Scientific Papers Matthias Boehm Graz University of Technology, Austria Institute of Interactive Systems and Data Science Computer Science and Biomedical Engineering BMK endowed chair for Data Management TU Graz, Austria  BMK endowed chair for data management  Data management for data science (ML systems internals, end-to-end data science lifecycle)  2012-2018 IBM Research – Almaden, USA  Declarative Apache SystemML  2011 PhD TU Dresden, Germany  Cost-based optimization of integration flows  Systems support for time series forecasting  In-memory indexing and query processing Data Management Group

Reproducibility Matthias Boehm Graz University of Technology, Austria Institute of Interactive Systems and Data Science Computer Science and Biomedical Engineering BMK endowed chair for Data Management 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 Result speedup-only results (need absolute numbers for grounding)  #2 Additional Baselines  Alternative systems w/ different runtime and compiler  Usually, not directly comparable but important for grounding

alignment. • printf() specifiers for some types are not cleanly portable between 32-bit and 64-bit systems. C99 defines some portable format specifiers. Unfor- tunately, MSVC 7.1 does not understand some hpp) and parallel/distributed algorithms and data structures (boost/graph/parallel/* and boost/graph/distributed/*). • Property Map from boost/property_map, except parallel/distributed property maps (b

object systems, S3 and S4, both of which are available in R. S3 methods are more interactive and flexible, whereas S4 methods are more formal and rigorous. (For an illustration of the two systems, see Thomas

Reading and Writing Matthias Boehm Graz University of Technology, Austria Institute of Interactive Systems and Data Science Computer Science and Biomedical Engineering BMK endowed chair for Data Management Trustworthy Sources  Archival publications, awareness of peer-review  From right communities (e.g., ML systems vs ML algorithms)  Reputation of website, authors, etc  Recap: Give Credit  Cite broadly, give