Enhanced DBMS Performance and Visual Analytics Innovations

The recent developments in the research area of database management systems (DBMS) and data visualization have shown a strong emphasis on enhancing performance, scalability, and usability. Innovations in benchmarking and performance analysis tools, such as visual analytics systems, are being developed to provide more intuitive and efficient ways to compare and interpret DBMS performance. These tools are crucial for both researchers and industry professionals to make informed decisions about DBMS selection and optimization. Additionally, there is a growing focus on modular and high-speed storage engine architectures, which aim to improve the performance of key-value stores while maintaining advanced functionalities. In the realm of data visualization, there is a shift towards more effective methods for representing uncertainty and multivariate data, which are critical for accurate decision-making. Furthermore, the integration of schema inference directly into DBMSs is being explored to enhance the usability and scalability of NoSQL databases. Overall, the field is moving towards more integrated, efficient, and user-friendly solutions that address the complex needs of modern data management and analysis.

Sources

DBenVis: A Visual Analytics System for Comparing DBMS Performance via Benchmark Programs

Analyzing Performance Characteristics of PostgreSQL and MariaDB on NVMeVirt

The Noisy Work of Uncertainty Visualisation Research: A Review

KV-Tandem -- a Modular Approach to Building High-Speed LSM Storage Engines

Performance Evaluation of Geospatial Images based on Zarr and Tiff

OrigamiPlot: An R Package and Shiny Web App Enhanced Visualizations for Multivariate Data

[Experiments \& Analysis] Hash-Based vs. Sort-Based Group-By-Aggregate: A Focused Empirical Study [Extended Version]

Introducing Schema Inference as a Scalable SQL Function [Extended Version]

Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All?

Data Formats in Analytical DBMSs: Performance Trade-offs and Future Directions

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