Capturing the characteristics of real-world analytical workloads is challenging yet critical for advancing industry practices and academic research. Historically, obtaining accurate query and data characteristics has been difficult, largely because detailed workload information has often been confined to on-premises database systems. With the rise of cloud-native databases like Snowflake, it has become possible to analyze production query workloads at scale and in greater detail. Leveraging this capability, this study presents a comprehensive analysis of analytics workloads across diverse customers and industries. In particular, we investigate the query characteristics of 667 million queries issued by the most popular BI tools against Snowflake over a two-week period. Based on this dataset, this paper makes two primary contributions: first, we conduct a detailed examination of query properties, with particular attention to filters, joins, aggregations, and other previously underexplored aspects. Second, we uncover unique and practically relevant query patterns that are typically absent from standard database benchmarks.

From 2017 to 2019 the Text REtrieval Conference (TREC) held a challenge task on precision medicine using documents from medical publications (PubMed) and clinical trials. Despite lots of performance measurements carried out in these evaluation campaigns, the scientific community is still pretty unsure about the impact individual system features and their weights have on the overall system performance. In order to overcome this explanatory gap, we first determined optimal feature configurations using the Sequential Model-based Algorithm Configuration (SMAC) program and applied its output to a BM25-based search engine. We then ran an ablation study to systematically assess the individual contributions of relevant system features: BM25 parameters, query type and weighting schema, query expansion, stop word filtering, and keyword boosting. For evaluation, we employed the gold standard data from the three TREC Precision Medicine (TREC-PM) installments to evaluate the effectiveness of different features using the commonly shared infNDCG metric.

OBJECTIVE: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset. We sought to evaluate shallow and deep learning text classifiers and the impact of pretrained embeddings in a small clinical dataset. MATERIALS AND METHODS: We participated in the 2018 National NLP Clinical Challenges (n2c2) Shared Task on cohort selection and received an annotated dataset with medical narratives of 202 patients for multilabel binary text classification. We set our baseline to a majority classifier, to which we compared a rule-based classifier and orthogonal machine learning strategies: support vector machines, logistic regression, and long short-term memory neural networks. We evaluated logistic regression and long short-term memory using both self-trained and pretrained BioWordVec word embeddings as input representation schemes. RESULTS: Rule-based classifier showed the highest overall micro F1 score (0.9100), with which we finished first in the challenge. Shallow machine learning strategies showed lower overall micro F1 scores, but still higher than deep learning strategies and the baseline. We could not show a difference in classification efficiency between self-trained and pretrained embeddings. DISCUSSION: Clinical context, negation, and value-based criteria hindered shallow machine learning approaches, while deep learning strategies could not capture the term diversity due to the small training dataset. CONCLUSION: Shallow methods for clinical phenotyping can still outperform deep learning methods in small imbalanced data, even when supported by pretrained embeddings.