An important obstacle to accurate data analytics is dirty data in the form of missing, duplicate, incorrect, or inconsistent values. In the SampleClean project, we have developed a new suite of techniques to estimate the results of queries when only a sample of data can be cleaned. Some forms of data corruption, such as duplication, can affect sampling probabilities, and thus, new techniques have to be designed to ensure correctness of the approximate query results. We first describe our initial project on computing statistically bounded estimates of sum, count, and avg queries from samples of cleaned data. We subsequently explored how the same techniques could apply to other problems in database research, namely, materialized view maintenance. To avoid expensive incremental maintenance, we maintain only a sample of rows in a view, and then leverage SampleClean to approximate aggregate query results. Finally, we describe our work on a gradient-descent algorithm that extends the key ideas to the increasingly common Machine Learning-based analytics.

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