3 citations · 9 across the 15 of their papers we have counts for
15 papers
A Decade of Systems for Human Data Interaction
Eugene Wu, Yiru Chen, Haneen Mohammed +1
Human-data interaction (HDI) presents fundamentally different challenges from traditional data management. HDI systems must meet latency, correctness, and consistency needs that st…
Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
Gagan Bansal, Wenyue Hua, Zezhou Huang +21
As LLM agents advance, they are increasingly mediating economic decisions, ranging from product discovery to transactions, on behalf of users. Such applications promise benefits bu…
GPU Acceleration of SQL Analytics on Compressed Data
Zezhou Huang, Krystian Sakowski, Hans Lehnert +5
GPUs are uniquely suited to accelerate (SQL) analytics workloads thanks to their massive compute parallelism and High Bandwidth Memory (HBM) -- when datasets fit in the GPU HBM, pe…
Data Cleaning Using Large Language Models
Shuo Zhang, Zezhou Huang, Eugene Wu
Data cleaning is a crucial yet challenging task in data analysis, often requiring significant manual effort. To automate data cleaning, previous systems have relied on statistical…
Cocoon: Semantic Table Profiling Using Large Language Models
Zezhou Huang, Eugene Wu
Data profilers play a crucial role in the preprocessing phase of data analysis by identifying quality issues such as missing, extreme, or erroneous values. Traditionally, profilers…
Disambiguate Entity Matching using Large Language Models through Relation Discovery
Zezhou Huang
Entity matching is a critical challenge in data integration and cleaning, central to tasks like fuzzy joins and deduplication. Traditional approaches have focused on overcoming fuz…
Data Ambiguity Strikes Back: How Documentation Improves GPT's Text-to-SQL
Zezhou Huang, Pavan Kalyan Damalapati, Eugene Wu
Text-to-SQL allows experts to use databases without in-depth knowledge of them. However, real-world tasks have both query and data ambiguities. Most works on Text-to-SQL focused on…
Lightweight Materialization for Fast Dashboards Over Joins
Zezhou Huang, Eugene Wu
Dashboards are vital in modern business intelligence tools, providing non-technical users with an interface to access comprehensive business data. With the rise of cloud technology…
The Fast and the Private: Task-based Dataset Search
Zezhou Huang, Jiaxiang Liu, Haonan Wang +1
Modern dataset search platforms employ ML task-based utility metrics instead of relying on metadata-based keywords to comb through extensive dataset repositories. In this setup, re…
Saibot: A Differentially Private Data Search Platform
Zezhou Huang, Jiaxiang Liu, Daniel Alabi +2
Recent data search platforms use ML task-based utility measures rather than metadata-based keywords, to search large dataset corpora. Requesters submit a training dataset and these…
JoinBoost: Grow Trees Over Normalized Data Using Only SQL
Zezhou Huang, Rathijit Sen, Jiaxiang Liu +1
Although dominant for tabular data, ML libraries that train tree models over normalized databases (e.g., LightGBM, XGBoost) require the data to be denormalized as a single table, m…
Aggregation Consistency Errors in Semantic Layers and How to Avoid Them
Zezhou Huang, Pavan Kalyan Damalapati, Eugene Wu
Analysts often struggle with analyzing data from multiple tables in a database due to their lack of knowledge on how to join and aggregate the data. To address this, data engineers…
Kitana: Efficient Data Augmentation Search for AutoML
Zezhou Huang, Pranav Subramaniam, Raul Castro Fernandez +1
AutoML services provide a way for non-expert users to benefit from high-quality ML models without worrying about model design and deployment, in exchange for a charge per hour ($21…
Calibration: A Simple Trick for Wide-table Delta Analytics
Zezhou Huang, Eugene Wu
Data analytics over normalized databases typically requires computing and materializing expensive joins (wide-tables). Factorized query execution models execution as message passin…
Reptile: Aggregation-level Explanations for Hierarchical Data
Zezhou Huang, Eugene Wu
Recent query explanation systems help users understand anomalies in aggregation results by proposing predicates that describe input records that, if deleted, would resolve the anom…