Data quality problems such as duplicate records, missing values, and violation of integrity constrains frequently appear in real world applications. Such problems cost enterprises billions of dollars annually, and might have unpredictable consequences in mission-critical tasks. The process of data cleaning refers to detecting and correcting errors in data in order to improve the data quality. Numerous efforts have been taken towards improving the effectiveness and the efficiency of the data cleaning. A major challenge in the data cleaning process is the inherent uncertainty about the cleaning decisions that should be taken by the cleaning algorithms (e.g., deciding whether two records are duplicates or not). Existing data cleaning systems deal with the uncertainty in data cleaning decisions by selecting one alternative, based on some heuristics, while discarding (i.e., destroying) all other alternatives, which results in a false sense of certainty. Furthermore, because of the complex dependencies among cleaning decisions, it is difficult to reverse the process of destroying some alternatives (e.g., when new external information becomes available). In most cases, restarting the data cleaning from scratch is inevitable whenever we need to incorporate new evidence. To address the uncertainty in the data cleaning process, we propose a new approach, called probabilistic data cleaning, that views data cleaning as a random process whose possible outcomes are possible clean instances (i.e., repairs). Our approach generates multiple possible clean instances to avoid the destructive aspect of current cleaning systems. In this dissertation, we apply this approach in the context of two prominent data cleaning problems: duplicate elimination, and repairing violations of functional dependencies (FDs). First, we propose a probabilistic cleaning approach for the problem of duplicate elimination. We define a space of possible repairs that can be efficiently generated. To achieve this goal, we concentrate on a family of duplicate detection approaches that are based on parameterized hierarchical clustering algorithms. We propose a novel probabilistic data model that compactly encodes the defined space of possible repairs. We show how to efficiently answer relational queries using the set of possible repairs. We also define new types of queries that reason about the uncertainty in the duplicate elimination process. Second, in the context of repairing violations of FDs, we propose a novel data cleaning approach that allows sampling from a space of possible repairs. Initially, we contrast the existing definitions of possible repairs, and we propose a new definition of possible repairs that can be sampled efficiently. We present an algorithm that randomly samples from this space, and we present multiple optimizations to improve the performance of the sampling algorithm. Third, we show how to apply our probabilistic data cleaning approach in scenarios where both data and FDs are unclean (e.g., due to data evolution or inaccurate understanding of the data semantics). We propose a framework that simultaneously modifies the data and the FDs while satisfying multiple objectives, such as consistency of the resulting data with respect to the resulting FDs, (approximate) minimality of changes of data and FDs, and leveraging the trade-off between trusting the data and trusting the FDs. In presence of uncertainty in the relative trust in data versus FDs, we show how to extend our cleaning algorithm to efficiently generate multiple possible repairs, each of which corresponds to a different level of relative trust.

Data quality problems such as duplicate records, missing values, and violation of integrity constrains frequently appear in real world applications. Such problems cost enterprises billions of dollars annually, and might have unpredictable consequences in mission-critical tasks. The process of data cleaning refers to detecting and correcting errors in data in order to improve the data quality. Numerous efforts have been taken towards improving the effectiveness and the efficiency of the data cleaning. A major challenge in the data cleaning process is the inherent uncertainty about the cleaning decisions that should be taken by the cleaning algorithms (e.g., deciding whether two records are duplicates or not). Existing data cleaning systems deal with the uncertainty in data cleaning decisions by selecting one alternative, based on some heuristics, while discarding (i.e., destroying) all other alternatives, which results in a false sense of certainty. Furthermore, because of the complex dependencies among cleaning decisions, it is difficult to reverse the process of destroying some alternatives (e.g., when new external information becomes available). In most cases, restarting the data cleaning from scratch is inevitable whenever we need to incorporate new evidence. To address the uncertainty in the data cleaning process, we propose a new approach, called probabilistic data cleaning, that views data cleaning as a random process whose possible outcomes are possible clean instances (i.e., repairs). Our approach generates multiple possible clean instances to avoid the destructive aspect of current cleaning systems. In this dissertation, we apply this approach in the context of two prominent data cleaning problems: duplicate elimination, and repairing violations of functional dependencies (FDs). First, we propose a probabilistic cleaning approach for the problem of duplicate elimination. We define a space of possible repairs that can be efficiently generated. To achieve this goal, we concentrate on a family of duplicate detection approaches that are based on parameterized hierarchical clustering algorithms. We propose a novel probabilistic data model that compactly encodes the defined space of possible repairs. We show how to efficiently answer relational queries using the set of possible repairs. We also define new types of queries that reason about the uncertainty in the duplicate elimination process. Second, in the context of repairing violations of FDs, we propose a novel data cleaning approach that allows sampling from a space of possible repairs. Initially, we contrast the existing definitions of possible repairs, and we propose a new definition of possible repairs that can be sampled efficiently. We present an algorithm that randomly samples from this space, and we present multiple optimizations to improve the performance of the sampling algorithm. Third, we show how to apply our probabilistic data cleaning approach in scenarios where both data and FDs are unclean (e.g., due to data evolution or inaccurate understanding of the data semantics). We propose a framework that simultaneously modifies the data and the FDs while satisfying multiple objectives, such as consistency of the resulting data with respect to the resulting FDs, (approximate) minimality of changes of data and FDs, and leveraging the trade-off between trusting the data and trusting the FDs. In presence of uncertainty in the relative trust in data versus FDs, we show how to extend our cleaning algorithm to efficiently generate multiple possible repairs, each of which corresponds to a different level of relative trust.