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List: esb-java-dev
Subject: Re: [Dev] Regarding Proposal 6: [ML] Predictive analytics with online data for WSO2 Machine Learner
From: Maheshakya Wijewardena <maheshakya () wso2 ! com>
Date: 2016-02-29 9:49:50
Message-ID: CAJqB=jdAy+BS62HAxA0xTRFsqEiASLKFMZsVbdUxU-7G4aHejQ () mail ! gmail ! com
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Hi Heshani,
Thank you for the interest for this project.
WSO2 Machine Learner supports several learning algorithms at the moment[1].
This project intends is to leverage the existing algorithms in WSO2 Machine
Learner to support streaming data. As an initiative, first you can get an
idea about what WSO2 Machine Learner does and how it operates. You can
download WSO2 Machine Learner from product page[2] and the the source code
[3]. ML is using Apache Spark MLLib[4] for its' algorithms so it's better
to read and understand what it does as well.
In order to get an idea about the deliverables and the scope of this
project, try to understand how Spark streaming[5] (see examples) handles
streaming data. Also, have a look in the streaming algorithms[6][7]
supported by MLLib. There are two approaches discussed to employ
incremental learning in ML in the project proposals page. These streaming
algorithms can be directly used in the first approach. For the other
approach, the your implementation should contain a procedure to create mini
batches from streaming data with relevant sizes (i.e. a moving window) and
do periodic retraining of the same algorithm.
To start with the project, you will need to come up with a suitable plan
and an architecture first.
Please watch the video referenced in the proposal (reference: 5). It will
help you getting a better idea about machine learning algorithms with
streaming data.
Let us know if you need any help with these.
Best regards
[1] https://docs.wso2.com/display/ML110/Machine+Learner+Algorithms
[2] http://wso2.com/products/machine-learner/
[3]
https://docs.wso2.com/display/ML110/Building+from+Source#BuildingfromSource-Downloadingthesourcecheckout
[4] https://spark.apache.org/docs/1.4.1/mllib-guide.html
[5] https://spark.apache.org/docs/1.4.1/streaming-programming-guide.html
[6]
https://spark.apache.org/docs/1.4.1/mllib-linear-methods.html#streaming-linear-regression
[7]
https://spark.apache.org/docs/1.4.1/mllib-clustering.html#streaming-k-means
On Mon, Feb 29, 2016 at 2:54 PM, Heshani Herath <heshani7.herath@gmail.com>
wrote:
> Hi,
>
>
> https://docs.wso2.com/display/GSoC/Project+Proposals+for+2016#ProjectProposalsfor2016-Proposal6:[ML]PredictiveanalyticswithonlinedataforWSO2MachineLearner
>
> I'm a 4th year undergraduate from SLIIT faculty of computing(specializing
> in Software Engineering) who is interested in doing the aforementioned
> project as the final year research. I would like to know more details on
> this topic and the procedure to be followed when implementing it. Please be
> kind enough to reply as soon as possible.
