Large Language Models (LLMs) have recently demonstrated exceptional performance in various real-world data management tasks through in-context learning (ICL), which involves structuring prompts with task descriptions and several demonstrations. However, most LLMs are not free and charge based on the number of input tokens. Specifically, for data management tasks, there may be massive related questions, leading to high inference cost due to redundant prompt content (i.e., overlapping demonstrations and repeated task descriptions). In this paper, we investigate the idea of batch prompting in leveraging LLMs for data management, which leads to cost-effective LLMs by grouping questions and demonstrations to perform inferences in batches. Current studies on batch prompting are preliminary and mostly based on heuristics, making it difficult to generalize to various types of tasks and adapt to different grouping strategies. To address these challenges, in this work we first formalize the batch prompting problem in general setting. Then, we study the hardness of this problem and propose efficient algorithms for adaptive grouping. Finally, we conduct comprehensive experiments on 14 datasets. Extensive experimental results demonstrate that our solution consistently outperforms the state-of-the-art baselines while consuming lower cost.

Data management is indispensable for informed decision-making in the big data era. In the meantime, Large Language Models (LLMs), equipped with billions of model parameters and trained on extensive data corpora, have recently achieved record-breaking results in various real-world applications, such as machine translation, content generation, information retrieval, etc. The emergent abilities of LLMs, e.g., in-context learning and advanced reasoning ability, have great potential to revolutionize data management. In this paper, we first present some promising categories of data management applications where LLMs can be adapted, including data generation, data transformation, data integration, and data exploration. We then discuss the corresponding challenges for such adaption. Finally, we envision potential solutions to these challenges.

Caching is a crucial solution to alleviate the high latency and low bandwidth of cloud databases. However, existing caching algorithms are not suitable for cloud databases as 1) they cannot ensure the adaptability to changing workloads; 2) they are not designed with awareness of data fetching costs. Combining learning-based models with cost-aware caching algorithms is natural for better performance. However, it is challenging due to the absence of the oracle algorithm for guiding the learning model. Moreover, current learning models incur significant computation overheads, potentially worsening the performance of cloud databases. In this paper, we propose a learning-based cost-aware caching framework called LBSC for cloud databases, ensuring faster query execution and robust performance in dynamic workloads. We first introduce an approximately optimal oracle algorithm called BeladySizeCost, which retains data items with high cost per byte that are likely to be accessed in near future. Then, we present a lightweight supervised learning-based model that learns from BeladySizeCost to predict the eviction probability of the cached data. Moreover, we design effective optimizations to reduce the computation overheads of the learning-based algorithm. Extensive experiments in both simulations and real-world cloud databases demonstrate that the proposed framework significantly outperforms the state-of-the-art baselines.