enter the same data set twice e.g. Password confirmation

Data Organization 1. Structure name Chan Tai Man Wong Siu Ming

age 15 14

Tam Ka Ming

15

gender M F

sid S012345 S012378

M

S012784

……

Database: Collections of tables Field: Field Type + Field Length e.g. Gender: character, length = 1

Field Data Record Table

Computer and Information Technology – Core Module – Data Processing

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Key field: A field which can unique identify a particular record in a table Example: HKID number can uniquely identify a person from all HK citizens  Key of a table can be single field, e.g. student_no, or composed of a number  of fields, e.g. class + class_no can unique identify a student in a school In other words, a field cannot be a key if there is possibility of duplicated  occurrence of data ind that field. E.g. Name

Common Database Operations 1. Sorting Arrange the records in ascending / descending order of particular field(s)  E.g. Sort student records in ascending order of their class and class number  Sort key = Class + Class number 2. Filtering / Searching Selecting records according to some rules  E.g. Select all student records where age = 15  3. Merging Combining two or more tables of the same structures  4. Insertion Insert new record(s) into the table  5. Deletion Remove existing record(s) from the table  6. Update Changing existing data  Modes of Operation 1. Comparisons between batch processing and real-time processing Description Feature

Batch Processing Data are collected in batched before processing  Significant delay between data collection and data processing

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information may be outdated Low Relatively low High Payroll System

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Information is always up-to-date High High Low Online game

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Printing academic report

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Banking system

 Cost System Complexity Resource utilization Example

Real-time Processing Data are processed immediately after collection  Very short delay from collection to processing