the work within the object itself. • If the work belongs outside the object and instead in some processing code, consider a double-dispatch solution, such as the Visitor design pattern. This allows a facility (adj_list_serialize.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 property maps (boost/property_map/parallel/*). • Iterator from boost/iterator • The part of Polygon that deals with Voronoi diagram construction and doesn’t depend on the rest of Polygon: boost/polygon/voronoi_builder

two continuation lines use the same indentation level if and only if they begin with syntactically parallel elements. Section 4.6.3 on Horizontal alignment addresses the discouraged practice of using a variable

integration flows  Systems support for time series forecasting  In-memory indexing and query processing Data Management Group DB group https://github.com/ apache/systemds 6 706.015 Introduction Salihoglu, Jimmy Lin, M. Tamer Özsu: The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing. PVLDB 11(4) 2017  #4.3 Yuanyuan Tian, Andrey Balmin, Severin Andreas Corsten, Shirish Tatikonda Dehnert, Ilan Horn, Naty Leiser, Grzegorz Czajkowski: Pregel: a system for large-scale graph processing. SIGMOD 2010 Paper Project Proposals 29 706.015 Introduction to Scientific Writing – 01 Introduction

introduced. Later, Raman and Swart investigated query processing on heavyweight Huffman coding schemes [40], Later, [40] investigated query processing on heavyweight Huffman coding schemes, 22 706.015

benchmarks  Benchmarks can define a field and foster innovation  #1 Data Management  Query processing: 007, TPC-C, TPC-E, TPC-H, TPC-DS (w/ audit)  Join ordering: JOB  #2 “Big Data”  MR/Spark: