Diagnosing the root causes of Quality of Experience (QoE) degradations in operational mobile networks is challenging due to complex cross-layer interactions among kernel performance indicators (KPIs) and the scarcity of reliable expert annotations. Although rule-based heuristics can generate labels at scale, they are noisy and coarse-grained, limiting the accuracy of purely data-driven approaches. To address this, we propose DK-Root, a joint data-and-knowledge-driven framework that unifies scalable weak supervision with precise expert guidance for robust root-cause analysis. DK-Root first pretrains an encoder via contrastive representation learning using abundant rule-based labels while explicitly denoising their noise through a supervised contrastive objective. To supply task-faithful data augmentation, we introduce a class-conditional diffusion model that generates KPIs sequences preserving root-cause semantics, and by controlling reverse diffusion steps, it produces weak and strong augmentations that improve intra-class compactness and inter-class separability. Finally, the encoder and the lightweight classifier are jointly fine-tuned with scarce expert-verified labels to sharpen decision boundaries. Extensive experiments on a real-world, operator-grade dataset demonstrate state-of-the-art accuracy, with DK-Root surpassing traditional ML and recent semi-supervised time-series methods. Ablations confirm the necessity of the conditional diffusion augmentation and the pretrain-finetune design, validating both representation quality and classification gains.

Time Series Management System (TSMS) is a specialized database management system designed for storing, querying, and analyzing time series data. Its correctness is essential for accurate data processing. However, logic bugs can lead to erroneous query outputs, severely compromising the reliability of data analysis. Compared with traditional relational database SQL, time series SQL exhibits significant syntactic and semantic differences, making existing tools inapplicable. To the best of our knowledge, the detection of logic bugs remains an open problem. In this paper, we propose TSGuard, a tool for detecting logic bugs in TSMSs via time series algebra. The core idea of TSGuard is to convert time series SQL queries into equivalent time series algebra expressions, evaluate these expressions to derive the expected result set, and then compare it with the actual query result set to detect potential logic bugs in the TSMS. Additionally, we introduce a feedback mechanism for query generation and develop query syntax validators for different TSMSs to improve the efficiency of logic bug detection. Through extensive testing, TSGuard discovered 48 previously unknown bugs, including 45 logic bugs and 3 crash bugs.