14 citations · 53 across the 15 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…