Functional dependencies (FDs) have been extensively employed in discovering inferential relationships in databases, which provide feasible approaches for many data mining tasks, such as data obfuscation, query optimization, and schema normalization. Since the explosive growth of data leads to a rapid increase of FDs on large datasets, existing algorithms that pay more attention to the exact FD discovery cannot extract FDs efficiently. To bridge this gap, we propose an Efficient double-cycle approximation of Functional Dependency (EulerFD) discovery algorithm, which ensures both efficiency and accuracy of FD discovery. EulerFD induces FDs from invalid ones as invalidating an FD only requires comparing and verifying some pairs of tuples (that violate the dependency) while validating an FD requires examining and verifying all tuples. Considering the abundant tuple pairs in large datasets, a novel sampling strategy is employed in EulerFD to quickly extract invalid FDs by revising the sampling range according to previous sampling results. Furthermore, EulerFD evaluates the stopping criteria in a double-cycle structure as feedback for further sampling. The sampling strategy and the double-cycle structure complement each other to achieve a more efficient sampling effect. Experimental results on real-world and synthetic datasets, especially the massive datasets from DMS of Alibaba Cloud, justify the design and verify the efficiency and effectiveness of the proposed EulerFD.

Data-driven machine learning (ML) has witnessed great success across a variety of application domains. Since ML model training relies on a large amount of data, there is a growing demand for high-quality data to be collected for ML model training. Data markets can be employed to significantly facilitate data collection. In this work, we demonstrate Dealer, an en D -to-end model m a rketp l ace with diff e rential p r ivacy. Dealer consists of three entities, data owners, the broker, and model buyers. Data owners receive compensation for their data usages allocated by the broker; The broker collects data from data owners, builds and sells models to model buyers; Model buyers buy their target models from the broker. We demonstrate the functionalities of the three participating entities and the abbreviated interactions between them. The demonstration allows the audience to understand and experience interactively the process of model trading. The audience can act as a data owner to control what and how the data would be compensated, can act as a broker to price machine learning models with maximum revenue, as well as can act as a model buyer to purchase target models that meet expectations.