<div dir="ltr">Hi,<div><br></div><div>There is the <a href="http://holumbus.fh-wedel.de/trac">Holumbus</a> packages. It seems to have a DFS and support for analytics (map/reduce at least).</div><div><br></div><div>Regards</div>
<div><br></div><div>J-C</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Sat, Dec 21, 2013 at 2:50 PM, Alexander Kjeldaas <span dir="ltr"><<a href="mailto:alexander.kjeldaas@gmail.com" target="_blank">alexander.kjeldaas@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><br>In the HPCC documentation it is hard to cut through the buzzword jungle. Is there an efficient storage solution lurking there?<br>
<br>I searched for haskell packages related to the big data storage layer, and the only thing I've found that could support efficient erasure code-based storage is this 3 years old binding to libhdfs. There is only one commit in github:<br>
<br><a href="https://github.com/kim/hdfs-haskell" target="_blank">https://github.com/kim/hdfs-haskell</a><br><br></div><div>Somewhat related are these bindings to zfec, from 2008, and part of the Tahoe LAFS project.<br></div>
<div><br><a href="http://hackage.haskell.org/package/fec" target="_blank">http://hackage.haskell.org/package/fec</a></div><span class="HOEnZb"><font color="#888888">
<div><br><br></div><div>Alexander<br></div><div><br></div></font></span></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><br><div class="gmail_quote">On Fri, Dec 20, 2013 at 8:24 AM, Carter Schonwald <span dir="ltr"><<a href="mailto:carter.schonwald@gmail.com" target="_blank">carter.schonwald@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Cloud Haskell is a substrate that could be used to build such a layer. I'm sure the cloud Haskell people would love such experimenration. <div>
<div><span></span><br><br>On Friday, December 20, 2013, He-chien Tsai wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<p dir="ltr">What I meant is that split the data into several parts,send each splited data to different computers, train them seperately, finally send the results back and combine those results. I didn't mean to use Cloud Haskell.</p>
<p dir="ltr">2013/12/20 上午5:40 於 "jean-christophe mincke" <<a>jeanchristophe.mincke@gmail.com</a>> 寫道：<br>
><br>
> He-Chien Tsai,<br>
><br>
> > its training result is designed for composable<br>
><br>
> Yes it is indeed composable (parallel function of that lib) but parallelizing it on a cluster changes all the type because running on a cluster implies IO.<br>
> Moreover using Cloud Haskell (for instance) implies that: <br>
> 1. training functions should be (serializable) clojures, which can only be defined as module level (not as local -let/where - bindings). <br>
> 2. train is a typeclass function and is not serializable.<br>
><br>
> So the idea behind HLearn are interesting but I do not see how it could be run on a cluster... But, unfortunately, I am not an Haskell expert.<br>
><br>
> What do you think?<br>
><br>
> Regards<br>
><br>
> J-C<br>
><br>
><br>
><br>
> On Thu, Dec 19, 2013 at 6:15 PM, He-chien Tsai <<a>depot051@gmail.com</a>> wrote:<br>
>><br>
>> have you took a look at hlearn and statistics packages? it's even easy to parallellize hlearn on cluster because it's training result is designed for composable, which means you can create two model , train them seperately and finally combine them. you can also use other database such as redis or cassandra,which has haskell binding, as backend. for parallellizing on clusters, hdph is also good.<br>
>><br>
>> I personally prefer python for data science because it has much more mature packages and is more interactive and more effective (not kidding. you can create compiled C for core datas and algorithms by python-like cython and call it from python, and exploit gpus for accelerating by theano) than haskell and scala, spark also has a unfinish python binding.<br>
>><br>
>> 2013/12/18 下午3:41 於 "jean-christophe mincke" <<a>jeanchristophe.mincke@gmail.com</a>> 寫道：<br>
>><br>
>><br>
>> ><br>
>> > Hello Cafe,<br>
>> > <br>
>> > Big Data is a bit trendy these days.<br>
>> > <br>
>> > Does anybody know about plans to develop an Haskell eco-system in that domain?<br>
>> > I.e tools such as Storm or Spark (possibly on top of Cloud Haskell) or, at least, bindings to tools which exist in other languages.<br>
>> > <br>
>> > Thank you<br>
>> > <br>
>> > Regards<br>
>> > <br>
>> > J-C<br>
>> ><br>
>> > _______________________________________________<br>
>> > Haskell-Cafe mailing list<br>
>> > <a>Haskell-Cafe@haskell.org</a><br>
>> > <a href="http://www.haskell.org/mailman/listinfo/haskell-cafe" target="_blank">http://www.haskell.org/mailman/listinfo/haskell-cafe</a><br>
>> ><br>
><br>
><br>
</p>
</blockquote>
</div></div><br>_______________________________________________<br>
Haskell-Cafe mailing list<br>
<a href="mailto:Haskell-Cafe@haskell.org" target="_blank">Haskell-Cafe@haskell.org</a><br>
<a href="http://www.haskell.org/mailman/listinfo/haskell-cafe" target="_blank">http://www.haskell.org/mailman/listinfo/haskell-cafe</a><br>
<br></blockquote></div><br></div>
</div></div><br>_______________________________________________<br>
Haskell-Cafe mailing list<br>
<a href="mailto:Haskell-Cafe@haskell.org">Haskell-Cafe@haskell.org</a><br>
<a href="http://www.haskell.org/mailman/listinfo/haskell-cafe" target="_blank">http://www.haskell.org/mailman/listinfo/haskell-cafe</a><br>
<br></blockquote></div><br></div>