Over the past five decades, the relational database model has proven to be a scaleable and adaptable model for querying a variety of structured data, with use cases in analytics, transactions, graphs, streaming and more. However, most of the world's data ...

We demonstrate PrismX (PRAM with SSDs as Memory eXtension), a single-machine system for graph analytics. PrismX allows users to make practical use of existing PRAM algorithms without any change. To cope with the limited DRAM capacity, it employs NVMe ...

This paper demonstrates Pynapple-G, an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely, SGPAC and DDCEL, that support grouping points, grouping lines, and ...

Top-k Publish/Subscribe (TkPS) service is widely studied in spatial database, with various cache-based methods proposed to address its efficiency challenge in top-k result maintenance. These methods require in-depth exploration of relationships between ...

Time series database systems (TSDBs) are prevalent in many applications ranging from monitoring and IoT devices to scientific research. Those systems are specifically designed to efficiently manage data indexed by time. Because of the variety of ...

Materialized views (MVs) are critical for improving query performance of database systems, especially in online analytical processing (OLAP) databases. Typically, MVs are maintained by DBAs, which relies on prior knowledge and manual operations. Recently,...

Progressive query processing enables data scientists to efficiently analyze and explore large datasets. Data scientists can start further analyses earlier if the progressive result can represent the complete results well. Most progressive processing ...

Database queries are commonly used to select and rank items. With the increasing awareness of diversity, ensuring a diverse output (i.e., the representation of different groups in the top-k positions) becomes essential. To address this challenge, we ...

In this paper we explore the ability of Large Language Models (LLMs) in analyzing and comparing query plans, and resolving query performance regressions. We present DBG-PT, a query regression debugging framework powered by LLMs. DBG-PT keeps track of ...

Query optimization is a key component in database management systems (DBMS) and distributed data processing platforms. Recent research in the database community incorporated techniques from artificial intelligence to enhance query optimization. Various ...

Native distributed databases, crucial for scalable applications, offer transactional and analytical prowess but face data intricacies and network challenges. Under the CAP theorem's constraints, latency and replication issues necessitate creative ...

Machine learning techniques have been verified to be effective in optimizing data management systems and are widely researched in recent years. However, traditional small-sized ML models often struggle to generalize to new scenarios, and have limited ...

Query optimization must evolve with new workloads. As analytic and data warehouse workloads become more ubiquitous, optimization techniques that reduce the amount of data processed during query execution, enable shared computation and avoid expensive ...

Data lakehouses combine the almost infinite scale and diverse tooling of a data lake with the reliability and functionality of a data warehouse. This paper presents extensions that enhance data lake-houses using Apache Iceberg and Apache Spark with ...

In the Internet of Vehicle (IoV) systems, intelligent vehicles generate huge amounts of data that supports diverse services and applications. In practice, database systems are deployed in the cloud to manage data uploaded from the vehicle side and ...

An important feature of modern query optimizers is the ability to produce a query plan that is optimal for the underlying data set. This requires the ability to estimate cardinalities and computational costs of intermediate query plan nodes, which is ...

SQL has been extremely successful as the de facto standard language for working with data. Virtually all mainstream database-like systems use SQL as their primary query language. But SQL is an old language with significant design problems, making it ...

Many organizations have embraced the "Lakehouse" data management paradigm, which involves constructing structured data warehouses on top of open, unstructured data lakes. This approach stands in stark contrast to traditional, closed, relational databases ...

Modern database systems including IBM Db2 have numerous parameters, "knobs," that require precise configuration to achieve optimal workload performance. Even for experts, manually "tuning" these knobs is a challenging process. We present Db2une, an ...

Over the past several decades, the amount of data being stored and analyzed has increased exponentially. Businesses across industries and sectors have begun relying on this data to improve products, evaluate performance, and make business-critical ...