48 citations · 101 across the 10 of their papers we have counts for
10 papers
ReAcTable: Enhancing ReAct for Table Question Answering
Yunjia Zhang, Jordan Henkel, Avrilia Floratou +3
Table Question Answering (TQA) presents a substantial challenge at the intersection of natural language processing and data analytics. This task involves answering natural language…
Rapidash: Efficient Constraint Discovery via Rapid Verification
Zifan Liu, Shaleen Deep, Anna Fariha +3
Denial Constraint (DC) is a well-established formalism that captures a wide range of integrity constraints commonly encountered, including candidate keys, functional dependencies,…
From Words to Code: Harnessing Data for Program Synthesis from Natural Language
Anirudh Khatry, Joyce Cahoon, Jordan Henkel +9
Creating programs to correctly manipulate data is a difficult task, as the underlying programming languages and APIs can be challenging to learn for many users who are not skilled…
LST-Bench: Benchmarking Log-Structured Tables in the Cloud
Jesús Camacho-Rodríguez, Ashvin Agrawal, Anja Gruenheid +6
Data processing engines increasingly leverage distributed file systems for scalable, cost-effective storage. While the Apache Parquet columnar format has become a popular choice fo…
OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance from Database Logs [Technical Report]
Fotis Psallidas, Ashvin Agrawal, Chandru Sugunan +6
Provenance encodes information that connects datasets, their generation workflows, and associated metadata (e.g., who or when executed a query). As such, it is instrumental for a w…
Vamsa: Automated Provenance Tracking in Data Science Scripts
Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas +5
There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requiremen…
Data Science through the looking glass and what we found there
Fotis Psallidas, Yiwen Zhu, Bojan Karlas +8
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by…
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino +19
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios inc…
Can the Elephants Handle the NoSQL Onslaught?
Avrilia Floratou, Nikhil Teletia, David J. Dewitt +2
In this new era of "big data", traditional DBMSs are under attack from two sides. At one end of the spectrum, the use of document store NoSQL systems (e.g. MongoDB) threatens to mo…
Column-Oriented Storage Techniques for MapReduce
Avrilia Floratou, Jignesh Patel, Eugene Shekita +1
Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from o…