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From Henry Saputra <henry.sapu...@gmail.com>
Subject Re: [DISCUSS] [PROPOSAL] Singa for Apache Incubator
Date Fri, 27 Feb 2015 07:42:06 GMT
I was not actually talking about requirement, but for the sake of
podling itself.

If all initial mentors coming from same company, the risk of all of
them absent are greater because all will be subjected to same schedule
and priorities from their daytime employers. Especially for release
VOTEs. Three initial mentors wont be enough for this project, I think.

Not too worries about initial committers coming from same org, but I
have seen that podling that does not have initial community will
struggle to thrive.

Just 2-cents from my experience in incubator.

- Henry

On Thu, Feb 26, 2015 at 11:37 PM, jan i <jani@apache.org> wrote:
> On Friday, February 27, 2015, Henry Saputra <henry.saputra@gmail.com> wrote:
>
>> I am strongly suggest you solicit more (diverse) mentors before start the
>> VOTE.
>>
>> All initial committers are from same org and all initial mentors are
>> from same company (HW).
>
> We do have a requirement for diversity, for me all initial committers from
> the same company is just as big a problem as mentors. when everyone
> involved are from the same company then that signals a serious problem
> which should be addressed before starting a vote.
>
> rgds
> jan i
>
>>
>> I am not sure this is a good start for Apache podling.
>>
>>
>> - Henry
>>
>> On Thu, Feb 26, 2015 at 9:12 AM, Thejas Nair <thejas.nair@gmail.com
>> <javascript:;>> wrote:
>> > The incubator proposal has been updated with the feedback so far.
>> > We have 3 mentors now, but I think it would be good to have additional
>> > mentors. Please let me know if anyone is able to help mentor this
>> > project.
>> >
>> > I am planning to start a vote on the proposal in a day or two.
>> >
>> >
>> > On Fri, Feb 6, 2015 at 5:21 PM,  <ooibc@comp.nus.edu.sg <javascript:;>>
>> wrote:
>> >>
>> >> Regarding the number of users using this project -- at this moment, the
>> >> community is not big.  A few local start-ups have been trying to use it
>> >> (mainly due to announcement in our seminar list), eg. one is using it
>> for
>> >> image recognition (given a phone snapped by a user, it wants to be
>> return
>> >> the same the product, and a list of similar products, such as a luxury
>> bag
>> >> on a passerby).  Researchers from outside of NUS may have been using it
>> >> since we published an application paper on cross domain/modal retrieval
>> in
>> >> VLDB 2014.
>> >>
>> >> We have not announced the project to the outside community yet -- we
>> would
>> >> announce it in dbworld etc in due course.
>> >>
>> >> Thanks and have a good weekend.
>> >>
>> >> regards
>> >> beng chin
>> >>
>> >>>
>> >>> Thanks for the comments and suggestions.
>> >>> With permission from Thejas, I would like to respond to point 2.
>> >>>
>> >>> We have a huge team down at NUS (National University of Singapore) --
>> >>> we have about seven database/data mining data professors (not including
>> >>> those in systems, networking, and machine learning).
>> >>> I myself have nine PhD students in a steady state, and I have a few
>> large
>> >>> grants, with a total budget of about 15 million S$ (~12 million USD),
>> that
>> >>> allows me to hire a number of research fellows and research assistants
>> for
>> >>> the next few years.  In a constant state, I have about 20 people (PhD
>> >>> students/RA/RF) working with me alone.  Other professors have their
own
>> >>> grants (unlike other countries, it is relatively easy to get large
>> grants
>> >>> in Singapore; many overseas Universities, including UIUC, MIT, ETH etc
>> >>> have research labs funded by Singapore Research Foundation [equivalent
>> of
>> >>> NSF]).
>> >>>
>> >>> SINGA is a long term project for us -- while it is a platform as it
>> is, we
>> >>> are using it for healthcare predictive analytics (by working with a
>> >>> hospital associated with the University).  Therefore, we will be
>> working
>> >>> on SINGA, not solely as a distributed DL platform, but as a tool that
>> will
>> >>> enable us to do data analytics on some business domains (eg.
