Machine learning (ML) algorithms have advanced significantly in recent years, progressively evolving into artificial intelligence (AI) agents capable of solving complex, human-like intellectual challenges. Despite the advancements, the interpretability of these sophisticated models lags behind, with many ML architectures remaining "black boxes" that are too intricate and expansive for human interpretation. Recognizing this issue, there has been a revived interest in the field of explainable AI (XAI) aimed at explaining these opaque ML models. However, XAI tools often suffer from being tightly coupled with the underlying ML models and are inefficient due to redundant computations. We introduce provenance-enabled explainable AI (PXAI). PXAI decouples XAI computation from ML models through a provenance graph that tracks the creation and transformation of all data within the model. PXAI improves XAI computational efficiency by excluding irrelevant and insignificant variables and computation in the provenance graph. Through various case studies, we demonstrate how PXAI enhances computational efficiency when interpreting complex ML models, confirming its potential as a valuable tool in the field of XAI.

Alibaba relies on its robust database infrastructure to facilitate realtime data access and ensure business continuity despite regional disruptions. To address these operational imperatives, Alibaba developed the Data Transmission Service (DTS), which has become critical for internal applications and public cloud services alike. This paper presents a comprehensive study of the architectural innovations, resource scheduling mechanisms, and performance optimization strategies that have been implemented within DTS to tackle the significant challenges of cross-network, heterogeneous data transmission in a cost-effective manner. We explore the novel Any-to-Any (A2A) architecture, which simplifies the complexity of data paths between diverse databases and mitigates network connectivity issues, thereby significantly reducing development overhead. Additionally, we examine a dynamic network bandwidth scheduling algorithm that effectively maintains Service-Level Objectives (SLOs), complemented by a serverless mechanism that ensures efficient resource utilization. Furthermore, DTS utilizes advanced strategies such as transaction dependency tracking, hot data consolidation, and batching to enhance synchronization performance and efficiency. DTS has distilled the lessons learned from years of serving our customer base and currently supports nearly 1 million public cloud instances annually. Our evaluation results show that DTS can effectively and efficiently handle real-time data transmission in both experimental and production environments.