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 <[email protected]> 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/ >
