18 citations · 35 across the 10 of their papers we have counts for
11 papers
CACTUSDB: Unlock Co-Optimization Opportunities for SQL and AI/ML Inferences
Lixi Zhou, Kanchan Chowdhury, Lulu Xie +5
There is a growing demand for supporting inference queries that combine Structured Query Language (SQL) and Artificial Intelligence / Machine Learning (AI/ML) model inferences in d…
InferF: Declarative Factorization of AI/ML Inferences over Joins
Kanchan Chowdhury, Lixi Zhou, Lulu Xie +2
Real-world AI/ML workflows often apply inference computations to feature vectors joined from multiple datasets. To avoid the redundant AI/ML computations caused by repeated data re…
Privacy and Accuracy-Aware AI/ML Model Deduplication
Hong Guan, Lei Yu, Lixi Zhou +5
With the growing adoption of privacy-preserving machine learning algorithms, such as Differentially Private Stochastic Gradient Descent (DP-SGD), training or fine-tuning models on…
Privacy-Preserving Redaction of Diagnosis Data through Source Code Analysis
Lixi Zhou, Lei Yu, Jia Zou +1
Protecting sensitive information in diagnostic data such as logs, is a critical concern in the industrial software diagnosis and debugging process. While there are many tools devel…
Declarative Privacy-Preserving Inference Queries
Hong Guan, Ansh Tiwari, Summer Gautier +8
Detecting inference queries running over personal attributes and protecting such queries from leaking individual information requires tremendous effort from practitioners. To tackl…
Serving Deep Learning Model in Relational Databases
Lixi Zhou, Qi Lin, Kanchan Chowdhury +9
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this…
DeepMapping: Learned Data Mapping for Lossless Compression and Efficient Lookup
Lixi Zhou, K. Selçuk Candan, Jia Zou
Storing tabular data to balance storage and query efficiency is a long-standing research question in the database community. In this work, we argue and show that a novel DeepMappin…
Benchmark of DNN Model Search at Deployment Time
Lixi Zhou, Arindam Jain, Zijie Wang +3
Deep learning has become the most popular direction in machine learning and artificial intelligence. However, the preparation of training data, as well as model training, are often…
Serving Deep Learning Models with Deduplication from Relational Databases
Lixi Zhou, Jiaqing Chen, Amitabh Das +4
There are significant benefits to serve deep learning models from relational databases. First, features extracted from databases do not need to be transferred to any decoupled deep…
Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning
Zijie Wang, Lixi Zhou, Amitabh Das +3
Data is the king in the age of AI. However data integration is often a laborious task that is hard to automate. Schema change is one significant obstacle to the automation of the e…
It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing
Lixi Zhou, Zijie Wang, Amitabh Das +1
In recent, deep learning has become the most popular direction in machine learning and artificial intelligence. However, preparation of training data is often a bottleneck in the l…