Going by the hint given in the problem

Divide the stream into windows and assume that we have enough space to store
all the queries from the stream window in a hash table along frequency
count. Also maintain a global Hash table that will contain the frequency
counts of top n queries seen so far. Make sure that n>>100 .Once the window
is filled with enough queries, merge them into the global hash table. If a
query is already available in the global hash table update the frequency
count.Again this solution cannot be accurate due to limited memory and it
allows for some error. larger the value of n lesser the error proneness.

On Thu, Feb 3, 2011 at 9:51 PM, Srikar <srikar2...@gmail.com> wrote:

> @algoose I see what you are saying. what do you propose? checking out your
> link now...
>
>
>
> On Thu, Feb 3, 2011 at 11:44 AM, Algoose chase <harishp...@gmail.com>wrote:
>
>> @Srikar
>>
>> In your first approach you cant simply ignore the queries that are not
>> present in the heap because you have a stream of queries and you never know
>> if the query that you are about to ignore is going be received frequently or
>> not in future. Your approach is like find the top 100 queries from the
>> stream and keep updating the frequencies of only those queries using heap
>> and hash table. If you have to process some 1,00,000 , with a space for only
>> 100 elements you cant find the frequencies correctly.
>>
>> this is a nice article related to this :
>> http://www.americanscientist.org/issues/pub/the-britney-spears-problem
>>
>>
>>
>> On Tue, Feb 1, 2011 at 8:09 PM, sankalp srivastava <
>> richi.sankalp1...@gmail.com> wrote:
>>
>>> @guy above juver++
>>> The solution , i don't think can get better than this , because you
>>> need to store the querries anyway (at least for the output )
>>>
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