Functional dependencies (FDs) specify the intended data semantics while violations of FDs indicate deviation from these semantics. In this paper, we study a data cleaning problem in which the FDs may not be completely correct, e.g., due to data evolution or incomplete knowledge of the data semantics. We argue that the notion of relative trust is a crucial aspect of this problem: if the FDs are outdated, we should modify them to fit the data, but if we suspect that there are problems with the data, we should modify the data to fit the FDs. In practice, it is usually unclear how much to trust the data versus the FDs. To address this problem, we propose an algorithm for generating non-redundant solutions (i.e., simultaneous modifications of the data and the FDs) corresponding to various levels of relative trust. This can help users determine the best way to modify their data and/or FDs to achieve consistency.

Many large-scale Web applications that require ranked top-k retrieval are implemented using inverted indices. An inverted index represents a sparse term-document matrix, where non-zero elements indicate the strength of term-document associations. In this work, we present an approach for lossless compression of inverted indices. Our approach maps terms in a document corpus to a new term space in order to reduce the number of non-zero elements in the term-document matrix, resulting in a more compact inverted index. We formulate the problem of selecting a new term space as a matrix factorization problem, and prove that finding the optimal solution is an NP-hard problem. We develop a greedy algorithm for finding an approximate solution. A side effect of our approach is increasing the number of terms in the index, which may negatively affect query evaluation performance. To eliminate such effect, we develop a methodology for modifying query evaluation algorithms by exploiting specific properties of our compression approach.

One of the most prominent data quality problems is the existence of duplicate records. Current data cleaning systems usually produce one clean instance (repair) of the input data, by carefully choosing the parameters of the duplicate detection algorithms. Finding the right parameter settings can be hard, and in many cases, perfect settings do not exist. We propose ProbClean, a system that treats duplicate detection procedures as data processing tasks with uncertain outcomes. We use a novel uncertainty model that compactly encodes the space of possible repairs corresponding to different parameter settings. ProbClean efficiently supports relational queries and allows new types of queries against a set of possible repairs.

Violations of functional dependencies (FDs) are common in practice, often arising in the context of data integration or Web data extraction. Resolving these violations is known to be challenging for a variety of reasons, one of them being the exponential number of possible "repairs". Previous work has tackled this problem either by producing a single repair that is (nearly) optimal with respect to some metric, or by computing consistent answers to selected classes of queries without explicitly generating the repairs. In this paper, we propose a novel data cleaning approach that is not limited to finding a single repair or to a particular class of queries, namely, sampling from the space of possible repairs. We give several motivating scenarios where sampling from the space of FD repairs is desirable, propose a new class of useful repairs, and present an algorithm that randomly samples from this space. We also show how to restrict the space of generated repairs based on user-defined hard constraints that define an immutable trusted subset of the input relation, and we experimentally evaluate our algorithm against previous approaches. While this paper focuses on repairing FDs, we envision the proposed sampling approach to be applicable to other integrity constraints with large repair spaces.

One of the most prominent data quality problems is the existence of duplicate records. Current duplicate elimination procedures usually produce one clean instance (repair) of the input data, by carefully choosing the parameters of the duplicate detection algorithms. Finding the right parameter settings can be hard, and in many cases, perfect settings do not exist. Furthermore, replacing the input dirty data with one possible clean instance may result in unrecoverable errors, for example, identification and merging of possible duplicate records in health care systems. In this paper, we treat duplicate detection procedures as data processing tasks with uncertain outcomes. We concentrate on a family of duplicate detection algorithms that are based on parameterized clustering. We propose a novel uncertainty model that compactly encodes the space of possible repairs corresponding to different parameter settings. We show how to efficiently support relational queries under our model, and to allow new types of queries on the set of possible repairs. We give an experimental study illustrating the scalability and the efficiency of our techniques in different configurations.

Uncertainty pervades many domains in our lives. Current real-life applications, e.g., location tracking using GPS devices or cell phones, multimedia feature extraction, and sensor data management, deal with different kinds of uncertainty. Finding the nearest neighbor objects to a given query point is an important query type in these applications. In this paper, we study the problem of finding objects with the highest marginal probability of being the nearest neighbors to a query object. We adopt a general uncertainty model allowing for data and query uncertainty. Under this model, we define new query semantics, and provide several efficient evaluation algorithms. We analyze the cost factors involved in query evaluation, and present novel techniques to address the trade-offs among these factors. We give multiple extensions to our techniques including handling dependencies among data objects, and answering threshold queries. We conduct an extensive experimental study to evaluate our techniques on both real and synthetic data.