We conduct research that spans several areas of database systems such as information integration, data provenance, scheduling, stream processing, and data mining. Our main contributions impacted the theoretical and practical research in data provenance. We strive to develop solutions to emerging challenges in database systems such as tight integration of provenance support into database engines, provenance for distributed data processing paradigms, and provenance for sequences of database operations. For a list of our publications click here.
BART is an error-generation tool for data cleaning applications. Its purpose is to introduce errors into clean databases for the purpose of benchmarking data-repairing algorithms.
developing algorithms and systems for scaling provenance to Big Data dimensions.
Explaining Outliers in Query Results Beyond Provenance
A database-independent middleware for computing the provenance of queries, updates, and transactions
is a distributed database build from scratch that combines the best of traditional relational platforms with ideas from Big Data platforms.
A new, generic benchmark generator for data integration tasks.
LDV is a lightweight database virtualization system marrying OS and DB provenance.
PUGS is a unified framework for capturing why and why-not provenance of Datalog queries with negation and for automatic generation of concise provenance summaries.
Relevance-based Data Management
We use provenance to determine what data is relevant for which task and then exploit this information to improve a wide range of data management tasks.
Provenace for Updates and Transactions
In this project, we study provenance models for update and transactions and their implementation through reenactment, a declarative replay technique which utilizes audit logs and temporal database technologies.
A framework for user-friendly and effective data curation.
Computing fine-grained Provenance for Data Streams using Operator Instrumentation
Native Database Provenance
In this project we study how to integrate provenance techniques with a database core to improve various aspects of provenance managements including performance and storage requirements.
Declarative modelling and implementation of domain specific scheduling protocols.
Efficient Provenance Support for Relational Databases
Understanding the Behavior of Schema Mappings though Provenance and Meta-querying
Automatic generation of explanations for data exchange errors.
SponsorsWe would like to thank the following sponsors for their support:
CollaboratorsWe are grateful to our awesome current and past collaborators!
- Aaron Huber - SUNY Buffalo
- Alex Rasin - DePaul University
- Anton Dignös - Free University of Bozen/Bolzano
- Bertram Ludäscher - UIUC
- Carl-Christian Kanne - Platfora
- Christian Tilgner
- Dieter Gawlick - Oracle
- Donatello Santoro - Università della Basilicata
- Eric Houston
- Giansalvatore Mecca - Università della Basilicata
- Gustavo Alonso - ETH Zurich
- Ioan Raicu - Illinois Institute of Technology
- James Wagner - DePaul University
- Jiang Du
- Johann Gamper - Free University of Bozen/Bolzano
- Juliana Freire - NYU
- Kenny Gross - Oracle
- Kyle Hale - Illinois Institute of Technology
- Kyumars Sheyk Esmaili
- Laura Haas - UMass Amherst
- Melanie Herschel - University of Stuttgart
- Michael H. Böhlen - University of Zurich
- Nesime Tatbul - Intel Labs
- Oliver Kennedy - SUNY Buffalo
- Paolo Papotti - Eurecom
- Patricia C. Arocena - TD
- Periklis Andritsos - University of Toronto
- Peter M. Fischer - University of Augsburg
- Radu Ciucanu - Université d'Orléans
- Renée J. Miller - Northeastern University
- Sudeepa Roy - Duke University
- Sven Köhler - Google
- Tanu Malik - DePaul University
- Vasudha Krishnaswamy - Oracle
- Venkatesh Radhakrishnan
- Zhen Hua Liu - Oracle
- Zhengjie Miao - Duke University