51 citations · 120 across the 6 of their papers we have counts for
6 papers
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…
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…
Iterative MapReduce for Large Scale Machine Learning
Joshua Rosen, Neoklis Polyzotis, Vinayak Borkar +5
Large datasets ("Big Data") are becoming ubiquitous because the potential value in deriving insights from data, across a wide range of business and scientific applications, is incr…
Scaling Datalog for Machine Learning on Big Data
Yingyi Bu, Vinayak Borkar, Michael J. Carey +5
In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine le…