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Organizations collect data from various sources, and these datasets may have characteristics that are unknown. Selecting the appropriate statistical and machine learning algorithm for data analytical purposes benefits from understanding these characteristics, such as if it contains temporal attributes or not. This paper presents a theoretical basis for automatically determining the presence of temporal data in a dataset given no prior knowledge about its attributes. We use a method to classify an attribute as temporal, non-temporal, or hidden temporal. A hidden (grouping) temporal attribute can only be treated as temporal if its values are categorized in groups. Our method uses a Ljung-Box test for autocorrelation as well as a set of metrics we proposed based on the classification statistics. Our approach detects all temporal and hidden temporal attributes in 15 datasets from various domains. 

Organizations collect data from various sources, and these datasets may have characteristics that are unknown. Selecting the appropriate statistical and machine learning algorithm for data analytical purposes benefits from understanding these characteristics, such as if it contains temporal attributes or not. This paper presents a theoretical basis for automatically determining the presence of temporal data in a dataset given no prior knowledge about its attributes. We use a method to classify an attribute as temporal, non-temporal, or hidden temporal. A hidden (grouping) temporal attribute can only be treated as temporal if its values are categorized in groups. Our method uses a Ljung-Box test for autocorrelation as well as a set of metrics we proposed based on the classification statistics. Our approach detects all temporal and hidden temporal attributes in 15 datasets from various domains. 

The quality of data is extremely important for data analytics. Data quality tests typically involve checking constraints specified by domain experts. Existing approaches detect trivial constraint violations and identify outliers without explaining the constraints that were violated. Moreover, domain experts may specify constraints in an ad hoc manner and miss important ones. We describe an automated data quality test approach, ADQuaTe2, which uses an autoencoder to (1) discover constraints that may have been missed by experts, (2) label as suspicious those records that violate the constraints, and (3) provide explanations about the violations. An interactive learning technique incorporates expert feedback, which improves the accuracy. We evaluate the effectiveness of ADQuaTe2 on real-world datasets from health and plant domains. We also use datasets from the UCI repository to evaluate the improvement in the accuracy after incorporating ground truth knowledge.