>> healthcase,
>> >>> consumer etc)
>> >>>
>> >>> For the initial set of committers, three are tenured professors, five
>> are
>> >>> students, with 2-5 years to go before they complete their PhD.  Quite
>> >>> often, some would stay back as a research fellow for a couple of years
>> >>> before they start looking for a job outside.  We will work with mentors
>> >>> and new developers (from outside of NUS or Zhejiang University) in
>> >>> enhancing the system.
>> >>>
>> >>> The project should survive in that sense.
>> >>>
>> >>> (I have an on-going project CIIDAA that has been around since 2008;
it
>> was
>> >>> started as another project, epiC,  with a different grant, and then
we
>> >>> continue the development with a new grant for CIIDAA --
>> >>> http://www.comp.nus.edu.sg/~ciidaa/
>> >>> )
>> >>>
>> >>> Thanks.
>> >>>
>> >>> regards
>> >>> beng chin
>> >>> ps: i am not sure if my email will get through to the group.
>> >>>
>> >>>
>> >>> ---------------------------- Original Message
>> ----------------------------
>> >>> Subject: Re: [DISCUSS] [PROPOSAL] Singa for Apache Incubator
>> >>> From:    "Henry Saputra" <henry.saputra@gmail.com <javascript:;>>
>> >>> Date:    Thu, February 5, 2015 2:57 pm
>> >>> To:      "general@incubator.apache.org <javascript:;>" <
>> general@incubator.apache.org <javascript:;>>
>> >>> Cc:      ooibc@comp.nus.edu.sg <javascript:;>
>> >>>
>> --------------------------------------------------------------------------
>> >>>
>> >>> Several comments:
>> >>> -) How many users already using this project? I would reccomend to
>> >>> drop request for singa-user list at the beginning.
>> >>> -) All the initial committers come from university and seemed like
>> >>> some of them already ready to leave university. I am not too sure if
>> >>> this project go survive if all of the inital committers are from
>> >>> university as students.
>> >>> -) Need to solicit more mentors if this project ever get to Apache
>> >>> incubator.
>> >>>
>> >>> - Henry
>> >>>
>> >>> On Tue, Feb 3, 2015 at 3:58 PM, Thejas Nair <thejas.nair@gmail.com
>> <javascript:;>> wrote:
>> >>>> The "Relationship with Other Apache Products" section has been
>> >>>> updated. The reference to H2O in that section has been removed,
and
>> >>>> other projects have been added.
>> >>>>  Thanks for the feedback!
>> >>>>
>> >>>>
>> >>>> On Wed, Jan 28, 2015 at 10:27 AM, Thejas Nair <thejas.nair@gmail.com
>> <javascript:;>>
>> >>> wrote:
>> >>>>> Thanks for pointing that out Henry! Yes, looks like H20 is not
an
>> >>>>> apache project, I should have verified that.
>> >>>>> I will edit that, and revisit that section along with the folks
in
>> >>>>> Singa community.
>> >>>>>
>> >>>>>
>> >>>>> On Tue, Jan 27, 2015 at 6:55 PM, Henry Saputra
>> >>> <henry.saputra@gmail.com <javascript:;>> wrote:
>> >>>>>> Quick immediate comment that "Apache H2O" is not really
Apache
>> >>>>>> project.
>> >>>>>>
>> >>>>>> I assume you are referring to https://github.com/h2oai/h2o
(or
>> >>>>>> https://github.com/h2oai/h2o-dev) ?
>> >>>>>>
>> >>>>>> - Henry
>> >>>>>>
>> >>>>>> On Tue, Jan 27, 2015 at 5:29 PM, Thejas Nair <thejas.nair@gmail.com
>> <javascript:;>>
>> >>> wrote:
>> >>>>>>> Hello everyone,
>> >>>>>>>
>> >>>>>>> I would like to propose the inclusion of Singa as an
Apache
>> Incubator
>> >>> project.