>
> Thank you
>
> --
> Best Regards,
> Heshani Herath
>
--
Pruthuvi Maheshakya Wijewardena
maheshakya@wso2.com
+94711228855
[Attachment #5 (text/html)]
<div dir="ltr"><div><div><div><div>Hi Heshani,<br><br></div>Thank you for the \
interest for this project. <br><br>WSO2 Machine Learner supports several learning \
algorithms at the moment[1]. This project intends is to leverage the existing \
algorithms in WSO2 Machine Learner to support streaming data. As an initiative, \
first you can get an idea about what WSO2 Machine Learner does and how it operates. \
You can download WSO2 Machine Learner from product page[2] and the the source code \
[3]. ML is using Apache Spark MLLib[4] for its' algorithms so it's better to \
read and understand what it does as well.<br></div><br></div>In order to get an idea \
about the deliverables and the scope of this project, try to understand how Spark \
streaming[5] (see examples) handles streaming data. Also, have a look in the \
streaming algorithms[6][7] supported by MLLib. There are two approaches discussed to \
employ incremental learning in ML in the project proposals page. These streaming \
algorithms can be directly used in the first approach. For the other approach, the \
your implementation should contain a procedure to create mini batches from streaming \
data with relevant sizes (i.e. a moving window) and do periodic retraining of the \
same algorithm. <br><br></div><div>To start with the project, you will need to come \
up with a suitable plan and an architecture \
first.<br></div><div><br></div><div>Please watch the video referenced in the proposal \
(reference: 5). It will help you getting a better idea about machine learning \
algorithms with streaming data.<br></div><div><br></div><div>Let us know if you need \
any help with these.<br><br></div>Best regards<br><br>[1] <a \
href="https://docs.wso2.com/display/ML110/Machine+Learner+Algorithms">https://docs.wso2.com/display/ML110/Machine+Learner+Algorithms</a><br>[2] \
<a href="http://wso2.com/products/machine-learner/">http://wso2.com/products/machine-learner/</a><br>[3] \
<a href="https://docs.wso2.com/display/ML110/Building+from+Source#BuildingfromSource-D \
ownloadingthesourcecheckout">https://docs.wso2.com/display/ML110/Building+from+Source#BuildingfromSource-Downloadingthesourcecheckout</a><br>[4] \
<a href="https://spark.apache.org/docs/1.4.1/mllib-guide.html" \
target="_blank">https://spark.apache.org/docs/1.4.1/mllib-guide.html</a><br>[5] <a \
href="https://spark.apache.org/docs/1.4.1/streaming-programming-guide.html" \
target="_blank">https://spark.apache.org/docs/1.4.1/streaming-programming-guide.html</a><br>[6] \
<a href="https://spark.apache.org/docs/1.4.1/mllib-linear-methods.html#streaming-linear-regression" \
target="_blank">https://spark.apache.org/docs/1.4.1/mllib-linear-methods.html#streaming-linear-regression</a><br>[7] \
<a href="https://spark.apache.org/docs/1.4.1/mllib-clustering.html#streaming-k-means" \
target="_blank">https://spark.apache.org/docs/1.4.1/mllib-clustering.html#streaming-k-means</a></div><div \
class="gmail_extra"><br><div class="gmail_quote">On Mon, Feb 29, 2016 at 2:54 PM, \
Heshani Herath <span dir="ltr"><<a href="mailto:heshani7.herath@gmail.com" \
target="_blank">heshani7.herath@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">Hi,<br><br><a \
href="https://docs.wso2.com/display/GSoC/Project+Proposals+for+2016#ProjectProposalsfor2016-Proposal6:[ML]PredictiveanalyticswithonlinedataforWSO2MachineLearner" \
target="_blank">https://docs.wso2.com/display/GSoC/Project+Proposals+for+2016#ProjectP \
roposalsfor2016-Proposal6:[ML]PredictiveanalyticswithonlinedataforWSO2MachineLearner</a><br><br>I'm \
a 4th year undergraduate from SLIIT faculty of computing(specializing in Software \
Engineering) who is interested in doing the aforementioned project as the final year \
research. I would like to know more details on this topic and the procedure to be \
followed when implementing it. Please be kind enough to reply as soon as \
possible.<br><br>Thank you<span class="HOEnZb"><font color="#888888"><br><br>-- \
<br><div><div dir="ltr">Best Regards,<div>Heshani Herath</div></div></div> \
</font></span></div> </blockquote></div><br><br clear="all"><br>-- <br><div \
class="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div \
dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div \
dir="ltr"><div><div dir="ltr"><span style="font-family:monospace,monospace"><span \
style="color:rgb(153,153,153)">Pruthuvi Maheshakya \
Wijewardena<br></span></span></div><div><span \
style="font-family:monospace,monospace"><span \
style="color:rgb(153,153,153)"></span></span></div><div><span \
style="font-family:monospace,monospace"><span \
style="color:rgb(153,153,153)"></span></span></div><div><span \
style="font-family:monospace,monospace"><span style="color:rgb(153,153,153)"><a \
href="mailto:maheshakya@wso2.com" \
target="_blank">maheshakya@wso2.com</a><br></span></span></div><div><span \
style="color:rgb(153,153,153)"><span \
style="font-family:monospace,monospace">+94711228855</span><i><b><span \
style="font-family:georgia,serif"><br></span></b></i></span></div><div \
dir="ltr"><br></div><div><br></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
</div>
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