Deploying machine learning (ML) inference pipelines in databases become increasingly prevalent in many applications. In order to avoid data transfer between the database and ML runtimes, existing ML2SQL frameworks parse ML pipelines to a graph consisting of ML operators and then translate it into pure SQL. Nevertheless, they typically rewrite the graph without operator fusion or only consider the fusion between certain operators such as StandardScaler and tree inference. However, there are various operators in ML pipelines, which have rich fusion opportunities between each other. To fully exploit operator fusion for graph rewriting, we classify widely used ML operators and design fusion rules driven by their characteristics. Moreover, rewriting the original graph by fusion rules produces candidate graphs that generate SQLs with different execution time. We employ an enumeration-based strategy to search for the graph with the lowest cost. However, this strategy may suffer from the combination explosion on search space for complex ML pipelines. To reduce this space, we propose a greedy-based strategy by exploiting the independence among ML operators. We implement a novel ML2SQL framework as a portable plugin for databases, namely Craftsman. Our experimental evaluations show that, in comparison to the existing approaches, Craftsman generates efficient SQL queries which achieves an average speedup of 2.9x on popular databases such as DuckDB.

In the cloud era, hosting-based email services have become a common business model. Various entities can participate in the email delivery process. However, the intermediate paths of email delivery have received little attention. In particular, the vulnerabilities and centralization of email intermediate paths have already posed real-world security threats. This paper conducts the first systematic analysis of intermediate paths of email delivery, aiming to understand dependence patterns and characterize the centralization. In collaboration with a large email service provider, we collected Received headers from email reception logs spanning nine months and reconstructed the complete intermediate paths of 105M clean emails. Our results reveal that Microsoft is the dominant provider of intermediate paths, participating in 66.4% of emails. We find that 86.9M (82.7%) emails rely on third-party providers in intermediate paths, and 9.1M (8.7%) paths involve multiple providers. Email signature providers frequently appear in cross-vendor intermediate paths. In addition, we reveal significant differences in the regional dependencies and centralization of email intermediate paths across countries and continents. The centralization observed in email intermediate paths also differs from incoming and outgoing servers. We hope our work prompts more attention to email intermediate paths to enhance the security of the email ecosystem.

Abnormal email bounces seriously disrupt user lives and company transactions. Proliferating security protocols and protection strategies have made email delivery increasingly complex. A natural question is how and why email delivery fails in the wild. Filling this knowledge gap requires a representative global email delivery dataset, which is rarely disclosed by email service providers (ESPs).