# INTRODUCTION

ggregate functions perform a calculation on a set of values and return a single value. Aggregate function avg( ) only calculate average without null values. It provides average result, eliminating null values. Null does not have a value (and is not a member of any data domain) but it is a placeholder or "mark" for missing information. Comparisons with Null can never result in either True or False but always in the third logical result is Unknown. So comparing two null is difficult. We discus about

(1) review of the research for handling null values in database system using aggregate function (2) problem structure with null value with respect to database (3) describes existing solution and proposed solution and its algorithm as well as how it works (4) details the experimental work that has been carried out. The experimental evaluation has been performed using a large amount of datasets.   


# COMPARISON TABLE BETWEEN EXISTING AND PROPOSED SOLUTION

From comparison table we see that our propose system takes less times than existing system. By proposed system can reduce time and reduce the problem of existing system. To understand easily a graph chart is given below.


# VII. GRAPH OF EXISTING SOLUTION VS. PROPOSED SOLUTION

Our propose solution is efficient to calculate average value with Null values from large amount of data.

From the above graph Green bar indicates Existing solution time and Red Bar indicates proposed solution time. We see that in proposed system needs execution time less than existing system. VIII.


# CONCLUSION

At the age of globalization most of all bank already has been computerized. They store their customer information, balance, transaction etc. in database. And they need to calculate average number of transaction after a certain period of time. Even stock exchange Ltd. Hospital, Airlines etc. need to calculate average number of transaction frequently. So our proposed system will be best for them which can save their times.
bankVI.Amount ofExisting solutionProposed SolutionDataExecution Time (sec)Execution Time (sec)400000.28400.2460800000.57200.50401200000.84400.82501600001.18411.15911800001.33011.30112000001.55111.50112200001.75111.57812400001.93711.76412600002.06011.96712800002.18512.0762
			© 2011 Global Journals Inc. (US)
			December
			Global I ) 2011 December ( A New Approach for Calculating Average Value (Including Null) Without Aggregate Function
		
		
			

V.


## PERFORMANCE MEASURE TABLE OF PROPOSED SOLUTION

In this solution we see that if the number of data in database gradually increased then the execution time is increased. Built 
			
			

				


* 
	
		Algorithm Average_WN (d, Avg, size )
		
	




* 
	
		// d is a data table which like two dimensional //array, size is maximum data row
		
	




* 
	
		size of data table
				
	




* 
	
		If( size mod 2=1) 18. { 19. If(size=1) then 20. sum := sum + d
		
			17
		
	




* 
	
		Avg:=sum/size ; 25
		23. } 24
		
	
	Print AVG




* 
	
		A Relational Model of Data for Large Shared Data Banks
		
			EFCodd
		
	
	
		Communications of the ACM
		
			13
			6
			
			June 1970
		
	




* 
	
		December A New Approach for Calculating Average Value (Including Null) Without Aggregate Function 3. Jeff Smith is software developer, he using UNION operator comparing NULL values to other NULLs
		
		
	




* 
	
		Analytic Functions from Oracle® Database SQL Reference 10g Release 1 (10.1) Part Number
		B10759-01
		
	




* 
	
		Incremental computation of nested relational query expression
		
			LBaekgraard
		
		
			LMark
		
	
	
		ACM TODS
		
			20
			2
			
			June 1995
		
	




* 
	
		Comparing Tables By Bill Graziano on 07
		
			January 2002
		
	




* 
	
		SQL Functions from Oracle Database Globalization Support Guide 8
	
	
		Structured Query Language (SQL)". International Business Machines
				
			October 27. 2006. 2007-06-10