code. It finds problems that are typically caught by a compiler for less dynamic languages like C and C++. Because of the dynamic nature of Python, some warnings may be incorrect; however, spurious warnings 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. Cons: Complicated list comprehensions

undefined behavior, which can manifest as subtle run-time bugs. • User-defined literals allow the creation of new syntactic forms that are unfamiliar even to experienced C++ programmers. Define overloaded may 27 never be deleted. • Smart pointers are not perfect substitutes for plain pointers. If dynamic allocation is necessary, prefer to keep ownership with the code that allocated it. If other code allows a programmer to query the C++ class of an object at run time. This is done by use of typeid or dynamic_cast. Querying the type of an object at run-time frequently means a design problem. Needing to know

University of Technology, WS 2021/22 Benchmarks  Overview  Community- and organization-driven creation of agreed benchmarks  Benchmarks can define a field and foster innovation  #1 Data Management each, 85% confidence, ~55GB H5)  ML pipeline: preprocessing, ResNet18, climate models  Label Creation/ Validation  Team learning  Labeling w/ checks  Label validation  Quantitative validation

Practical. SIGMOD 2021  #14.2 Guido Moerkotte, Thomas Neumann: Analysis of Two Existing and One New Dynamic Programming Algorithm for the Generation of Optimal Bushy Join Trees without Cross Products. VLDB