Related papers: Evolving NoSQL Databases Without Downtime

Efficient consistency maintenance of incomplete and dynamic real-life databases is a quality label for further data analysis. In prior work, we tackled the generic problem of database updating in the presence of tuple generating constraints…

The development of high-speed storage devices such as NVMe SSDs has shifted the primary I/O bottleneck from hardware to software. Modern database systems also rely on kernel-based I/O paths, where frequent system call invocations and…

Modern databases typically makes use of the Log Structured Merge-Tree for organizing data in indexes, which is a kind of disk-based data structure. It was proposed to efficiently handle frequent update queries (also called update intensive…

As data-driven methods are becoming pervasive in a wide variety of disciplines, there is an urgent need to develop scalable and sustainable tools to simplify the process of data science, to make it easier to keep track of the analyses being…

Based on a brief history of the storage systems for Web applications, we motivate the need for a new storage system. We then describe the architecture of such a system, called Yesquel. Yesquel supports the SQL query language and offers…

"PL/SQL functions are slow," is common developer wisdom that derives from the tension between set-oriented SQL evaluation and statement-by-statement PL/SQL interpretation. We pursue the radical approach of compiling PL/SQL away, turning…

With the advent of the Internet and Internet-connected devices, modern business applications can experience rapid increases as well as variability in transactional workloads. Database replication has been employed to scale performance and…

Modern and emerging architectures demand increasingly complex compiler analyses and transformations. As the emphasis on compiler infrastructure moves beyond support for peephole optimizations and the extraction of instruction-level…

Recent advances in query optimization have shifted from traditional rule-based and cost-based techniques towards machine learning-driven approaches. Among these, reinforcement learning (RL) has attracted significant attention due to its…

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing…

The complexity of database systems has increased significantly along with the continuous growth of data, resulting in NoSQL systems and forcing Information Systems (IS) architects to constantly adapt their data models (i.e., the data…

This paper introduces Rumble, a query execution engine for large, heterogeneous, and nested collections of JSON objects built on top of Apache Spark. While data sets of this type are more and more wide-spread, most existing tools are built…

The data-centric paradigm has emerged as a pivotal direction in artificial intelligence (AI), emphasizing the role of high-quality training data. This shift is especially critical in the Text-to-SQL task, where the scarcity, limited…

Variability inherently exists in databases in various contexts which creates database variants. For example, variants of a database could have different schemas/content (database evolution problem), variants of a database could root from…

A computing job in a big data system can take a long time to run, especially for pipelined executions on data streams. Developers often need to change the computing logic of the job such as fixing a loophole in an operator or changing the…

Keyword search in relational databases has been widely studied in recent years because it does not require users neither to master a certain structured query language nor to know the complex underlying database schemas. Most of existing…

Streaming data pipelines remain challenging and expensive to build and maintain, despite significant advancements in stronger consistency, event time semantics, and SQL support over the last decade. Persistent obstacles continue to hinder…

Transforming natural language into SQL queries (NL2SQL) is crucial for data-driven business applications. Existing frameworks, trained on open-source datasets, struggle with complex business logic and lack domain-specific data for…

While traditional RDBMSes offer a lot of advantages, they require significant effort to setup and to use. Because of these challenges, many data scientists and analysts have switched to using alternative data management solutions. These…

Recursive queries and recursive derived tables constitute an important part of the SQL standard. Their efficient processing is important for many real-life applications that rely on graph or hierarchy traversal. Position-enabled…