activity
20172025
most citedQuery Processing on Tensor Computation Runtimes

53 citations · 152 across the 7 of their papers we have counts for

collaborators

8 papers

cs.DB2025

Exqutor: Extended Query Optimizer for Vector-augmented Analytical Queries

Hyunjoon Kim, Chaerim Lim, Hyeonjun An +2

Vector similarity search is becoming increasingly important for data science pipelines, particularly in Retrieval-Augmented Generation (RAG), where it enhances large language model…

cs.DB2022★ 1 cited

VIP Hashing -- Adapting to Skew in Popularity of Data on the Fly (extended version)

Aarati Kakaraparthy, Jignesh M. Patel, Brian P. Kroth +1

All data is not equally popular. Often, some portion of data is more frequently accessed than the rest, which causes a skew in popularity of the data items. Adapting to this skew c…

cs.DB2022★ 51 cited

End-to-end Optimization of Machine Learning Prediction Queries

Kwanghyun Park, Karla Saur, Dalitso Banda +3

Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, c…

cs.DB2022★ 53 cited

Query Processing on Tensor Computation Runtimes

Dong He, Supun Nakandala, Dalitso Banda +7

The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the numbe…

cs.DB2020★ 5 cited

Lessons learned from the early performance evaluation of Intel Optane DC Persistent Memory in DBMS

Yinjun Wu, Kwanghyun Park, Rathijit Sen +2

Non-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices(e.g., HDDs and SSDs), while providing the low acces…

cs.DB2019★ 16 cited

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…

cs.DB2019★ 21 cited

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…

cs.DB2017★ 5 cited

Optimization of Imperative Programs in a Relational Database

Karthik Ramachandra, Kwanghyun Park, K. Venkatesh Emani +3

For decades, RDBMSs have supported declarative SQL as well as imperative functions and procedures as ways for users to express data processing tasks. While the evaluation of declar…