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From Janardhan Pulivarthi <janardhan.pulivar...@gmail.com>
Subject Re: List of SystemML research papers?
Date Fri, 02 Jun 2017 06:21:17 GMT
Hello @deroneriksson & other developers,

My name's Janardhan, (@p-janardhan) currently working on SystemML-1437.

So far, I found the following research papers & patents (of 413 google
scholar search results.). This list includes research by IBM teams and the
before work by other researchers.

Performance Related:

   1.

   Elgohary, Ahmed, et al. "Compressed linear algebra for large-scale
   machine learning.
   <http://delivery.acm.org/10.1145/3000000/2994515/p960-elgohary.pdf>"
Proceedings
   of the VLDB Endowment 9.12 (2016): 960-971.



   1.

   Ghoting, Amol, et al. "SystemML: Declarative machine learning on
   MapReduce. <http://people.cs.uchicago.edu/%7Evikass/SystemML.pdf>" Data
   Engineering (ICDE), 2011 IEEE 27th International Conference on. IEEE,
   2011.



   1.

   Boehm, Matthias, et al. "Hybrid parallelization strategies for
   large-scale machine learning in SystemML.
   <http://ai2-s2-pdfs.s3.amazonaws.com/290c/735c8e3e2ffe896d80ea379e48b8177a7f39.pdf>"
   Proceedings of the VLDB Endowment 7.7 (2014): 553-564.



   1.

   Y. Tian, S. Tatikonda and B. Reinwald, "Scalable and Numerically Stable
   Descriptive Statistics in SystemML
   <http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6228204&isnumber=6228063>,"
   2012 IEEE 28th International Conference on Data Engineering, Washington,
   DC, 2012, pp. 1351-1359.



   1.

   Boehm, Matthias, et al. "SystemML: declarative machine learning on spark.
   <http://www.vldb.org/pvldb/vol9/p1425-boehm.pdf>" Proceedings of the
   VLDB Endowment 9.13 (2016): 1425-1436.



   1.

   Boehm, Matthias, et al. "Compiling machine learning algorithms with
   systemml.
   <https://www.researchgate.net/profile/Yuanyuan_Tian/publication/259366998_Compiling_Machine_Learning_Algorithms_with_SystemML/links/00b7d52b4e08f03102000000.pdf>"
   Proceedings of the 4th annual Symposium on Cloud Computing. ACM, 2013.



   1.

   Kirchner, Peter D., et al. "Large scale discriminative metric learning.
   <http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6969574>" Parallel
   & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE
   International. IEEE, 2014.



   1.

   Boehm, Matthias, et al. "Declarative Machine Learning-A Classification
   of Basic Properties and Types. <https://arxiv.org/pdf/1605.05826.pdf>" arXiv
   preprint arXiv:1605.05826 (2016).



   1.

   Boehm, Matthias, et al. "SystemML's Optimizer: Plan Generation for
   Large-Scale Machine Learning Programs.
   <https://www.researchgate.net/profile/Yuanyuan_Tian/publication/266911194_SystemML%27s_Optimizer_Plan_Generation_for_Large-Scale_Machine_Learning_Programs/links/543ec4290cf2e76f02243522/SystemMLs-Optimizer-Plan-Generation-for-Large-Scale-Machine-Learning-Programs.pdf>"
   IEEE Data Eng. Bull. 37.3 (2014): 52-62.



Architecture Related

   1.

   Shin, Sungho, et al. "Platform to build the knowledge base by combining
   sensor data and context data.
   <http://downloads.hindawi.com/journals/ijdsn/2014/542764.pdf>" International
   Journal of Distributed Sensor Networks 10.1 (2014): 542764.



DML

   1.

   Kunft, Andreas, et al. "Bridging the gap: towards optimization across
   linear and relational algebra.
   <http://delivery.acm.org/10.1145/2930000/2926540/a1-kunft.pdf?ip=14.139.60.12&id=2926540&acc=ACTIVE%20SERVICE&key=045416EF4DDA69D9%2E9EE979152FC58D87%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=943555920&CFTOKEN=61064007&__acm__=1496380938_a922fbde5841174577cea997d3db01ae>"
   Proceedings of the 3rd ACM SIGMOD Workshop on Algorithms and Systems for
   MapReduce and Beyond. ACM, 2016.



   1.

   Boehm, Matthias. "Costing generated runtime execution plans for
   large-scale machine learning programs.
   <https://arxiv.org/pdf/1503.06384.pdf>" arXiv preprint arXiv:1503.06384
   (2015).





Matrix Operations Related

   1.

   ULDE, AHMED ABDUL HAMEED. PERFORMANCE EVALUATION OF MATRIX OPERATIONS ON
   MAP-REDUCE QUERY LANGUAGE
   <https://uta-ir.tdl.org/uta-ir/bitstream/handle/10106/25879/ULDE-THESIS-2016.pdf?sequence=1&isAllowed=y>.
   Diss. UNIVERSITY OF TEXAS AT ARLINGTON, 2016.



   1.

   Yu, Lele, Yingxia Shao, and Bin Cui. "Exploiting matrix dependency for
   efficient distributed matrix computation.
   <http://delivery.acm.org/10.1145/2730000/2723712/p93-yu.pdf>" Proceedings
   of the 2015 ACM SIGMOD International Conference on Management of Data.
   ACM, 2015.



Alternatively where SystemML can be used:

   1.

   Marten, Dennis, and Andreas Heuer. "A framework for self-managing
   database support and parallel computing for assistive systems.
   <http://delivery.acm.org/10.1145/2770000/2769526/a25-marten.pdf>"
Proceedings
   of the 8th ACM International Conference on PErvasive Technologies Related
   to Assistive Environments. ACM, 2015.





Patents

   1.

   R-language integration with a declarative machine learning language
   https://www.google.com/patents/US20150347101



   1.

   Hybrid parallelization strategies for machine learning programs on top
   of mapreduce https://www.google.com/patents/US20160124730



   1.

   Burdick, Douglas Ronald, et al. "Systems and methods for processing
   machine learning algorithms in a MapReduce environment
   <https://www.google.com/patents/US8612368>." U.S. Patent No. 8,612,368.
   17 Dec. 2013.


But, one more thing.. Do you like to include papers related to algorithms ?
Please let me know what you are thinking about this.

Janardhan

On Thu, Jun 1, 2017 at 11:57 PM, Deron Eriksson <deroneriksson@gmail.com>
wrote:

> Currently the SystemML website does not have a research page, but we should
> probably add one, similar to Apache Spark's research page at
> http://spark.apache.org/research.html.
>
> Could someone respond with a list of SystemML research papers that could be
> added to a research page?
>
> Thanks,
> Deron
>
> --
> Deron Eriksson
> Spark Technology Center
> http://www.spark.tc/
>

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