Integrity constraints (ICs) provide a valuable tool for enforcing correct application semantics. However, designing ICs requires experts and time. Proposals for automatic discovery have been made for some formalisms, such as functional dependencies and their extension conditional functional dependencies. Unfortunately, these dependencies cannot express many common business rules. For example, an American citizen cannot have lower salary and higher tax rate than another citizen in the same state. In this paper, we tackle the challenges of discovering dependencies in a more expressive integrity constraint language, namely Denial Constraints (DCs). DCs are expressive enough to overcome the limits of previous languages and, at the same time, have enough structure to allow efficient discovery and application in several scenarios. We lay out theoretical and practical foundations for DCs, including a set of sound inference rules and a linear algorithm for implication testing. We then develop an efficient instance-driven DC discovery algorithm and propose a novel scoring function to rank DCs for user validation. Using real-world and synthetic datasets, we experimentally evaluate scalability and effectiveness of our solution.

The chase is a family of algorithms used in a number of data management tasks, such as data exchange, answering queries under dependencies, query reformulation with constraints, and data cleaning. It is well established as a theoretical tool for understanding these tasks, and in addition a number of prototype systems have been developed. While individual chase-based systems and particular optimizations of the chase have been experimentally evaluated in the past, we provide the first comprehensive and publicly available benchmark-test infrastructure and a set of test scenarios-for evaluating chase implementations across a wide range of assumptions about the dependencies and the data. We used our benchmark to compare chase-based systems on data exchange and query answering tasks with one another, as well as with systems that can solve similar tasks developed in closely related communities. Our evaluation provided us with a number of new insights concerning the factors that impact the performance of chase implementations.

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Data-cleaning (or data-repairing) is considered a crucial problem in many database-related tasks. It consists in making a database consistent with respect to a set of given constraints. In recent years, repairing methods have been proposed for several classes of constraints. However, these methods rely on ad hoc decisions and tend to hard-code the strategy to repair conflicting values. As a consequence, there is currently no general algorithm to solve database repairing problems that involve different kinds of constraints and different strategies to select preferred values. In this paper we develop a uniform framework to solve this problem. We propose a new semantics for repairs, and a chase-based algorithm to compute minimal solutions. We implemented the framework in a DBMSbased prototype, and we report experimental results that confirm its good scalability and superior quality in computing repairs.

The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker --- a professional whose main aim is to examine claims using available evidence and to assess their veracity. Here, we survey the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor. These include identifying claims worth fact-checking, detecting relevant previously fact-checked claims, retrieving relevant evidence to fact-check a claim, and actually verifying a claim. In each case, we pay attention to the challenges and the potential impact on real-world fact-checking.