>> >>>>>>>
>> >>>>>>> Here is the proposal -
>> >>>>>>> https://wiki.apache.org/incubator/SingaProposal
>> >>>>>>>
>> >>>>>>> Please review the proposal and give feedback. I am planning
to
>> start
>> >>>>>>> a
>> >>>>>>> vote after 7 days if the proposal looks good.
>> >>>>>>> We are also seeking additional Apache mentors for the
project.
>> >>>>>>>
>> >>>>>>> Thanks,
>> >>>>>>> Thejas
>> >>>>>>> ==========================================================
>> >>>>>>> Singa Incubator Proposal
>> >>>>>>>
>> >>>>>>> Abstract
>> >>>>>>>
>> >>>>>>> SINGA is a distributed deep learning platform.
>> >>>>>>>
>> >>>>>>> Proposal
>> >>>>>>>
>> >>>>>>> SINGA is an efficient, scalable and easy-to-use distributed
>> platform
>> >>>>>>> for training deep learning models, e.g., Deep Convolutional
Neural
>> >>>>>>> Network and Deep Belief Network. It parallelizes the
computation
>> >>>>>>> (i.e., training) onto a cluster of nodes by distributing
the
>> training
>> >>>>>>> data and model automatically to speed up the training.
Built-in
>> >>>>>>> training algorithms like Back-Propagation and Contrastive
>> Divergence
>> >>>>>>> are implemented based on common abstractions of deep
learning
>> models.
>> >>>>>>> Users can train their own deep learning models by simply
>> customizing
>> >>>>>>> these abstractions like implementing the Mapper and
Reducer in
>> >>>>>>> Hadoop.
>> >>>>>>>
>> >>>>>>> Background
>> >>>>>>>
>> >>>>>>> Deep learning refers to a set of feature (or representation)
>> learning
>> >>>>>>> models that consist of multiple (non-linear) layers,
where
>> different
>> >>>>>>> layers learn different levels of abstractions (representations)
of
>> >>>>>>> the
>> >>>>>>> raw input data. Larger (in terms of model parameters)
and deeper
>> (in
>> >>>>>>> terms of number of layers) models have shown better
performance,
>> >>>>>>> e.g.,
>> >>>>>>> lower image classification error in Large Scale Visual
Recognition
>> >>>>>>> Challenge. However, a larger model requires more memory
and larger
>> >>>>>>> training data to reduce over-fitting. Complex numeric
operations
>> make
>> >>>>>>> the training computation intensive. In practice, training
large
>> deep
>> >>>>>>> learning models takes weeks or months on a single node
(even with
>> >>>>>>> GPU).
>> >>>>>>>
>> >>>>>>> Rational
>> >>>>>>>
>> >>>>>>> Deep learning has gained a lot of attraction in both
academia and
>> >>>>>>> industry due to its success in a wide range of areas
such as
>> computer
>> >>>>>>> vision and speech recognition. However, training of
such models is
>> >>>>>>> computationally expensive, especially for large and
deep models
>> >>>>>>> (e.g.,
>> >>>>>>> with billions of parameters and more than 10 layers).
Both Google
>> and
>> >>>>>>> Microsoft have developed distributed deep learning systems
to make
>> >>>>>>> the
>> >>>>>>> training more efficient by distributing the computations
within a
>> >>>>>>> cluster of nodes. However, these systems are closed
source
>> softwares.
>> >>>>>>> Our goal is to leverage the community of open source
developers to
>> >>>>>>> make SINGA efficient, scalable and easy to use. SINGA
is a full
>> >>>>>>> fledged distributed platform, that could benefit the
community and
>> >>>>>>> also benefit from the community in their involvement
in
>> contributing
>> >>>>>>> to the further work in this area. We believe the nature
of SINGA
>> and
>> >>>>>>> our visions for the system fit naturally to Apache's
philosophy and
>> >>>>>>> development framework.
