This text, accompanied by a ‘gospel’ that I made by montaging the US-recorded captions, is an attempt for re-narrating Marshall Plan’s discourse on the working class, aka the so-called ‘free labor’ of the US against the ‘communist labor’ of the “Soviet thr ...
The aim of this paper is to serve as a lightweight introduction to concurrency control for database theorists through a uniform presentation of the work on robustness against Multiversion Read Committed and Snapshot Isolation. ...
As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...
Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists ...
Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processin ...
As a unified data repository, data lake plays a vital role in enterprise data management and analysis. It composes the raw files into tables that are processed in-situ by various computation engines and applications. Therefore, the read performance of the ...
Transaction processing is a central part of most database applications. While serializability remains the gold standard for desirable transactional semantics, many database systems offer improved transaction throughput at the expense of introducing potenti ...
The hardware complexity of modern machines makes the design of adequate programming models crucial for jointly ensuring performance, portability, and productivity in high-performance computing (HPC). Sequential task-based programming models paired with adv ...
Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...
Timely insights lead to business growth and scientific breakthroughs but require analytical engines that cope with the ever-increasing data processing needs. Analytical engines relied on rapid CPU improvements, yet the end of Dennard scaling stopped the fr ...
