99 citations · 329 across the 22 of their papers we have counts for
23 papers
The Case for Text-to-SQL Friendly Logical Database Design
Shi Heng Zhang, Zhengjie Miao, Jiannan Wang
Logical database design has traditionally optimized database schemas, including tables, columns, keys, constraints, and views, for correctness, integrity, and human-written applica…
PrepBench: How Far Are We from Natural-Language-Driven Data Preparation?
Jingzhe Xu, Rui Wang, Jiannan Wang +1
Data preparation is a central and time-consuming stage in data analysis workflows. Traditionally, commercial tools have relied on graphical user interfaces (GUIs) to simplify data…
FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tables
Danrui Qi, Weiling Zheng, Jiannan Wang
Feature augmentation from one-to-many relationship tables is a critical but challenging problem in ML model development. To augment good features, data scientists need to come up w…
CleanAgent: Automating Data Standardization with LLM-based Agents
Danrui Qi, Zhengjie Miao, Jiannan Wang
Data standardization is a crucial part of the data science life cycle. While tools like Pandas offer robust functionalities, their complexity and the manual effort required for cus…
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
Danrui Qi, Jinglin Peng, Yongjun He +1
Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data distribution, thus feature preproces…
Enabling SQL-based Training Data Debugging for Federated Learning
Yejia Liu, Weiyuan Wu, Lampros Flokas +2
How can we debug a logistical regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan ap…
DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python
Jinglin Peng, Weiyuan Wu, Brandon Lockhart +6
Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA ta…
Explaining Inference Queries with Bayesian Optimization
Brandon Lockhart, Jinglin Peng, Weiyuan Wu +2
Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal data errors, and provide deeper insight into the data. Inference query explanation seeks…
Are We Ready For Learned Cardinality Estimation?
Xiaoying Wang, Changbo Qu, Weiyuan Wu +2
Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned…
Complaint-driven Training Data Debugging for Query 2.0
Weiyuan Wu, Lampros Flokas, Eugene Wu +1
As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", whi…
Towards Extracting Highlights From Recorded Live Videos: An Implicit Crowdsourcing Approach
Ruochen Jiang, Changbo Qu, Jiannan Wang +2
Live streaming platforms need to store a lot of recorded live videos on a daily basis. An important problem is how to automatically extract highlights (i.e., attractive short video…
Detecting Data Errors with Statistical Constraints
Jing Nathan Yan, Oliver Schulte, Jiannan Wang +1
A powerful approach to detecting erroneous data is to check which potentially dirty data records are incompatible with a user's domain knowledge. Previous approaches allow the user…
Crowd-Powered Data Mining
Chengliang Chai, Ju Fan, Guoliang Li +2
Many data mining tasks cannot be completely addressed by auto- mated processes, such as sentiment analysis and image classification. Crowdsourcing is an effective way to harness th…
Preference-driven Similarity Join
Chuancong Gao, Jiannan Wang, Jian Pei +2
Similarity join, which can find similar objects (e.g., products, names, addresses) across different sources, is powerful in dealing with variety in big data, especially web data. T…
PreCog: Improving Crowdsourced Data Quality Before Acquisition
Hamed Nilforoshan, Jiannan Wang, Eugene Wu
Quality control in crowdsourcing systems is crucial. It is typically done after data collection, often using additional crowdsourced tasks to assess and improve the quality. These…
Reprowd: Crowdsourced Data Processing Made Reproducible
Ruochen Jiang, Jiannan Wang
Crowdsourcing is a multidisciplinary research area including disciplines like artificial intelligence, human-computer interaction, database, and social science. To facilitate coope…
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
Sanjay Krishnan, Jiannan Wang, Eugene Wu +2
Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of…
Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views
Sanjay Krishnan, Jiannan Wang, Michael J. Franklin +2
Materialized views (MVs), stored pre-computed results, are widely used to facilitate fast queries on large datasets. When new records arrive at a high rate, it is infeasible to con…
CLAMShell: Speeding up Crowds for Low-latency Data Labeling
Daniel Haas, Jiannan Wang, Eugene Wu +1
Data labeling is a necessary but often slow process that impedes the development of interactive systems for modern data analysis. Despite rising demand for manual data labeling, th…
The Expected Optimal Labeling Order Problem for Crowdsourced Joins and Entity Resolution
Jiannan Wang, Guoliang Li, Tim Kraska +2
In the SIGMOD 2013 conference, we published a paper extending our earlier work on crowdsourced entity resolution to improve crowdsourced join processing by exploiting transitive re…
Leveraging Transitive Relations for Crowdsourced Joins
Jiannan Wang, Guoliang Li, Tim Kraska +2
The development of crowdsourced query processing systems has recently attracted a significant attention in the database community. A variety of crowdsourced queries have been inves…
CrowdER: Crowdsourcing Entity Resolution
Jiannan Wang, Tim Kraska, Michael J. Franklin +1
Entity resolution is central to data integration and data cleaning. Algorithmic approaches have been improving in quality, but remain far from perfect. Crowdsourcing platforms offe…
PASS-JOIN: A Partition-based Method for Similarity Joins
Guoliang Li, Dong Deng, Jiannan Wang +1
As an essential operation in data cleaning, the similarity join has attracted considerable attention from the database community. In this paper, we study string similarity joins wi…