48 citations · 113 across the 9 of their papers we have counts for
11 papers
Towards an AI Software Factory for Data Systems
Anna Pavlenko, Bogdan Crivat, Brandon Haynes +23
AI-assisted coding tools deliver significant acceleration of coding, but only limited impact across the end-to-end software development lifecycle (SDLC)--an Amdahl's law effect! In…
Nexus: Inferring Join Graphs from Metadata Alone via Iterative Low-Rank Matrix Completion
Tianji Cong, Yuanyuan Tian, Andreas Mueller +5
Automatically inferring join relationships is a critical task for effective data discovery, integration, querying and reuse. However, accurately and efficiently identifying these r…
Optimizing open-domain question answering with graph-based retrieval augmented generation
Joyce Cahoon, Prerna Singh, Nick Litombe +6
In this work, we benchmark various graph-based retrieval-augmented generation (RAG) systems across a broad spectrum of query types, including OLTP-style (fact-based) and OLAP-style…
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,…
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…
Extending Relational Query Processing with ML Inference
Konstantinos Karanasos, Matteo Interlandi, Doris Xin +10
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and cons…
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…
Provenance for Interactive Visualizations
Fotis Psallidas, Eugene Wu
We highlight the connections between data provenance and interactive visualizations. To do so, we first incrementally add interactions to a visualization and show how these interac…
Smoke: Fine-grained Lineage at Interactive Speed
Fotis Psallidas, Eugene Wu
Data lineage describes the relationship between individual input and output data items of a workflow, and has served as an integral ingredient for both traditional (e.g., debugging…