Modern AI and vector search are rapidly converging, forming a promising research frontier in intelligent information systems. On one hand, advances in AI have substantially improved the semantic accuracy and efficiency of vector search, including learned indexing structures, adaptive pruning strategies, and automated parameter tuning. On the other hand, powerful vector search techniques have enabled new AI paradigms, notably Retrieval-Augmented Generation (RAG), which effectively mitigates challenges in Large Language Models (LLMs) like knowledge staleness and hallucinations. This mutual reinforcement establishes a virtuous cycle where AI injects intelligence and adaptive optimization into vector search, while vector search, in turn, expands AI's capabilities in knowledge integration and context-aware generation. This tutorial provides a comprehensive overview of recent research and advancements at this intersection. We begin by discussing the foundational background and motivations for integrating vector search and AI. Subsequently, we explore how AI empowers vector search (AI4VS) across each step of the vector search pipeline. We then investigate how vector search empowers AI (VS4AI), with a particular focus on RAG frameworks that integrate dynamic, external knowledge sources into the generative process of LLMs. Furthermore, we analyze end-to-end co-optimization strategies that fully unlock the potential of the ``virtuous cycle" between vector search and AI. Finally, we highlight key challenges and future research opportunities in this emerging area. This paper was published in ICDE 2026.

Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search with theoretical guarantees. Traditional LSH-based methods mainly focus on improving the efficiency and accuracy of query phase by designing different query strategies, but pay little attention to improving the efficiency of the indexing phase. They typically fine tune existing data-oriented partitioning trees to index data points and support their query strategies. However, their strategy to directly partition the multidimensional space is time-consuming, and performance degrades as the space dimensionality increases. In this paper, we design an encoding-based tree called Dynamic Encoding Tree (DE-Tree) to improve the indexing efficiency and support efficient range queries. Based on DE-Tree, we propose a novel LSH scheme called DET-LSH. DET-LSH adopts a novel query strategy, which performs range queries in multiple independent index DE-Trees to reduce the probability of missing exact NN points. Extensive experiments demonstrate that while achieving best query accuracy, DET-LSH achieves up to 6x speedup in indexing time and 2x speedup in query time over the state-of-the-art LSH-based methods. In addition, to further improve the performance of DET-LSH, we propose PDET-LSH, an in-memory method adopting the parallelization opportunities provided by multicore CPUs. PDET-LSH exhibits considerable advantages in indexing and query efficiency, especially on large scale datasets. Extensive experiments show that, while achieving the same query accuracy as DET-LSH, PDET-LSH offers up to 40x speedup in indexing time and 62x speedup in query answering time over the state-of-the-art LSH-based methods. Our theoretical analysis demonstrates that DET-LSH and PDET-LSH offer probabilistic guarantees on query answering accuracy.

Cardinality Estimation (CardEst) is crucial for query optimization. Despite the remarkable achievement in DBMS, there is a pressing need to test or tune the work of CardEst. To satisfy the need, we introduce Artemis, a customizable workload generator, which can be used to generate various scenarios with the sensitive features for CardEst, including various data dependencies, complex SQL structures, and diverse cardinalities. It designs a PK-oriented deterministic data generation mechanism to plot various data characteristics; a search-based workload generation is proposed for composing queries with various complexities; it takes a constraint optimization-guided way to achieve a cost-effective cardinality calculation. In this demonstration, users can explore the core features of Artemis in generating workloads.

In the era of big data, the ability to process and analyze large volumes of data is critical for decision-making, marketing and sales, healthcare and scientific research, etc. However, this capability also brings significant challenges related to data security and privacy. Secure and private query processing is essential to address these challenges. It ensures the protection of private and confidential data, compliance with stringent regulatory requirements, and the maintenance of user trust. Additionally, it enables safe data sharing and collaborative analysis while preserving the utility of the data. By incorporating advanced cryptographic and privacy techniques, secure and private query processing can defend against sophisticated cyber threats. This tutorial highlights the importance of integrating robust security and privacy measures into query processing to build trustworthy database systems. It reviews current systems and protocols that achieve these goals and discusses future directions for easy-to-use query processing under secure and private protection.

Structural Graph Clustering is a well-known problem that aims to identify clusters and distinguish between special roles, such as hub and outlier. However, SCAN, the fundamental structural clustering model, is designed for pairwise graphs and fails to capture the unique structural information inherent in hypergraphs when clustering hypergraphs. Motivated by this, we propose a new structural clustering model, HSCAN, specifically for hypergraphs. We further design an Order-Index to accelerate fetching the key information of the HSCAN and a Lightweight Similarity Bucket Index to reduce the index cost. Next, we present an index-based sequential query algorithm with high performance and a parallel query algorithm to process large hypergraphs faster. Additionally, we provide the algorithms for constructing Order-Index and Lightweight Similarity Bucket Index. Extensive experiments on both real-world and synthetic datasets show that HSCAN performs better than existing models, and the two index-based query algorithms are up to three orders of magnitude faster than the existing algorithm.

Distributed database systems are extensively utilized to provide cloud services for online map platforms, offering consistency, disaster recovery, and high performance, whereas traditional systems relying on singly-homed architecture face challenges in scaling for large-scale services. In this paper, we propose the architectural design of OceanBase (OB), a distributed database system that “unitizes” services and operations into individual machines. The unitization approach migrates from a singly-homed to a multi-homed design across multiple regions. By leveraging this feature, OceanBase ensures data replication and seamless service handover when a machine goes offline. However, communication overhead between regions can sometimes be burdensome. To address this issue, OceanBase unitizes read and write operations, and employs a hybrid centralization and unitization approach that is dynamically optimized for Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP). To validate our design, we deploy OceanBase on AMap, an online map application platform supporting large-scale distributed services. Through a series of experiments, we demonstrate that OceanBase exhibits enhanced disaster tolerance capabilities and achieves improved performance for both write-intensive and read-intensive benchmarks.

Due to the rising concerns on privacy protection, how to build machine learning models from distributed databases with privacy guarantees has gained more popularity. Vertical federated learning (VFL) trains machine learning models in a privacy-preserving way when the data features are scattered over distributed databases. We study the participant selection problem (PSP) for VFL, which chooses a given number of participants to conduct training while maximizing model accuracy. Compared to training with all participants, PSP can filter out hitch-riders that contribute marginally to model quality and reduce training time by involving fewer participants. To achieve good model accuracy, we formulate PSP as choosing a set of participants that maximizes the likelihood of the data samples. Then, utilizing the k-nearest neighbors (KNN) classifier as the proxy model, we express the likelihood as a function of the selected participants and prove that the function is sub modular. The submodular property is favorable as it can account for the feature diversity among the participants and allows to greedily select the participant with the maximum gain in each step. However, the selection process requires finding the top-k neighbors of a data sample as the basic operation, which is expensive in VFL setting as it involves encrypted communication. As such, we adapt the Fagin's algorithm, a famous top-k query algorithm, to reduce the amount of encrypted communication. We deploy our solution VFPS-SM across five distributed nodes and conduct experiments with 10 datasets and 3 models to evaluate its performance. The results show that VFPS-SM can reduce the end-to-end running time by up to$35\times$, selection time$365\times$and improve model accuracy by 6.0% compared with state-of-the-art baselines.

In federated learning (FL), the assumption that datasets from different devices are independent and identically distributed (i.i.d.) often does not hold due to user differences, and the presence of various data modalities across clients makes using a single model impractical. Personalizing certain parts of the model can effectively address these issues by allowing those parts to differ across clients, while the remaining parts serve as a shared model. However, we found that partial model personalization may exacerbate client drift (each client's local model diverges from the shared model), thereby reducing the effectiveness and efficiency of FL algorithms. We propose an FL framework based on the alternating direction method of multipliers (ADMM), referred to as FedAPM, to mitigate client drift. We construct the augmented Lagrangian function by incorporating first-order and second-order proximal terms into the objective, with the second-order term providing fixed correction and the first-order term offering compensatory correction between the local and shared models. Our analysis demonstrates that FedAPM, by using explicit estimates of the Lagrange multiplier, is more stable and efficient in terms of convergence compared to other FL frameworks. We establish the global convergence of FedAPM training from arbitrary initial points to a stationary point, achieving three types of rates: constant, linear, and sublinear, under mild assumptions. We conduct experiments using four heterogeneous and multimodal datasets with different metrics to validate the performance of FedAPM. Specifically, FedAPM achieves faster and more accurate convergence, outperforming the SOTA methods with average improvements of 12.3% in test accuracy, 16.4% in F1 score, and 18.0% in AUC while requiring fewer communication rounds.

