In the wake of rapid advancements in artificial intelligence (AI), we stand
on the brink of a transformative leap in data systems. The imminent fusion of
AI and DB (AIxDB) promises a new generation of data systems, which will relieve
the burden on end-users across all industry sectors by featuring AI-enhanced
functionalities, such as personalized and automated in-database AI-powered
analytics, self-driving capabilities for improved system performance, etc. In
this paper, we explore the evolution of data systems with a focus on deepening
the fusion of AI and DB. We present NeurDB, our next-generation data system
designed to fully embrace AI design in each major system component and provide
in-database AI-powered analytics. We outline the conceptual and architectural
overview of NeurDB, discuss its design choices and key components, and report
its current development and future plan.

人工智能和数据库的融合在数据系统中具有巨大的潜力，本文介绍了 NeurDB 作为下一代数据系统的设计，以全面引入人工智能设计，并提供基于数据库的人工智能分析。

NeurDB: 一款基于人工智能的自主数据系统

NeurDB: An AI-powered Autonomous Data System

Higher education plays a critical role in driving an innovative economy by
equipping students with knowledge and skills demanded by the workforce. While
researchers and practitioners have developed data systems to track detailed
occupational skills, such as those established by the U.S. Department of Labor
(DOL), much less effort has been made to document skill development in higher
education at a similar granularity. Here, we fill this gap by presenting a
longitudinal dataset of skills inferred from over three million course syllabi
taught at nearly three thousand U.S. higher education institutions. To
construct this dataset, we apply natural language processing to extract from
course descriptions detailed workplace activities (DWAs) used by the DOL to
describe occupations. We then aggregate these DWAs to create skill profiles for
institutions and academic majors. Our dataset offers a large-scale
representation of college-educated workers and their role in the economy. To
showcase the utility of this dataset, we use it to 1) compare the similarity of
skills taught and skills in the workforce according to the US Bureau of Labor
Statistics, 2) estimate gender differences in acquired skills based on
enrollment data, 3) depict temporal trends in the skills taught in social
science curricula, and 4) connect college majors' skill distinctiveness to
salary differences of graduates. Overall, this dataset can enable new research
on the source of skills in the context of workforce development and provide
actionable insights for shaping the future of higher education to meet evolving
labor demands especially in the face of new technologies.

高等教育在推动创新经济方面发挥着关键作用，通过为学生提供劳动力需求所需的知识和技能。本研究填补了一个空白，通过提供从近 3000 所美国高等教育机构的三百万门课程大纲中推断技能的纵向数据集，回答了如何在高等教育中记录技能发展的问题。这个数据集通过自然语言处理从课程描述中提取具体的工作场景活动（DOL 所用来描述职业的 DWAs），并聚合这些 DWAs 来为机构和学术专业创建技能概况。这个数据集可以用于研究技能来源、劳动力发展的背景，并为塑造未来高等教育以满足不断变化的劳动力需求提供可操作的见解。

美国高等教育课程中所教授技能的国家纵向数据集

A national longitudinal dataset of skills taught in U.S. higher  education curricula

Query optimizers in relational database management systems (RDBMSs) search
for execution plans expected to be optimal for a given queries. They use
parameter estimates, often inaccurate, and make assumptions that may not hold
in practice. Consequently, they may select execution plans that are suboptimal
at runtime, when these estimates and assumptions are not valid, which may
result in poor query performance. Therefore, query optimizers do not
sufficiently support robust query optimization. Recent years have seen a surge
of interest in using machine learning (ML) to improve efficiency of data
systems and reduce their maintenance overheads, with promising results obtained
in the area of query optimization in particular. In this paper, inspired by
these advancements, and based on several years of experience of IBM Db2 in this
journey, we propose Robust Optimization of Queries, (Roq), a holistic framework
that enables robust query optimization based on a risk-aware learning approach.
Roq includes a novel formalization of the notion of robustness in the context
of query optimization and a principled approach for its quantification and
measurement based on approximate probabilistic ML. It also includes novel
strategies and algorithms for query plan evaluation and selection. Roq also
includes a novel learned cost model that is designed to predict query execution
cost and the associated risks and performs query optimization accordingly. We
demonstrate experimentally that Roq provides significant improvements to robust
query optimization compared to the state-of-the-art.

在本篇论文中，我们提出了基于风险感知学习方法的全面框架 Robust Optimization of Queries（Roq），它通过一种新颖的鲁棒性概念的形式化和定量测量，实现了鲁棒查询优化。Roq 还包括查询计划评估和选择的新策略、算法以及预测查询执行成本和相关风险的新型学习成本模型。我们通过实验证明，与现有技术相比，Roq 显著提高了鲁棒查询优化的效果。