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This was extracted (@ 2017-05-22 18:10) from a list of minutes which have been approved by the Board.
Please Note The Board typically approves the minutes of the previous meeting at the beginning of every Board meeting; therefore, the list below does not normally contain details from the minutes of the most recent Board meeting.

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SystemML

17 May 2017

Report was filed, but display is awaiting the approval of the Board minutes.

27 Feb 2017

SystemML provides declarative large-scale machine learning (ML) that aims at
flexible specification of ML algorithms and automatic generation of hybrid
runtime plans ranging from single node, in-memory computations, to distributed
computations running on Apache Hadoop MapReduce and Apache Spark.

SystemML has been incubating since 2015-11-02.

Three most important issues to address in the move towards graduation:

  1. Grow SystemML community: increase mailing list activity, increase
     adoption of SystemML for scalable machine learning, encourage data
     scientists to adopt DML and PyDML algorithm scripts, respond to user
     feedback to ensure SystemML meets the requirements of real-world
     situations, write papers, and present talks about SystemML.
  2. Continue to produce releases.
  3. Increase the diversity of our project's contributors and committers.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

  NONE.

How has the community developed since the last report?

  Our mailing list from November through January had 249 messages on a
  variety of topics. We gained 2 new contributors to the project since
  November 1. On GitHub, the project has been starred 451 times and forked
  168 times. Elgamal et al wrote the paper "SPOOF: Sum-Product Optimization
  and Operator Fusion for Large-Scale Machine Learning," which was presented
  at CIDR'17 in January.

How has the project developed since the last report?

  The main project has had 114 commits since November 1. 90 pull requests
  have been created since November 1, and 79 pull requests have been closed.
  Since November 1, 143 issues have been reported on our JIRA site and 64 of
  these have been resolved or closed.

Date of last release:

  2016-11-13 (version 0.11.0-incubating)

When were the last committers or PPMC members elected?

 2017-01-19 Nakul Jindal (Committer and PPMC)

Signed-off-by:

  [x](systemml) Henry Saputra
  [x](systemml) Luciano Resende
  [ ](systemml) Patrick Wendell
  [ ](systemml) Reynold Xin
  [ ](systemml) Rich Bowen

16 Nov 2016

SystemML provides declarative large-scale machine learning (ML) that aims at
flexible specification of ML algorithms and automatic generation of hybrid
runtime plans ranging from single node, in-memory computations, to distributed
computations running on Apache Hadoop MapReduce and Apache Spark.

SystemML has been incubating since 2015-11-02.

Three most important issues to address in the move towards graduation:

 - Grow SystemML community: increase mailing list activity,
   increase adoption of SystemML for scalable machine learning, encourage
   data scientists to adopt DML and PyDML algorithm scripts, respond to
   user feedback to ensure SystemML meets the requirements of real-world
   situations, write papers, and present talks about SystemML.
 - Continue to produce releases.
 - Increase the diversity of our project's contributors and committers.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be aware
of?

 NONE.

How has the community developed since the last report?

 Our mailing list from August through October had 375 messages on a wide
 range of topics. We have gained 4 new contributors to the main project since
 August 1st. Our website has been redesigned with the help of several design
 engineers and we have commits from 3 new contributors to the website
 project. On GitHub, the project has been starred 417 times and forked 156
 times.

 Niketan Pansare gave a talk with the title "Apache SystemML - Declarative
 Machine Learning at Scale" on October 7th in the CS graduate seminar at UC
 Merced. Matthias Boehm gave a talk on "Compressed Linear Algebra for Large-
 Scale Machine Learning" at TU Dresden on August 30th. We presented the
 papers "Compressed Linear Algebra for Large-Scale Machine Learning"
 (research paper + poster) and "SystemML: Declarative Machine Learning on
 Spark" (industry paper) at VLDB'16. The "Compressed Linear Algebra for
 Large-Scale Machine Learning" paper won the VLDB 2016 Best Paper Award. We
 gave two 90 minute tutorials at the BOSS'16 workshop, co-located with
 VLDB'16, and our paper "SPOOF: Sum-Product Optimization and Operator Fusion
 for Large-Scale Machine Learning" has been accepted at CIDR'17.

How has the project developed since the last report?

 The main project has had 213 commits since August 1. The website project has
 had 51 commits since August 1. Since August 1, 241 issues have been reported
 on our JIRA site and 137 issues have been resolved or closed. 79 pull
 requests have been created since August 1, and 72 pull requests have been
 closed.

Date of last release:

 2016-06-15 (version 0.10.0-incubating)

When were the last committers or PMC members elected?

