data science lifecycle)  2012-2018 IBM Research – Almaden, USA  Declarative large-scale machine learning  Optimizer and runtime of Apache SystemML  2011 PhD TU Dresden, Germany  Cost-based optimization Algebra for Large-Scale Machine Learning. PVLDB 2016] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Large-Scale Machine Learning. VLDB Journal 2018 27(5)] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large-Scale Machine Learning. Commun.

feedback and recommendations, widen own horizon  Lots of similarities to code reviews in OSS  Learning by What NOT to Do  Accept if no time to review  The Goldilocks Method (examples, proofs, theoretical Blind reviewing Scientific Reading [Graham Cormode: How NOT to review a paper: the tools and techniques of the adversarial reviewer. SIGMOD Rec. 37(4) 2008] This paper leaves many questions unanswered particularly poor. We use index structures like b-trees, tries, and hash tables; as well as compression techniques like run-length encoding, dictionary encoding, and null suppression. A woman without her man

inclusion of header files). It makes testing much more difficult. Macros can do things these other techniques cannot, and you do see them in the codebase, especially in the lower-level libraries. And some metaprogramming Avoid complicated template programming. Template metaprogramming refers to a family of techniques that exploit the fact that the C++ template instantiation mechanism is Turing complete and can like Google Test, std::tuple, std::function, and Boost.Spirit would be impossible without it. The techniques used in template metaprogramming are often obscure to anyone but language experts. Code that uses

or lambda. Pros: Simple list comprehensions can be clearer and simpler than other list creation techniques. Generator expressions can be very efficient, since they avoid the creation of a list entirely actually run the file’s destructor. Different Python implementations use different memory management techniques, such as delayed Garbage Collection, which may increase the object’s lifetime arbitrarily and indefinitely

#2 “Big Data”  MR/Spark: BigBench, HiBench, SparkBench  Array Databases: GenBase  #3 Machine Learning Systems  SLAB, DAWNBench, MLPerf, MLBench, AutoML Bench, Meta Worlds, TPCx-AI Experiments and text Experiments and Result Presentation [Matthias Boehm et al: SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CIDR 2020] 17 706.015 Introduction to Scientific Interpretation [Matthias Boehm et al: On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. PVLDB 11(12) 2018] 19 706.015 Introduction to Scientific Writing – 03 Experiments