>> >>>>>>>
>> >>>>>>> Initial Goals
>> >>>>>>>
>> >>>>>>> We have developed a system for SINGA running on a commodity
>> computer
>> >>>>>>> cluster. The initial goals include, * improving the
system in terms
>> >>>>>>> of
>> >>>>>>> scalability and efficiency, e.g., using Infiniband for
network
>> >>>>>>> communication and multi-threading for one node computation.
We
>> would
>> >>>>>>> consider extending SINGA to GPU clusters later. * benchmarking
with
>> >>>>>>> larger datasets (hundreds of millions of training instances)
and
>> >>>>>>> models (billions of parameters). * adding more built-in
deep
>> learning
>> >>>>>>> models. Users can train the built-in models on their
datasets
>> >>>>>>> directly.
>> >>>>>>>
>> >>>>>>> Current Status
>> >>>>>>>
>> >>>>>>> Meritocracy
>> >>>>>>>
>> >>>>>>> We would like to follow ASF meritocratic principles
to encourage
>> more
>> >>>>>>> developers to contribute in this project. We know that
only active
>> >>>>>>> and
>> >>>>>>> excellent developers can make SINGA a successful project.
The
>> >>>>>>> committer list and PMC will be updated based on developers'
>> >>>>>>> performance and commitment. We are also improving the
documentation
>> >>>>>>> and code to help new developers get started quickly.
>> >>>>>>>
>> >>>>>>> Community
>> >>>>>>>
>> >>>>>>> SINGA is currently being developed in the Database System
Research
>> >>>>>>> Lab
>> >>>>>>> at the National University of Singapore (NUS) in collaboration
with
>> >>>>>>> Zhejiang University in China. Our lab has extensive
experience in
>> >>>>>>> building database related systems, including distributed
systems.
>> Six
>> >>>>>>> PhD students and research assistants (Jinyang Gao, Kaiping
Zheng,
>> >>>>>>> Sheng Wang, Wei Wang, Zhaojing Luo and Zhongle Xie)
, a research
>> >>>>>>> fellow (Anh Dinh) and three professors (Beng Chin Ooi,
Gang Chen,
>> >>>>>>> Kian
>> >>>>>>> Lee Tan) have been working for a year on this project.
We are open
>> to
>> >>>>>>> recruiting more developers from diverse backgrounds.
>> >>>>>>>
>> >>>>>>> Core Developers
>> >>>>>>>
>> >>>>>>> Beng Chin Ooi, Gang Chen and Kian Lee Tan are professors
who have
>> >>>>>>> worked on distributed systems for more than 20 years.
They have
>> >>>>>>> collaborated with the industry and have built various
large scale
>> >>>>>>> systems. Anh Dinh's research is also on distributed
systems, albeit
>> >>>>>>> with more focus on security aspects. Wei Wang's research
is on deep
>> >>>>>>> learning problems including deep learning applications
and large
>> >>>>>>> scale
>> >>>>>>> training. Sheng Wang and Jinyang are working on efficient
indexing,
>> >>>>>>> querying of large scale data and machine learning. Kaiping,
>> Zhaojing
>> >>>>>>> and Zhongle are new PhD students who jointed SINGA recently.
They
>> >>>>>>> will
>> >>>>>>> work on this project for a longer time (next 4-5 years).
While we
>> >>>>>>> share common research interests, each member also brings
diverse
>> >>>>>>> expertise to the team.
