108 citations · 353 across the 17 of their papers we have counts for
17 papers
DAGSmith: Dependency-Aware Rewriting for dbt-Style SQL Pipelines
Jie Liu, Lin Ma, Barzan Mozafari
Modern analytics is increasingly organized as recurring SQL pipelines rather than isolated SQL statements. Tools such as dbt, which have gained extreme popularity in recent years,…
BEGIN AI TRANSACTION: Semantic Isolation for Durable AI Workflows
Barzan Mozafari
An AI execution can now outlive the environment in which it began. What once fit inside one model call increasingly unfolds across pauses, retries, branches, subagents, and model-s…
GenRewrite: Query Rewriting via Large Language Models
Jie Liu, Barzan Mozafari
Query rewriting is an effective technique for refining poorly written queries before they reach the query optimizer. However, manual rewriting is not scalable, as it is prone to er…
Transformer with Memory Replay
Rui Liu, Barzan Mozafari
Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora. They are extremely compute-intensive and have v…
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
Rui Liu, Young Jin Kim, Alexandre Muzio +1
Sparsely activated transformers, such as Mixture of Experts (MoE), have received great interest due to their outrageous scaling capability which enables dramatical increases in mod…
Adam with Bandit Sampling for Deep Learning
Rui Liu, Tianyi Wu, Barzan Mozafari
Adam is a widely used optimization method for training deep learning models. It computes individual adaptive learning rates for different parameters. In this paper, we propose a ge…
Joins on Samples: A Theoretical Guide for Practitioners
Dawei Huang, Dong Young Yoon, Seth Pettie +1
Despite decades of research on approximate query processing (AQP), our understanding of sample-based joins has remained limited and, to some extent, even superficial. The common be…
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
Yongjoo Park, Jingyi Qing, Xiaoyang Shen +1
The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning,…
A Bandit Approach to Maximum Inner Product Search
Rui Liu, Tianyi Wu, Barzan Mozafari
There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques requi…
QuickSel: Quick Selectivity Learning with Mixture Models
Yongjoo Park, Shucheng Zhong, Barzan Mozafari
Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshe…
Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets
Jarrid Rector-Brooks, Jun-Kun Wang, Barzan Mozafari
We revisit the Frank-Wolfe (FW) optimization under strongly convex constraint sets. We provide a faster convergence rate for FW without line search, showing that a previously overl…
VerdictDB: Universalizing Approximate Query Processing
Yongjoo Park, Barzan Mozafari, Joseph Sorenson +1
Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of…
Database Learning: Toward a Database that Becomes Smarter Every Time
Yongjoo Park, Ahmad Shahab Tajik, Michael Cafarella +1
In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate qu…
Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases
Jiamin Huang, Barzan Mozafari, Grant Schoenebeck +1
Decades of research have sought to improve transaction processing performance and scalability in database management systems (DBMSs). However, significantly less attention has been…
Visualization-Aware Sampling for Very Large Databases
Yongjoo Park, Michael Cafarella, Barzan Mozafari
Interactive visualizations are crucial in ad hoc data exploration and analysis. However, with the growing number of massive datasets, generating visualizations in interactive times…
Active Learning for Crowd-Sourced Databases
Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin +2
Crowd-sourcing has become a popular means of acquiring labeled data for a wide variety of tasks where humans are more accurate than computers, e.g., labeling images, matching objec…
BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data
Sameer Agarwal, Aurojit Panda, Barzan Mozafari +2
In this paper, we present BlinkDB, a massively parallel, sampling-based approximate query engine for running ad-hoc, interactive SQL queries on large volumes of data. The key insig…