Coding technique for unique data representation.

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Coding technique for unique data representation.

WHY DO WE NEED CODES?

•UNIQUE IDENTIFIER

-Example Roll no instead of name

• CROSS REFERENCING BETWEEN APPLICATIONS

-unique Roll no may be used in examination records, accounts, and health centre

• EFFICIENT STORAGE AND RETRIEVAL

- Codes concise- a long name will have a shorter roll no

WHAT ARE THE REQUIREMENTS OF A GOOD CODE?

•CONCISE - Smallest length to reduce storage and data input effort

•EXPANDABLE - Add new members easily

•MEANINGFUL- Code must convey some information about item being coded

•COMPREHENSIVE - Include all relevant characteristics of item being coded

•PRECISE - Unique, unambiguous code

WHAT METHODS DO WE USE TO CODE

1) SERIAL NO: Assign serial number to each item

2) BLOCK CODES: Blocks of serial numbers assigned to different categories.

3) GROUP CLASSIFICATION CODE- Groups of digits/characters assigned for different characteristics

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CODEERROR DETECTION CODE

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•Incorrect data entry can lead to chaos.Mistakes occur as volume of data processed is large. Therfore its necessary to detect and if possible correct errors in data entry. Error can be detetected by introducing controlled redundancy in codes.

MODULUS 11 CHECK DIGIT SYSTEM

Error detection digit added at the end of a numeric code

Code designed in such a way as to detect all single transcription and single transposition errors which is 95% of all errors

Single transcription error→49687→48687

Single transposition error→45687→48657

Given code 49687 modulus check digit obtained as follows:

Multiply each digit by

Weights of 2,3,4 etc starting with least significant digit

7*2+8*3+6*4+9*5+4*6=131

131/11=11,remainder 10; or 131 mod (11) =10;

(11-10)=1 append it to the code

The code with check digit=496871

If remainder is 1 then append(11-1)=10 code as X

WHY DOES MODULUS 11 CHECK DIGIT WORK

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Use modulo n check with n prime > largest code character value Conditions

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VALIDATING INPUT DATA

When large volume of data is input special precautions are needed to validate data

• validation checks methods:

• sequence numbering - detects missing record

• batch control - use batch totals

• data entry and verification-dual input

• record totals-add individual values for checking

•modulus 11 check digit

CHECKS ON INDIVIDUAL FIELDS

• Radix errors - For example seconds field cannot exceed 60,month field cannot exceed 12

• Range check - Fields should be within specified range

• Reasonableness check - Telephone bill cannot be more than 10 times average bill of last few months

• Inconsistent data - For example : 31-04-99

• Incorrect data- Batch total checks this

• Missing data - Batch control data checks this

• Inter field relationship check - For example - Student of 8lh class cannot have age > 25

REFERENCES

1. Most of the material in this module has been adapted from the book “Analysis and Design of Information Systems”, 2nd Edition, by V.Rajaraman, Prentice Hall of India, 2003. Chapter 5 (pp. 49-52) and Chapter 11 (pp.154-170).

2. Good material on Data Dictionary is found in K.E.Kendall and J.E.Kendall , “Systems Analysis and Design”, 5th Edition, Pearson Education Asia, 2003.Chapter 10 on Data Dictionaries. Chapter 16 Designing Effective Input.

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