thanks Michael . After creating Model i will share

On Wed, May 21, 2014 at 2:29 PM, Michael Hunger <
[email protected]> wrote:

> Perhaps you can start by modelling that in a GraphGist with Cypher Queries
> for the use-cases. And then we can discuss it on these concrete examples?
>
> See: http://www.neo4j.org/learn/graphgist
> And gist.neo4j.org -> examples list on the right
>
>
> On Wed, May 21, 2014 at 10:53 AM, Navrattan Yadav <
> [email protected]> wrote:
>
>> From data i want to calculate :
>> 1. which location is most visited by user.
>> 2. which path is most visited by users
>> 3. also need  track  a user : path or location visited by him.
>> 4. Also need to track how muck time a user spent on a location
>>
>>
>>
>>
>> On Wed, May 21, 2014 at 2:19 PM, Michael Hunger <
>> [email protected]> wrote:
>>
>>> Depends on your use-case, what do you want to do with the data? If you
>>> just want to store the fact that the user was there a relationship is good
>>> enough. If the actual visit is a really important concept in your domain
>>> that also other things are linked to and which has its own identity then a
>>> node would be better.
>>>
>>>
>>> On Wed, May 21, 2014 at 10:38 AM, Navrattan Yadav <
>>> [email protected]> wrote:
>>>
>>>> hi . i am using Neo4j 2.0.3 version.
>>>>  i am working on a DB where we need to track user path based on some
>>>> fixed location.
>>>>
>>>> like which location is most visited.
>>>>
>>>> so what i am doing :
>>>> 1. Each user has unique node.
>>>> 2. Each location has unique node.
>>>>
>>>> when user traverse a location then which is better option :
>>>>  1. create a relation b/w location node and user node . relation name
>>>> is : username_time :  so when ever a user traverse a location a new
>>>> relation is created.
>>>>   or
>>>>  2. create a node new node which has user info and location info and
>>>> time. so when ever a user traverse a location a new node is created.
>>>>    or
>>>> any better idea...  Any suggestion would be highly appreciated.
>>>>
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>>>
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>>
>>
>>
>> --
>> *Thanks and Regards*
>>
>> *Navrattan Yadav*
>>
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*Thanks and Regards*

*Navrattan Yadav*

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