SQL query comprehension is a significant challenge in database and data analysis environments due to complex syntax, diverse join types, and deep nesting. Despite its critical role in backend development and data science, many queries, particularly within legacy systems, often lack adequate comments, which severely hinders code readability, maintainability, and knowledge transfer. Existing approaches to automated SQL comment generation face two main challenges: limited training datasets that inadequately represent real-world analytical queries involving multi-table joins, window functions, and complex aggregations, and an insufficient understanding of SQL-specific logical semantics and schema-related context by Large Language Models (LLMs), even after standard training. Our empirical analysis shows that even after continual pre-training and supervised fine-tuning, LLMs struggle to precisely understand complex SQL semantics, leading to inaccurate or incomplete comments. To address these challenges, we propose SQL-Commenter, an advanced comment generation method based on LLaMA-3.1-8B. First, we construct a comprehensive dataset containing longer, more complex SQL queries with expert-verified, detailed comments. Second, we perform continual pre-training using a large-scale SQL corpus to enhance the LLM’s understanding of SQL syntax and semantics. Then, we conduct supervised fine-tuning with our high-quality dataset. Finally, we introduce Direct Preference Optimization (DPO), which leverages human feedback to significantly improve comment quality. SQL-Commenter utilizes a preference-based loss function that encourages the LLM to increase the probability of preferred outputs while decreasing the probability of non-preferred outputs, thereby enhancing both fine-grained semantic learning, such as distinguishing between different join types, and context-dependent quality assessment based on business logic. We evaluate SQL-Commenter on the authoritative Spider and Bird benchmarks, where it significantly outperforms state-of-the-art baselines. On average, across these datasets, our method surpasses the strongest baseline (Qwen3-14B) by 9.29, 4.99, and 13.23 percentage points on BLEU-4, METEOR, and ROUGE-L, respectively. Moreover, human evaluation demonstrates the superior quality of comments generated by SQL-Commenter in terms of correctness, completeness, and naturalness.

Plain text documents created and distributed on the Internet are ever changing in various forms. Mining topics of these documents has significant applications in many domains. Most of the literature is devoted to topic modeling, while sequential patterns of topics in document streams are ignored. Moreover, traditional sequential pattern mining algorithms mainly focused on frequent patterns for deterministic data sets, and thus not suitable for document streams with topic uncertainty and rare patterns. In this paper, we formulate and handle the mining problem of rare Sequential Topic Patterns (STPs) for Internet document streams, which are rare on the whole but relatively often for specific users, so also interesting. Since this type of rare STPs reflects users’ specific behaviors, our work can be applied in many fields, such as personalized context-aware recommendation and real-time monitoring on abnormal user behaviors on the Internet. We propose a novel approach to discovering user-related rare STPs based on the temporal and probabilistic information of concerned topics. After extracting topics from documents by LDA and sorting the document stream into sessions for different users during different time periods, the proposed algorithms discover rare STPs by (1) mining STP candidates for each user through an efficient algorithm based on pattern-growth, and (2) generating user-related rare STPs by pattern rarity analysis. Experiments on both synthetic and real data sets show that our approach can discover interesting rare STPs very effectively and efficiently.

Cooperative design is one of the most important subjects of the researches on CSCW. Distributed cognition is a useful theoretical framework applied to analyzing, designing, implementing, and evaluating cooperative design systems. In this paper, we construct a detailed distributed cognition framework, explain these terms in it, and implement a prototype of the cooperative design system, smart cooperative design system (SCDS), based on the framework. In SCDS, the computer can infer not only local participant intent but also team intent by capturing information distributed external environment and individual historical interactions. Cooperative participants can learn the intent each other through information representation distributed over individuals and artifacts. In some extent, this avoids some collaborative conflict.