Context Free Data Cleaning and its Application in Mechanism for Suggestive Data Cleaning

Research Statement Data Cleaning Algorithmic Data-cleaning Techniques

With the increasing amount of available data, turning raw data into actionable information is a requirement in every field. However, one bottleneck that impedes the process is data cleaning. Data analysts usually spend over half of their time cleaning data that is dirty — inconsistent, inaccurate, missing, and so on — before they even begin to do any real analysis. It is a time consuming and co...

Bayesian Data Cleaning for Web Data

Data Cleaning is a long standing problem, which is growing in importance with the mass of uncurated web data. State of the art approaches for handling inconsistent data are systems that learn and use conditional functional dependencies (CFDs) to rectify data. These methods learn data patterns–CFDs–from a clean sample of the data and use them to rectify the dirty/inconsistent data. While getting...

Pattern-Driven Data Cleaning

Data is inherently dirty and there has been a sustained effort to come up with different approaches to clean it. A large class of data repair algorithms rely on data-quality rules and integrity constraints to detect and repair the data. A well-studied class of integrity constraints is Functional Dependencies (FDs, for short) that specify dependencies among attributes in a relation. In this pape...

Data Cleaning for Decision Support

Data Cleaning for Word Alignment

Parallel corpora are made by human beings. However, as an MT system is an aggregation of state-of-the-art NLP technologies without any intervention of human beings, it is unavoidable that quite a few sentence pairs are beyond its analysis and that will therefore not contribute to the system. Furthermore, they in turn may act against our objectives to make the overall performance worse. Possible...