Welcome to Miller Lab homepage!
Our group works broadly in the area of data curation. This includes data integration, data quality, data cleaning, data provenance, and big data analytics.
Dr. Bobbie Cochrane from IBM New York will be giving an informal tutorial on Blockchain in the Database Lab (BA7230) on Monday, March 27 at 12:10pm.
Christina’s work, VIQS: Visual Interactive Exploration of Query Semantics, is accepted at ACM ESIDA 2017!
Jiang’s work, DeepSea: Progressive Workload-Aware Partitioning of Materialized Views in Scalable Data Analytics, is accepted at EDBT 2017!
The first metadata generator that can be used to evaluate a wide-range of integration tasks.
Dirty data is a serious problem for businesses leading to incorrect decision making, inefficient daily operations, and ultimately wasting both time and money. Dirty data often arises when domain constraints and business rules, meant to preserve data consistency and accuracy, are enforced incompletely or not at all in application code. In this work, we are studying how to detect errors in data. In a sister project, BART, we provide a scalable way to generate dirty data to systematically evaluate data cleaning systems.
A Linked Data Space for Clinical Drug Trials (a part of Linking Open Drug Data (LODD) project at W3C).
Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema that reflects the source data as accurately as possible. In this project, we address foundational and algorithmic issues related to the semantics of data exchange and to the query answering problem in the context of data exchange. These issues arise because, given a source instance, there may be many target instances that satisfy the constraints of the data exchange problem, or none at all.
Bahen Centre, Room 7230
40 St George St, Toronto, ON, M5S 2E4