Research
Advancing data systems and analytics that enable scalable insights, intelligent applications, and scientific discovery.
Demand-driven resource allocation for cloud databases has become a popular research direction. Recent approaches have evolved from reactive policies to proactive decision making. These approaches leverage not only the current resource demand but also the…
The Intelligence Toolkit is a suite of interactive workflows for creating AI intelligence reports from real-world data sources. The toolkit is designed to help users identify patterns, answers, relationships, and risks within complex datasets, with…
Unified databases offer better knowledge transfer between multimodal data types. They provide substantial corpus support for large language models and are poised to drive innovation in underlying hardware, laying the foundation for data-enhanced AI.
In this issue: RELEVANCE automatically evaluates creative LLM responses; Recyclable vitrimer-based printed circuit boards; Lean Attention: Hardware-aware scalable attention mechanism; WaveCoder: a fine-tuned code LLM; New AutoGen training course.
SIBYL is a machine learning model that makes highly accurate predictions of database queries, enabling tuning for more efficiency. Applying traditional database optimizations to these predicted queries helps maintain high performance as demands change.
LST-Bench is a new open-source benchmark designed to evaluate table formats in cloud environments. It extends existing benchmarks to better reflect real-world usage & performance of data lakes and easily integrates with commonly used analytical…
VisEval: A NL2VIS Benchmark. VisEval is a benchmark designed to evaluate visualization generation methods. In this repository, we provide both the toolkit to support the benchmarking, as well as the data used for benchmarks.