>> >>>>>>>
>> >>>>>>> Alignment
>> >>>>>>>
>> >>>>>>> ASF is already the home of many distributed platforms,
e.g.,
>> Hadoop,
>> >>>>>>> Spark and Mahout, each of which targets a different
application
>> >>>>>>> domain. SINGA, being a distributed platform for large-scale
deep
>> >>>>>>> learning, focuses on another important domain for which
there still
>> >>>>>>> lacks a robust and scalable open-source platform. The
recent
>> success
>> >>>>>>> of deep learning models especially for vision and speech
>> recognition
>> >>>>>>> tasks has generated interests in both applying existing
deep
>> learning
>> >>>>>>> models and in developing new ones. Thus, an open-source
platform
>> for
>> >>>>>>> deep learning will be able to attract a large community
of users
>> and
>> >>>>>>> developers. SINGA is a complex system needing many iterations
of
>> >>>>>>> design, implementation and testing. Apache's collaboration
>> framework
>> >>>>>>> which encourages active contribution from developers
will
>> inevitably
>> >>>>>>> help improve the quality of the system, as shown in
the success of
>> >>>>>>> Hadoop, Spark, etc.. Equally important is the community
of users
>> >>>>>>> which
>> >>>>>>> helps identify real-life applications of deep learning,
and helps
>> to
>> >>>>>>> evaluate the system's performance and ease-of-use. We
hope to
>> >>>>>>> leverage
>> >>>>>>> ASF for coordinating and promoting both communities,
and in return
>> >>>>>>> benefit the communities with another useful tool.
>> >>>>>>>
>> >>>>>>> Known Risks
>> >>>>>>>
>> >>>>>>> Orphaned products
>> >>>>>>>
>> >>>>>>> Four core developers (Anh, Wei Wang, Jinyang and Sheng
Wang) may
>> >>>>>>> leave
>> >>>>>>> the lab in two to four years time. It is possible that
some of them
>> >>>>>>> may not have enough time to focus on this project after
that. But,
>> >>>>>>> SINGA is part of our other bigger research projects
on building an
>> >>>>>>> infrastructure for data intensive applications, which
include
>> >>>>>>> health-care analytics and brain-inspired computing.
Beng Chin and
>> >>>>>>> Kian
>> >>>>>>> Lee would continue working on it and getting more people
involved.
>> >>>>>>> For
>> >>>>>>> example, three new developers (Kaiping, Zhaojing and
Zhongle)
>> joined
>> >>>>>>> us recently. Individual developers are welcome to make
SINGA a
>> >>>>>>> diverse
>> >>>>>>> community that is robust and independent from any single
developer.
>> >>>>>>>
>> >>>>>>> Inexperience with Open Source
>> >>>>>>>
>> >>>>>>> All the developers are active users and followers of
open source
>> >>>>>>> projects. Our research lab has a strong commitment to
open source,
>> >>>>>>> and
>> >>>>>>> has released the source code of several systems under
open source
>> >>>>>>> license as a way of contributing back to the open source
community.
>> >>>>>>> But we do not have much real experience in open source
projects
>> with
>> >>>>>>> large and well organized communities like those in Apache.
This is
>> >>>>>>> one
>> >>>>>>> reason we choose Apache which is experienced in open
source project
>> >>>>>>> incubation. We hope to get the help from Apache (e.g.,
champion and
>> >>>>>>> mentors) to establish a healthy path for SINGA.
>> >>>>>>>
>> >>>>>>> Homogenous Developers
>> >>>>>>>
>> >>>>>>> Although the current developers are researchers in the
>> universities,
>> >>>>>>> they have different research interests and project experiences,
as
>> >>>>>>> mentioned in the section that introduces the core developers.
We
>> know
>> >>>>>>> that a diverse community is helpful. Hence we are open
to the idea
>> of
>> >>>>>>> recruiting developers from other regions and organizations.
>> >>>>>>>
>> >>>>>>> Reliance on Salaried Developers
>> >>>>>>>
>> >>>>>>> As a research project in the university, SINGA's current
developing
>> >>>>>>> community consists of professors, PhD students, research
assistants
>> >>>>>>> and postdoctoral fellows. They are driven by their interests
to
>> work
>> >>>>>>> on this project and have contributed actively since
the start of
>> the
>> >>>>>>> project. The research assistants and fellows are expected
to leave
>> >>>>>>> when their contracts expire. However, they are keen
to continue to
>> >>>>>>> work on the project voluntarily. Moreover, as a long
term research
>> >>>>>>> project, new research assistants and fellows are likely
to join the
>> >>>>>>> project.
