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The goal of classical normalization is to maintain data consistency under updates, with a minimum level of effort. Given functional dependencies (FDs) alone, this goal is only achievable in the special case an FD-preserving Boyce-Codd Normal Form (BCNF) decomposition exists. As we show, in all other cases the level of effort can be neither controlled nor quantified. In response, we establish the l-Bounded Cardinality Normal Form, parameterized by a positive integer l. For every l, the normal form condition requires from every instance that every value combination over the left-hand side of every non-trivial FD does not occur in more than l tuples. BCNF is captured when l=1. We demonstrate that schemata in this normal form characterize the instances that are i) free from level l data redundancy and update inefficiency, and ii) permit level l join efficiency. We establish algorithms that compute schemata in l-Bounded Cardinality Normal Form for the smallest level l attainable across all FD-preserving decompositions. Additional algorithms i) attain even smaller levels of effort based on the loss of some FDs, and ii) decompose schemata based on prioritized FDs that cause high levels of effort. Our framework informs de-normalization already during logical design. In particular, level l quantifies both the incremental maintenance and join support of materialized views. Experiments with synthetic and real-world data illustrate which properties the schemata have that result from our algorithms, and how these properties predict the performance of update and query operations on instances over the schemata, without and with materialized views.

Embedded functional dependencies (eFDs) advance data management applications by data completeness and integrity requirements. We show that the discovery problem of eFDs is NP-complete, W[2]-complete in the output, and has a minimum solution space that is larger than the maximum solution space for functional dependencies. Nevertheless, we use novel data structures and search strategies to develop row-efficient, column-efficient, and hybrid algorithms for eFD discovery. Our experiments demonstrate that the algorithms scale well in terms of their design targets, and that ranking the eFDs by the number of redundant data values they cause can provide useful guidance in identifying meaningful eFDs for applications. Finally, we demonstrate the benefits of introducing completeness requirements and ranking by the number of redundant data values for approximate and genuine functional dependencies.

We showcase the first semantic data profiler, DataProf. For the constraint class of interest, current profilers compute all constraints that hold on the given data set. DataProf also computes perfect sample records that together satisfy the same constraints as the given data set. Such perfect samples make it easier to spot violations of business rules, which experts can cleanse. This novel iterative process of discovery and sampling facilitates the interaction of experts with the data, and provides the key to improving data quality and business rule acquisition. The demonstration will exemplify the process on a real-world data set and the novel class of embedded uniqueness constraints. The audience will experience how DataProf guides them in cleansing data and discovering business rules.

By persistently gathering information over social networks, a person can extract detailed accounts of the lives of others and monitor their daily routines. Such surveillance behaviors have posed serious privacy concerns. This paper addresses the question, "who is surveilling you through social networking?". Viewing a network as interconnected agents who interact through posting and retrieving information, we provide a measure to quantify the level of attention a person pays towards another. This measure allows us to capture online interpersonal surveillance.