14 citations · 53 across the 15 of their papers we have counts for
15 papers
Saibot: A Differentially Private Data Search Platform
Zezhou Huang, Jiaxiang Liu, Daniel Alabi +2
Recent data search platforms use ML task-based utility measures rather than metadata-based keywords, to search large dataset corpora. Requesters submit a training dataset and these…
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez +1
High-quality machine learning models are dependent on access to high-quality training data. When the data are not already available, it is tedious and costly to obtain them. Data m…
Kitana: Efficient Data Augmentation Search for AutoML
Zezhou Huang, Pranav Subramaniam, Raul Castro Fernandez +1
AutoML services provide a way for non-expert users to benefit from high-quality ML models without worrying about model design and deployment, in exchange for a charge per hour ($21…
Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow
Siyuan Xia, Zhiru Zhu, Chris Zhu +7
Pooling and sharing data increases and distributes its value. But since data cannot be revoked once shared, scenarios that require controlled release of data for regulatory, privac…
METAM: Goal-Oriented Data Discovery
Sainyam Galhotra, Yue Gong, Raul Castro Fernandez
Data is a central component of machine learning and causal inference tasks. The availability of large amounts of data from sources such as open data repositories, data lakes and da…
Ver: View Discovery in the Wild
Yue Gong, Zhiru Zhu, Sainyam Galhotra +1
We present Ver, a data discovery system that identifies project-join views over large repositories of tables that do not contain join path information, and even when input queries…
Comprehensive and Comprehensible Data Catalogs: The What, Who, Where, When, Why, and How of Metadata Management
Pranav Subramaniam, Yintong Ma, Chi Li +2
Data management tasks require access to metadata, which is increasingly tracked by databases called data catalogs. Current catalogs are too dependent on users' understanding of dat…
The Data Station: Combining Data, Compute, and Market Forces
Raul Castro Fernandez, Kyle Chard, Ben Blaiszik +7
This paper introduces Data Stations, a new data architecture that we are designing to tackle some of the most challenging data problems that we face today: access to sensitive data…
ARDA: Automatic Relational Data Augmentation for Machine Learning
Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen +3
Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more…
Data Market Platforms: Trading Data Assets to Solve Data Problems
Raul Castro Fernandez, Pranav Subramaniam, Michael J. Franklin
Data only generates value for a few organizations with expertise and resources to make data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover an…
Dataset-On-Demand: Automatic View Search and Presentation for Data Discovery
Raul Castro Fernandez, Nan Tang, Mourad Ouzzani +2
Many data problems are solved when the right view of a combination of datasets is identified. Finding such a view is challenging because of the many tables spread across many datab…
Starling: A Scalable Query Engine on Cloud Function Services
Matthew Perron, Raul Castro Fernandez, David DeWitt +1
Much like on-premises systems, the natural choice for running database analytics workloads in the cloud is to provision a cluster of nodes to run a database instance. However, anal…
Termite: A System for Tunneling Through Heterogeneous Data
Raul Castro Fernandez, Samuel Madden
Data-driven analysis is important in virtually every modern organization. Yet, most data is underutilized because it remains locked in silos inside of organizations; large organiza…
Smallify: Learning Network Size while Training
Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez +2
As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along…
Extracting Syntactic Patterns from Databases
Andrew Ilyas, Joana M. F. da Trindade, Raul Castro Fernandez +1
Many database columns contain string or numerical data that conforms to a pattern, such as phone numbers, dates, addresses, product identifiers, and employee ids. These patterns ar…