 2016-05-07 Glenn Weidner
 2016-05-07 Faraz Makari Manshadi

Signed-off-by:

 [x](systemml) Luciano Resende
 [ ](systemml) Patrick Wendell
 [ ](systemml) Reynold Xin
 [ ](systemml) Rich Bowen

17 Aug 2016

SystemML provides declarative large-scale machine learning (ML) that aims at
flexible specification of ML algorithms and automatic generation of hybrid
runtime plans ranging from single node, in-memory computations, to distributed
computations running on Apache Hadoop MapReduce and Apache Spark.

SystemML has been incubating since 2015-11-02.

Three most important issues to address in the move towards graduation:

 - Grow SystemML community: increase mailing list activity,
   increase adoption of SystemML for scalable machine learning, encourage
   data scientists to adopt DML and PyDML algorithm scripts, respond to
   user feedback to ensure SystemML meets the requirements of real-world
   situations, write papers, and present talks about SystemML.
 - Continue to produce releases.
 - Increase the diversity of our project's contributors and committers.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be aware
of?

 NONE.

How has the community developed since the last report?

 Our mailing list from May through July had 237 messages including a wide
 range of topics. We have gained 6 new contributors since May 1st. On
 GitHub, the project has been starred 365 times and forked 129 times. Fred
 Reiss spoke at Spark Summit West on June 7 about building custom machine
 learning algorithms with SystemML. Mike Dusenberry presented about
 SystemML on May 19 at Datapalooza Denver. Elgohary, Boehm, Haas, Reiss,
 and Reinwald published Compressed Linear Algebra for Large-Scale Machine
 Learning.

How has the project developed since the last report?

 We produced our second Apache release, version 0.10.0-incubating. The
 project has had 205 commits since May 1. 187 issues have been reported on
 our JIRA site and 127 issues have been resolved. 70 pull requests have
 been created since May 1, and 63 of these have been closed.

Date of last release:

 2016-06-15 (version 0.10.0-incubating)

When were the last committers or PMC members elected?

 2016-05-07 Glenn Weidner
 2016-05-07 Faraz Makari Manshadi

Signed-off-by:

 [x](systemml) Luciano Resende
 [ ](systemml) Patrick Wendell
 [ ](systemml) Reynold Xin
 [ ](systemml) Rich Bowen

18 May 2016

SystemML provides declarative large-scale machine learning (ML) that aims at
flexible specification of ML algorithms and automatic generation of hybrid
runtime plans ranging from single node, in-memory computations, to distributed
computations running on Apache Hadoop MapReduce and Apache Spark.

SystemML has been incubating since 2015-11-02.

Three most important issues to address in the move towards graduation:

 - Grow SystemML community: increase mailing list activity,
   increase adoption of SystemML for scalable machine learning, encourage
   data scientists to adopt DML and PyDML algorithm scripts, respond to
   user feedback to ensure SystemML meets the requirements of real-world
   situations, write papers, and present talks about SystemML.
 - Continue to produce releases.
 - Increase the diversity of our project's contributors and committers.

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be aware
of?

 NONE.

How has the community developed since the last report?

 Our mailing list from February through April had 199 messages involving
 topics such as algorithms, DML functionality, usability, and bug fixes. In
 addition, we have had many discussions on our JIRA site and in pull
 request conversations. Fred Reiss presented at Spark Summit East on
 February 17 about SystemML internals. Berthold Reinwald spoke at the Spark
 Technology Center on March 9 about scalable machine learning with
 SystemML. Niketan Pansare spoke on April 28 at Datapalooza in Austin and
 on April 29 at Rice University about declarative machine learning at scale
 with SystemML. Researchers in Germany are working to add Flink as an
 additional SystemML backend. On GitHub, the project has been starred 267
 times and forked 92 times. We have gained two new contributors since
 February 1st.

How has the project developed since the last report?

 We produced our first Apache release, version 0.9.0-incubating. Numerous
 additions have been made to the project, including core functionality,
 usability improvements, and documentation. The project has had 204 commits
 since February 1. In the same time frame, 155 new issues have been
 reported on our JIRA site and 77 issues have been resolved. 114 pull
 requests opened since Febrary 1 have been closed. We are preparing to
 produce our second incubator release.

Date of last release:

 2016-02-15 (version 0.9.0-incubating)

When were the last committers or PMC members elected?

 NONE

Signed-off-by:

 [x](systemml) Luciano Resende
 [ ](systemml) Patrick Wendell
 [ ](systemml) Reynold Xin
 [ ](systemml) Rich Bowen

17 Feb 2016

SystemML provides declarative large-scale machine learning (ML) that aims at
flexible specification of ML algorithms and automatic generation of hybrid
runtime plans ranging from single node, in-memory computations, to
distributed computations running on Apache Hadoop MapReduce and Apache
Spark.

SystemML has been incubating since 2015-11-02.

Three most important issues to address in the move towards graduation:

 - Grow SystemML community: increase mailing list activity,
   increase adoption of SystemML for scalable machine learning, encourage
   data scientists to adopt DML and PyDML algorithm scripts, respond to
   user feedback to ensure SystemML meets the requirements of real-world
   situations, write papers, and present talks about SystemML.
 - Produce a release (in progress).

