The bi-channel paradigm separates database networking into a high-performance UDP data path and a TCP control path to reduce kernel overhead while preserving reliability on fast cloud networks.
Bercea, and Rasmus Pagh
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7verdicts
UNVERDICTED 7representative citing papers
EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
Sublime generalizes Count-Min and Count Sketch with dynamically elongating counters and expanding counter arrays to deliver sublinear error growth and lower memory use on skewed unbounded streams.
Interviews with nine data-intensive programmers identify three cross-cutting debugging challenges that visualization can address via evidence alignment, expectation comparison, and state tracing.
Introduces a toolbox for studying energy gaps, eigenstate structure, and optimization dynamics in quantum annealing formulations of database problems.
LogCopilot is an LLM framework that builds a hierarchical knowledge base from logs and generates/executes LogQL queries from natural language instructions, reporting 76.8% average accuracy across four datasets.
MountDB extends RocksDB with Memtable-level model reuse and a block-aware learned disk index, reporting up to 1.5X write and 2.1X read throughput over state-of-the-art on large-scale workloads.
citing papers explorer
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The Bi-Channel Networking Paradigm for Database Systems in the Cloud
The bi-channel paradigm separates database networking into a high-performance UDP data path and a TCP control path to reduce kernel overhead while preserving reliability on fast cloud networks.
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Cost-Aware Optimization for Agentic Query Execution
EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
-
Sublime: Sublinear Error & Space for Unbounded Skewed Streams
Sublime generalizes Count-Min and Count Sketch with dynamically elongating counters and expanding counter arrays to deliver sublinear error growth and lower memory use on skewed unbounded streams.
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Debugging as Evidence-Driven Reasoning: Visualization Opportunities in Data-Intensive Programming
Interviews with nine data-intensive programmers identify three cross-cutting debugging challenges that visualization can address via evidence alignment, expectation comparison, and state tracing.
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A Toolbox to Understand the Physics of Quantum Data Management
Introduces a toolbox for studying energy gaps, eigenstate structure, and optimization dynamics in quantum annealing formulations of database problems.
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LogCopilot: Automating Log Aggregation Analysis through Large Language Models
LogCopilot is an LLM framework that builds a hierarchical knowledge base from logs and generates/executes LogQL queries from natural language instructions, reporting 76.8% average accuracy across four datasets.
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A Pragmatic Approach to Learned Indexing in RocksDB: Targeted Optimizations with Minimal System Modification
MountDB extends RocksDB with Memtable-level model reuse and a block-aware learned disk index, reporting up to 1.5X write and 2.1X read throughput over state-of-the-art on large-scale workloads.