This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which was planned to take place in Dublin, Ireland, in April 2020, as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The actual ETAPS 2020 meeting was postponed due to the Corona pandemic. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems.
This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems.
This open access book constitutes the proceedings of the 30th European Symposium on Programming, ESOP 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 24 papers included in this volume were carefully reviewed and selected from 79 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems.
This open access book constitutes the proceedings of the 32nd European Symposium on Programming, ESOP 2023, which was held during April 22-27, 2023, in Paris, France, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2023. The 20 regular papers presented in this volume were carefully reviewed and selected from 55 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems.
This book constitutes the proceedings of the 26th European Symposium on Programming, ESOP 2017, which took place in Uppsala, Sweden in April 2017, held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017. The 36 papers presented in this volume were carefully reviewed and selected from 112 submissions. They cover traditional as well as emerging topics in programming languages. In detail they deal with semantic foundation and type system for probabilistic programming; techniqu3es for verifying concurrent or higher-order programs; programming languages for arrays or web data; program analysis and verification of non-standard program properties; foundation and application of interactive theorem proving; graph rewriting; separation logic; session type; type theory; and implicit computational complexity.
This book constitutes the refereed proceedings of the 26th International Symposium on Static Analysis, SAS 2019, held in Porto, Portugal, in October 2019. The 20 regular papers presented in this book were carefully reviewed and selected from 50 submissions. The papers are grouped in topical sections on pointers and dataflow; languages and decidability; numerical; trends: assuring machine learning; synthesis and security; and temporal properties and termination.
This book presents a comprehensive, structured, up-to-date survey on instruction selection. The survey is structured according to two dimensions: approaches to instruction selection from the past 45 years are organized and discussed according to their fundamental principles, and according to the characteristics of the supported machine instructions. The fundamental principles are macro expansion, tree covering, DAG covering, and graph covering. The machine instruction characteristics introduced are single-output, multi-output, disjoint-output, inter-block, and interdependent machine instructions. The survey also examines problems that have yet to be addressed by existing approaches. The book is suitable for advanced undergraduate students in computer science, graduate students, practitioners, and researchers.
This open access book constitutes the proceedings of the 27th European Symposium on Programming, ESOP 2018, which took place in Thessaloniki, Greece in April 2018, held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018. The 36 papers presented in this volume were carefully reviewed and selected from 114 submissions. The papers are organized in topical sections named: language design; probabilistic programming; types and effects; concurrency; security; program verification; program analysis and automated verification; session types and concurrency; concurrency and distribution; and compiler verification.
This is an overview of the end-to-end data cleaning process. Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, this book describes various error detection and repair methods, and attempts to anchor these proposals with multiple taxonomies and views. Specifically, it covers four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, it includes a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models. This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate.
