Depends on your use-case queries :)

Perhaps you can explain the use cases

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Am 11.08.2014 um 16:44 schrieb Alireza Rezaei Mahdiraji <[email protected]>:

> 
> Hi Michael,
> 
> Yes, that is right, in a simpler case there are no h value and we only have t 
> values. 
> Do you have any suggestions how to model it?
> 
> Thanks,
> Best,
> Alireza 
> 
> 
> On Monday, August 11, 2014 2:49:26 PM UTC+2, Michael Hunger wrote:
>> 
>> It sounds a bit like multiple concrete expressions of a meta model though.
>> 
>> Where the potential set of values is your hX and tX is expressed in your 
>> concrete data.
>> 
>> So you have multiple dimensions (one of which is time) which are then 
>> expressed in concrete data.
>> 
>> 
>> On Sun, Aug 10, 2014 at 11:20 PM, Alireza Rezaei Mahdiraji 
>> <[email protected]> wrote:
>>> 
>>> Hi All, 
>>> 
>>> I saw documentation about how to model time series in graph but my problem 
>>> seems 
>>> does not fall into that meta models.
>>> 
>>> Here is a description: I have a graph with a fixed connectivity, however 
>>> based on variable
>>> let say h  with values <h1,h2,..hn> some of internal properties of the 
>>> graph nodes changes, i.e.
>>> for h=h1 a node have value x1 for property x but for h=h2 the value of x 
>>> for the node is x2. 
>>> 
>>> Moreover, there are other data (properties in  graph terminology) for nodes 
>>> which are 
>>> based on time, i.e., I have a set of timestamps t=<t1,t2,t3, ...,tn> and 
>>> for each 
>>> timestamp and for each value of h, each node have one or a vector of data. 
>>> 
>>> This nested data structure seems very hard to model with graph. Does 
>>> anybody has 
>>> ideas on how to model this?
>>> 
>>> Thanks,
>>> Best,
>>> Alireza
>>> 
>>> 
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