Clean Answers over Dirty Databases: A Probabilistic Approach

TLDR

Duplicate detection is crucial for data integration, yet merging or eliminating duplicates remains difficult and often requires manual solutions, a challenge addressed in the ConQuer project at the University of Toronto. The authors propose a probabilistic approach that enables declarative query answering over duplicated data by assigning each duplicate a probability of belonging to the clean database. They rewrite queries on a database with duplicates to return each answer together with its probability of being in the clean database, using a semantics that can exploit arbitrary probability assignments and, when none are available, automatically generates them via tuple summaries, while experimentally evaluating the performance impact. The rewritten queries accurately reflect duplication semantics, helping users identify the most likely clean answers, and the experiments show that this approach incurs only negligible overhead in query execution time.

Abstract

The detection of duplicate tuples, corresponding to the same real-world entity, is an important task in data integration and cleaning. While many techniques exist to identify such tuples, the merging or elimination of duplicates can be a difficult task that relies on ad-hoc and often manual solutions. We propose a complementary approach that permits declarative query answering over duplicated data, where each duplicate is associated with a probability of being in the clean database. We rewrite queries over a database containing duplicates to return each answer with the probability that the answer is in the clean database. Our rewritten queries are sensitive to the semantics of duplication and help a user understand which query answers are most likely to be present in the clean database. The semantics that we adopt is independent of the way the probabilities are produced, but is able to effectively exploit them during query answering. In the absence of external knowledge that associates each database tuple with a probability, we offer a technique, based on tuple summaries, that automates this task. We experimentally study the performance of our rewritten queries. Our studies show that the rewriting does not introduce a significant overhead in query execution time. This work is done in the context of the ConQuer project at the University of Toronto, which focuses on the efficient management of inconsistent and dirty databases.