Data Management in Machine Learning Systems

Matthias Boehm is a professor at Graz University of Technology, Austria, where he holds a BMVIT-endowed chair for data management. Prior to joining TU Graz in 2018, he was a research staff member at IBM Research - Almaden, CA, USA, with a focus on compilation and runtime techniques for declarative, large-scale machine learning. He received his Ph.D.from Dresden University of Technology, Germany in 2011 with a dissertation on cost-based optimization of integration flows. His previous research also includes systems support for time series forecasting as well as in-memory indexing and query processing. Matthias is a recipient of the 2016 VLDB Best Paper Award, and a 2016 SIGMOD Research Highlight Award.Arun Kumar is an Assistant Professor at the University of California, San Diego. He received his Ph.D. from the University of Wisconsin-Madison in 2016. His research interests are in the intersection of data management, systems, and ML, with a focus on making ML-based data analytics easier,faster, cheaper, and more scalable. Ideas from his work have been adopted by many companies, including EMC, Oracle, Cloudera, Facebook, and Microsoft. He is a recipient of the Best Paper Award at SIGMOD 2014, the 2016 CS dissertation research award from UW-Madison, a 2016 Google Faculty Research Award, and a 2018 Hellman Fellowship.Jun Yang is a Professor of Computer Science at Duke University, where he has been teaching since receiving his Ph.D. from Stanford University in 2001. He is broadly interested in databases and data-intensive systems. He is a recipient of the NSF CAREER Award, IBM Faculty Award, HP Labs Innovation Research Award, and Google Faculty Research Award. He also received the David and Janet Vaughan Brooks Teaching Award at Duke. His current research interests lie in making data analysis easier and more scalable for scientists, statisticians, and journalists.

• Preface.- Acknowledgments.- Introduction.- ML Through Database Queries and UDFs.- Multi-Table ML and Deep Systems Integration.- Rewrites and Optimization.- Execution Strategies.- Data Access Methods.- Resource Heterogeneity and Elasticity.- Systems for ML Lifecycle Tasks.- Conclusions.- Bibliography.- Authors' Biographies.