Agreement graphs and data dependencies

The problem of deciding whether a join dependency [R] and a set F of functional dependencies logically imply an embedded join dependency [S] is known to be NP-complete. It is shown that if the set F of functional dependencies is required to be embedded in R, the problem can be decided in polynomial time. The problem is approached by introducing agreement graphs, a type of graph structure which helps expose the combinatorial structure of dependency implication problems. Agreement graphs provide an alternative formalism to tableaus and extend the application of graph and hypergraph theory in relational database research;Agreement graphs are also given a more abstract definition and are used to define agreement graph dependencies (AGDs). It is shown that AGDs are equivalent to Fagin\u27s (unirelational) embedded implicational dependencies. A decision method is given for the AGD implication problem. Although the implication problem for AGDs is undecidable, the decision method works in many cases and lends insight into dependency implication. A number of properties of agreement graph dependencies are given and directions for future research are suggested

Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).Comment: 5 pages, GlobalSIP 201

Graphical and Spreadsheet Reasoning for Sets of Functional Dependendies

Reasoning on constraint sets is a difficult task. Classical database design is based on a step-wise extension of the constraint set and on a consideration of constraint sets through generation by tools. Since the database developer must master semantics acquisition, tools and approaches are still sought that support reasoning on sets of constraints. We propose novel approaches for presentation of sets of functional dependencies based on specific graphs and spreadsheets. These approaches may be used for the elicitation of the full knowledge on validity of functional dependencies in relational schemata