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

 NONE. We have resolved our blocking infrastructure issues related to
 project JIRA.

How has the community developed since the last report?

 Our mailing list in January had 123 messages (7 of which were Jenkins). We
 have had several useful discussions on the mailing list concerning various
 aspects of SystemML. We have had several additional discussions on our
 JIRA site and in our Pull Request conversations. We have received
 usability feedback which includes filing JIRA issues and mailing list
 activity.

How has the project developed since the last report?

 Numerous additions have been made to the project, including core
 functionality, usability improvements, and documentation. The project has
 had 96 commits since January 6. In the last 30 days, 126 new issues have
 been reported on our JIRA site and 68 issues have been resolved. We are in
 the process of producing our first Apache release, version 0.9.0.  RC3
 passed the SystemML PMC vote and is currently being voted on by the IPMC.

Date of last release:

 NONE

When were the last committers or PMC members elected?

 NONE

Signed-off-by:

 [x](systemml) Luciano Resende
 [ ](systemml) Patrick Wendell
 [ ](systemml) Reynold Xin
 [ ](systemml) Rich Bowen

Shepherd/Mentor notes:

20 Jan 2016

SystemML provides declarative large-scale machine learning (ML) that aims at
flexible specification of ML algorithms and automatic generation of hybrid
runtime plans ranging from single node, in-memory computations, to
distributed computations such as Apache Hadoop MapReduce and Apache Spark.

SystemML has been incubating since 2015-11-02.

Three most important issues to address in the move towards graduation:

 1. Grow SystemML community: increase mailing list activity,
    increase adoption of SystemML for scalable machine learning, encourage
    data scientists to adopt DML and PyDML algorithm scripts, respond to
    user feedback to ensure SystemML meets the requirements of real-world
    situations, write papers, and present talks about SystemML.
 2. Core library improvements, including Apache Spark integration.
 3. Produce a release

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

 The community has been blocked by INFRA-10714 since November.
 Beginning Jan 6th, we are manually updating the missing fields in JIRA so
 that we can properly manage project issues and delegate issues to new
 users to grow our community.

How has the community developed since the last report?

 Users have asked several excellent questions on the dev list, and existing
 committers are actively helping these users. The project is receiving pull
 requests from contributors, committers have discussed these pull requests
 with their contributors, and contributions have been merged into the
 project.  Matthias Boehm has presented talks regarding the SystemML
 Optimizer at TU Dresden, HTW Dresden, and TU Berlin. Fred Reiss presented
 a talk on Dec 8th.

How has the project developed since the last report?

 Numerous core library improvements have been made to project. Additional
 documentation has been created to help new users. The test suite has been
 refactored to increase maintainability and performance. Contributions have
 been made by non-IBM contributors. We are developing our 2016 roadmap,
 as seen in the "[DISCUSS] Project Roadmap" thread on the mailing list at
 <http://s.apache.org/9hK>.

Date of last release:

 NONE

When were the last committers or PMC members elected?

 NONE

Signed-off-by:

 [x](systemml) Luciano Resende
 [ ](systemml) Patrick Wendell
 [ ](systemml) Reynold Xin
 [ ](systemml) Rich Bowen

Shepherd/Mentor notes:

16 Dec 2015

SystemML provides declarative large-scale machine learning (ML) that aims at
flexible specification of ML algorithms and automatic generation of hybrid
runtime plans ranging from single node, in-memory computations, to
distributed computations such as Apache Hadoop MapReduce and Apache Spark.

SystemML has been incubating since 2015-11-02.

Three most important issues to address in the move towards graduation:

 1. Grow SystemML community: increase mailing list activity,
    increase adoption of SystemML for scalable machine learning, encourage
    data scientists to adopt DML and PyDML algorithm scripts, respond to
    user feedback to ensure SystemML meets the requirements of real-world
    situations, write papers, and present SystemML at conferences.
 2. Core library improvements, including Apache Spark integration.
 3. Improved SystemML documentation to lower the learning curve.
 4. Produce a release

Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be
aware of?

 The community is blocked, and expects that the SystemML Apache JIRA
 site will be available shortly, as detailed in INFRA-10714.

How has the community developed since the last report?

 We have started to see few non original committers ask questions on the
 dev list, and the existing committers are actively helping these
 contributors.

How has the project developed since the last report?

 All infrastructure, except for JIRA, are in place and in use by the
 community.

Date of last release:

 NONE

When were the last committers or PMC members elected?

 NONE

Signed-off-by:

 [x](systemml) Luciano Resende
 [ ](systemml) Patrick Wendell
 [ ](systemml) Reynold Xin
 [ ](systemml) Rich Bowen

Shepherd/Mentor notes: