Yuwei Peng

Recent advances in large language models (LLMs) have motivated a new class of semantic data processing systems (SDPs) that integrate semantic reasoning into data processing over heterogeneous data lakes. Compared with traditional database systems, SDPs differ in three fundamental ways: (1) they operate over heterogeneous data lakes rather than curated relational tables; (2) they extend relational operators with LLM-powered semantic operators; (3) they increasingly take open-ended natural-language queries instead of precisely specified SQL. This survey provides a unified view of LLM-powered SDPs, including query analysis, semantic operator design, query optimization and execution, and relevant benchmarks. For each stage, we summarize representative techniques, their underlying motivations and design trade-offs. In addition, we further highlight major open challenges and future directions for this field.

Modern analytical workloads, particularly those powering business intelligence dashboards, are dominated by parameterized queries, where a small number of query templates are executed repeatedly with varying predicate values. Classical cost-based optimizers frequently fail to produce efficient execution plans in this setting due to inaccurate cardinality estimation and unstable plan choices. Although numerous learning-based optimizers have been proposed, most are designed for general, ad-hoc query optimization and overlook the repetitive structure of parameterized workloads. As a result, they often incur high training or serving overheads or require substantial modifications to existing database systems, limiting their practical adoption. We propose PLARQ, a practical learned optimizer tailored for Parameterized Query Optimization (PQO). We begin by analyzing existing approaches through a unified two-stage framework: (1) Plan Candidate Generation (PCG), which forms a set of plausible execution plans, and (2) Plan Ranking (PR), which selects the most promising one. This abstraction captures the core design principles of prior work and provides a foundation for systematic comparison. Building on this framework, PLARQ employs a similarity-based PCG approach to retrieve a compact, high-quality set of plan candidates from a precomputed plan pool, and a list-wise, attention-based ranking model to effectively identify the optimal plan among them. PLARQ integrates seamlessly with PostgreSQL without modifying the optimizer internals. Extensive experiments across five benchmarks demonstrate that PLARQ improves end-to-end query performance, achieving speedups of up to 420.31x over PostgreSQL and up to 2x over existing learned methods.

Small file management is widely encountered in industrial areas. Consolidating small files can benefit the performance of the data management system. Many existing consolidation solutions fail to realize the importance of a proper consolidation schema. Therefore, they use very primitive and ineffective schemas. In this paper, we focus on proposing an effective and robust consolidation schema. Unlike most of the existing solutions that only focus on the historical workload, we consider the workload uncertainty issue and propose a graph-clustering-based solution that is more robust to workload uncertainty in the future. To do this, we introduce robust optimization, a mathematical model that provides theoretical support for solving uncertainty issues. Then, we demonstrate that the robustness of the consolidation schema can be achieved using a graph clustering algorithm with a duplication mechanism. Since duplication leads to data redundancy, we propose a parameter to control the redundancy and the robustness of the schema. We also propose two algorithms to estimate the parameter automatically. Experimental results on both the synthetic and real-life data sets show the effectiveness of our algorithm.

Query optimizer is at the heart of the database systems. Cost-based optimizer studied in this paper is adopted in almost all current database systems. A cost-based optimizer introduces a plan enumeration algorithm to find a (sub)plan, and then uses a cost model to obtain the cost of that plan, and selects the plan with the lowest cost. In the cost model, cardinality, the number of tuples through an operator, plays a crucial role. Due to the inaccuracy in cardinality estimation, errors in cost model, and the huge plan space, the optimizer cannot find the optimal execution plan for a complex query in a reasonable time. In this paper, we first deeply study the causes behind the limitations above. Next, we review the techniques used to improve the quality of the three key components in the cost-based optimizer, cardinality estimation, cost model, and plan enumeration. We also provide our insights on the future directions for each of the above aspects.

With the rapid development of GIS and computer techniques, vector map data has been widely used in many fields. Since the production of map data is very costly, illegal copying will result in huge loss for data owners. In order to protect the copyright of vector data, digital watermarking has been employed in recent years. In this paper, a zero-watermarking scheme for vector map data is proposed. In the proposed scheme, FVDR (feature vertex distance ratio) is constructed based on the feature vertices of objects. The feature data, FVDR, is combined with watermark to generate the zero-watermark. Due to the specially designed cover data, the proposed scheme is robust to geometrical attacks, vertex attacks, and object attacks. The results of extensive experiments also demonstrate the robustness of the proposed scheme.