>> >>>>>>>
>> >>>>>>> A Excessive Fascination with the Apache Brand
>> >>>>>>>
>> >>>>>>> We choose Apache not for publicity. We have two purposes.
First, we
>> >>>>>>> want to leverage Apache's reputation to recruit more
developers to
>> >>>>>>> make a diverse community. Second, we hope that Apache
can help us
>> to
>> >>>>>>> establish a healthy path in developing SINGA. Beng Chin
and
>> Kian-Lee
>> >>>>>>> are established database and distributed system researchers,
and
>> >>>>>>> together with the other contributors, they sincerely
believe that
>> >>>>>>> there is a need for a widely accepted open source distributed
deep
>> >>>>>>> learning platform. The field of deep learning is still
at its
>> >>>>>>> infancy,
>> >>>>>>> and an open source platform will fuel the research in
the area.
>> >>>>>>> Moreover, such a platform will enable researchers to
develop new
>> >>>>>>> models and algorithms, rather than spending time implementing
a
>> deep
>> >>>>>>> learning system from scratch. Furthermore, the need
for scalability
>> >>>>>>> for such a platform is obvious.
>> >>>>>>>
>> >>>>>>> Relationship with Other Apache Products
>> >>>>>>>
>> >>>>>>> Apache H2O implemented two simple deep learning models,
namely the
>> >>>>>>> Multi-Layer Perceptron and Deep Auto-encoders. There
are two
>> >>>>>>> significant differences between H2O and SINGA. First,
H2O adopts
>> the
>> >>>>>>> Map-Reduce framework which runs a set of computing nodes
in
>> parallel
>> >>>>>>> againsts of the training set. Model parameters trained
by all
>> >>>>>>> computing nodes are averaged as the final model parameters.
This
>> >>>>>>> training algorithm is different from the distributed
training
>> >>>>>>> algorithm used by DistBelief, Adam and SINGA, which
frequently
>> >>>>>>> synchronizes the parameters trained from different nodes.
SINGA
>> >>>>>>> adopts
>> >>>>>>> the parameter server framework to support a wide range
of
>> distributed
>> >>>>>>> training algorithms and parallelization methods (e.g.,
data
>> >>>>>>> parallelism, model parallelism and hybrid parallelism.
H2O only
>> >>>>>>> support data parallelism) . Second, in H2O, users are
restricted to
>> >>>>>>> use the two built-in models. In SINGA, we provide simple
>> programming
>> >>>>>>> model to let users implement their own deep learning
models. A new
>> >>>>>>> deep learning model can be implemented by customizing
the base
>> Layer
>> >>>>>>> class for each layer involved in the model. It is similar
to
>> writing
>> >>>>>>> Hadoop programs where users only need to override the
base Mapper
>> and
>> >>>>>>> Reducer. We also provide built-in models for users to
use directly.
>> >>>>>>>
>> >>>>>>> Documentation
>> >>>>>>>
>> >>>>>>> The project is hosted at
>> >>>>>>> http://www.comp.nus.edu.sg/~dbsystem/project/singa.html.
>> >>>>>>> Documentations can be found at the Github Wiki Page:
>> >>>>>>> https://github.com/nusinga/singa/wiki. We continue to
refine and
>> >>>>>>> improve the documentation.
>> >>>>>>>
>> >>>>>>> Initial Source
>> >>>>>>>
>> >>>>>>> We use Github to maintain our source code,
>> >>> https://github.com/nusinga/singa
>> >>>>>>>
>> >>>>>>> Source and Intellectual Property Submission Plan
>> >>>>>>>
>> >>>>>>> We plan to make our code base be under Apache License,
Version 2.0.
>> >>>>>>>
>> >>>>>>> External Dependencies
>> >>>>>>>
>> >>>>>>> required by the core code base: glog, gflags, google
protobuf,
>> >>>>>>> open-blas, mpich, armci-mpi.
>> >>>>>>> required by data preparation and preprocessing: opencv,
hdfs,
>> python.
>> >>>>>>>
>> >>>>>>> Cryptography
>> >>>>>>>
>> >>>>>>> Not Applicable
>> >>>>>>>
>> >>>>>>> Required Resources
>> >>>>>>>
>> >>>>>>> Mailing Lists
>> >>>>>>>
>> >>>>>>> Currently, we use google group for internal discussion.
The mailing
>> >>>>>>> address is nusinga@googlegroup.com <javascript:;>.
We will
>> migrate the content to
>> >>>>>>> the
>> >>>>>>> apache mailing lists in the future.
>> >>>>>>>
>> >>>>>>> singa-dev
>> >>>>>>> singa-user
>> >>>>>>> singa-commits
>> >>>>>>> singa-private (for private discussion within PCM)
>> >>>>>>>
>> >>>>>>> Git Repository
>> >>>>>>>
>> >>>>>>> We want to continue using git for version control. Hence,
a git
>> repo
>> >>>>>>> is required.
>> >>>>>>>
>> >>>>>>> Issue Tracking
>> >>>>>>>
>> >>>>>>> JIRA Singa (SINGA)
>> >>>>>>>
>> >>>>>>> Initial Committers
>> >>>>>>>
>> >>>>>>> Beng Chin Ooi (ooibc @comp.nus.edu.sg)
>> >>>>>>> Kian Lee Tan (tankl @comp.nus.edu.sg)
>> >>>>>>> Gang Chen (cg @zju.edu.cn)
>> >>>>>>> Wei Wang (wangwei @comp.nus.edu.sg)
>> >>>>>>> Dinh Tien Tuan Anh (dinhtta @comp.nus.edu.sg)
>> >>>>>>> Jinyang Gao (jinyang.gao @comp.nus.edu.sg)
>> >>>>>>> Sheng Wang (wangsh @comp.nus.edu.sg)
>> >>>>>>> Kaiping Zheng (kaiping @comp.nus.edu.sg)
>> >>>>>>> Zhaojing Luo (zhaojing @comp.nus.edu.sg)
>> >>>>>>> Zhongle Xie (zhongle @comp.nus.edu.sg)
>> >>>>>>>
>> >>>>>>> Affiliations
>> >>>>>>>
>> >>>>>>> Beng Chin Ooi, National University of Singapore
>> >>>>>>> Kian Lee Tan, National University of Singapore
>> >>>>>>> Gang Chen, Zhejiang University
>> >>>>>>> Wei Wang, National University of Singapore
>> >>>>>>> Dinh Tien Tuan Anh, National University of Singapore
>> >>>>>>> Jinyang Gao, National University of Singapore
>> >>>>>>> Sheng Wang, National University of Singapore
>> >>>>>>> Kaiping Zheng, National University of Singapore
>> >>>>>>> Zhaojing Luo, National University of Singapore
>> >>>>>>> Zhongle Xie, National University of Singapore
>> >>>>>>>
>> >>>>>>> Sponsors
>> >>>>>>>
>> >>>>>>> Champion
>> >>>>>>>
>> >>>>>>> Thejas Nair (thejas at apache.org) - Hortonworks
>> >>>>>>>
>> >>>>>>> Nominated Mentors
>> >>>>>>>
>> >>>>>>> Thejas Nair (thejas at apache.org) - Hortonworks
>> >>>>>>> Alan Gates (gates at apache dot org) - Hortonworks
>> >>>>>>> (Seeking more volunteers!)
>> >>>>>>>
>> >>>>>>> Sponsoring Entity
>> >>>>>>>
>> >>>>>>> We are requesting the Incubator to sponsor this project.
>> >>>>>>>
>> >>>>>>>
>> ---------------------------------------------------------------------
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>> <javascript:;>
>> >>>>>>> For additional commands, e-mail: general-help@incubator.apache.org
>> <javascript:;>
>> >>>>>>>
>> >>>>>>
>> >>>>>>
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>> >>>
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> --
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