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From Pavan K Narayanan <>
Subject Re: Information
Date Tue, 15 Oct 2013 07:14:13 GMT
Based on the information you have provided, street routing is potentially a
Vehicle Routing Problem which is based on TSPs. You can check out the below
Secondly, if you want to use Mahout for Forecasting, it is not possible yet
as the solution methodology for Forecasting (LWR) is still an open problem.

Bottomline: IMHO, you cannot use Mahout for forecasting at the moment; good
luck with your project.

Also, you can explore parallel computing paradigms if you have relatively
high volumes of data.

On 15 October 2013 12:19, Angelo Immediata <> wrote:

> Hi there
> I'm pretty new to learning machine and apache mahout as well so pardon me
> if this question is not too correct :)
> I'm in a street routing project where, beside other functionalities, we
> have to make forecasts. Precisely we should be able in forecasting the
> medium speed in a street in a well know period season (e.g we should be
> able in answering to this kind of question: on the american route 66 what
> will be the medium speed in spring 2015?)
> As far as I know in order to offer this functionality we should use some
> learning machine; this is the reason I'm checking mahout (moreover we need
> to guarantee high performance and since mahout is based on Apache hadoop
> and since it uses Map/Reduce, it seems to me very amazing)
> The first question I'ld love to do is: can I use Apache mahout in order to
> implement the previously written funcionality?
> If I can use it sure I'll need some data in order to "train" mahout....can
> I train mahout in a different time respect to when i need the prevision? I
> mean: can I make the train let's say every week at 10pm and then offer the
> forecasting functionality only when a user is interested in it? Should I
> store the training result in some way?
> And the last, but not the least :), always if I can use mahout....which
> algoritm should I use in order to implement my scenario?
> Thank you for the help and pardon me if i was not too much corrected

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