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In the current paper, we propose to fuse together stored data (tables) and their functional dependencies (FDs) inside a DBMS. We aim to make FDs first-class citizens: objects which can be queried and used to query data. Our idea is to allow analysts to explore both data and functional dependencies using the database interface. For example, an analyst may be interested in such tasks as: find all rows which prevent a given functional dependency from holding, for a given table, find all functional dependencies that involve a given attribute, project all attributes that functionally determine a specified attribute. For this purpose, we propose: (1) an SQL-based query language for querying a collection of functional dependencies (2) an extension of the SQL SELECT clause for supporting FD-based predicates, including approximate ones (3) a special data structure intended for containing mined FDs and acting as a mediator between user queries and underlying data. We describe the proposed extensions, demonstrate their use-cases, and finally, discuss implementation details and their impact on query processing.
Functional Dependencies (FDs) define attribute relationships based on syntactic equality, and, when usedin data cleaning, they erroneously label syntactically different but semantically equivalent values as errors. We explore dependency-based data cl
We propose a class of functional dependencies for temporal graphs, called TGFDs. TGFDs capture both attribute-value dependencies and topological structures of entities over a valid period of time in a temporal graph. It subsumes graph functional depe
Individuals and organizations tend to migrate their data to clouds, especially in a DataBase as a Service (DBaaS) pattern. The major obstacle is the conflict between secrecy and utilization of the relational database to be outsourced. We address this
As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and use their feedback on the returned res
Graph query languages feature mainly two kinds of queries when applied to a graph database: those inspired by relational databases which return tables such as SELECT queries and those which return graphs such as CONSTRUCT queries in SPARQL. The latte