Previous learning-based parameter query optimization (PQO) methods excel in static workloads by precisely selecting optimal plans in a cache with a fixed set of representative plans. However, these methods struggle in dynamic workloads because they cannot predict over dynamically changed plan caches. These queries that fall outside the previously observed query parameter distribution have the risk of reusing suboptimal plans. Unlike traditional PQO methods that learn mappings from parametric query parameters to a fixed set of execution plans, our adaptive PQO framework ( APQO ) takes both query parameters and the plans themselves as model inputs, thereby handling variable numbers of plans in dynamic workloads. By embedding plan representations through representation learning, we pre-train a foundation model offline, enabling APQO to acquire a generalizable plan performance prediction model. Leveraging the foundation model's predictive capability along with a hybrid data augmentation strategy, we train an online calibration model with minimal training data for distribution-shifted new queries, rapidly adapting knowledge for reusing new plans. APQO is natively designed to handle the characteristics of dynamic workloads. Experimental results show that APQO outperforms existing PQO methods in dynamic workloads, achieving a higher cache hit ratio and significantly reducing query latency.

Differential privacy (DP) is a leading standard for protecting individual privacy in data collection and analysis. This paper explores the shuffle model of DP, which balances privacy and utility by allowing users to send messages to a trusted shuffler before reaching an untrusted analyzer anonymously. We focus on efficiently implementing the matrix mechanism in shuffle-DP, where efficiency is defined by the number of messages each user sends. Our contributions include a baseline shuffle-DP mechanism that naively adapts the matrix mechanism, followed by an improved mechanism that reduces message complexity while maintaining error levels comparable to central-DP. We demonstrate the versatility of our approach across common query workloads, such as range queries and data cubes, achieving significant improvements in message efficiency. Experimental results confirm that our method outperforms the baseline solution while closely matching the accuracy of central-DP mechanisms.

Accurately estimating the number of unique elements that appear in data streams in real time is a fundamental problem with applications including network traffic monitoring and real-time social media analytics. Traditional sketch-based algorithms such as FM Sketch and HyperLogLog offer memory-friendly solutions for cardinality estimation but fall short in scenarios where the stream elements are privacy-sensitive and require differential privacy. Although recent approaches have incorporated differential privacy into the above cardinality estimators, they are limited to single-query settings, restricting their applicability. Previous methods for private cardinality continual release settings-i.e., releasing the cardinality after each new element in the stream-demand large memory resources and are thus difficult to apply in practice. In this paper, we present a novel cardinality estimation framework, FC, which ensures differential privacy under continual releases while simultaneously achieving low memory usage, high accuracy, and efficient computation. Our approach innovatively leverages an efficient cardinality estimator and privacy-preserving mechanisms to overcome the limitations of existing methods. Comprehensive experiments demonstrate that our method reduces memory usage by up to 504 times compared to the best previous method while maintaining nearly the same accuracy. Additionally, under identical memory constraints, our method improves the estimation accuracy by orders of magnitude.

Prediction queries that apply machine learning (ML) models to perform analysis on data stored in the database are prevalent with the advance of research. Current database systems introduce Python UDFs to express prediction queries and call ML frameworks for inference. However, the impedance mismatch between database engines and prediction query execution imposes a challenge for query performance. First, the database engine is oblivious to the internal semantics of prediction functions and evaluates the UDF holistically, which incurs the repetitive inference context setup. Second, the invocation of prediction functions in the database does not consider that batching inference with a desirable inference batch size achieves a high performance in ML frameworks. To mitigate the mismatch, we propose to employ a prediction-aware operator in database engines, which leverages inference context reuse cache to achieve an automatic one-off inference context setup and batch-aware function invocation to ensure desirable batching inference. We implement a prototype system, called IMBridge, based on an open-source database OceanBase. Our experiments show that IMBridge achieves a 71.4x speedup on average over OceanBase for prediction query execution and significantly outperforms other solutions.

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features while contrastive learning (CL) maximizes the similarity between augmented views of the same graph. Existing works utilize MAE and CL separately but we observe that the MAE and CL paradigms are complementary and propose the graph contrastive masked autoencoder (GCMAE) framework to unify them. Specifically, by focusing on local edges or node features, MAE cannot capture global information of the graph and is sensitive to particular edges and features. On the contrary, CL excels in extracting global information because it considers the relation between graphs. As such, we equip GCMAE with an MAE branch and a CL branch, and the two branches share a common encoder, which allows the MAE branch to exploit the global information extracted by the CL branch. To force GCMAE to capture global graph structures, we train it to reconstruct the entire adjacency matrix instead of only the masked edges as in existing works. Moreover, a discrimination loss is proposed for feature reconstruction, which improves the disparity between node embeddings rather than reducing the reconstruction error to tackle the feature smoothing problem of MAE. We evaluate GCMAE on four popular graph tasks (i.e., node classification, node clustering, link prediction, and graph classification) and compare it with 14 state-of-the-art baselines. The results show that GCMAE consistently provides good accuracy across these tasks, and the maximum accuracy improvement is up to 3.2% compared with the best-performing baseline.

Functionalities of a database system are co-designed and jointly maintain the database performance. Each function-ality usually has its own metrics to evaluate its state. Previous knobs tuning methods regard the database system as a black box and aim to automatically find the optimal configurations by collecting and observing the overall performance data (e.g., transaction throughput per second) under various configuration knobs. However, if a functionality is not running in the tuning phase, its knobs irrelevant to performance changes can also be tuned by existing tools and potential risks would be introduced. To resolve this problem, we design a database knob tuning framework to support functionality-aware knobs tuning. It uses multitask learning to take the database overall performance as the objective of main learning task, and each function module as a separate learning task. This framework enhances the tuning results through learning the relationships between different tasks, and avoids adjusting irrelevant knobs by perceiving the status of functionalities. We validate its generalizability on OceanBase and PostgreSQL. Experimental results show that better performances were achieved on the overall performance and the metrics of various functionalities.

Oblivious map (OMAP) is an important component in encrypted databases, utilized to prevent the server inferring sensitive information about client's encrypted databases based on access patterns. Despite its widespread usage and importance, existing OMAP solutions face practical challenges, including the need for a large number of interaction rounds between the client and server, as well as substantial communication bandwidth. For example, the SOTA protocol OMIX++ in VLDB 2024 still requires O (log n ) interaction rounds and O (log 2 n ) communication bandwidth per access, where n denotes the total number of key-value pairs stored. In this work, we introduce more practical and efficient OMAP constructions. Consistent with all prior OMAPs, our constructions also adapt only the tree-based Oblivious RAM (ORAM) and oblivious data structures (ODS) to achieve OMAP for enhanced practicality. In complexity, our approach needs O (log n /log log n )+ O (log λ ) interaction rounds and O (log 2 n /log log n ) + O (log λ log n ) communication bandwidth per data access where λ is the security parameter. This new complexity results from our two main contributions. First, unlike prior works relying solely on search trees , we design a novel framework for OMAP that combines hash table with search trees. Second, we propose a more efficient tree-based ORAM named DAORAM, which is of significant independent interest. This new ORAM accelerates our constructions as it supports obliviously accessing hash tables more efficiently. We implement both our proposed constructions and prior methods to experimentally demonstrate that our constructions substantially outperform prior methods in terms of efficiency.

Distributed databases have been widely researched and developed in recent years due to their scalability, availability, and consistency guarantees. The write-ahead logging (WAL) system is one of the most vital components in a database. It is still a non-trivial problem to design a replicated logging system as the foundation of a distributed database with the power of ACID transactions. This paper proposes PALF, a Paxos-backed Append-only Log File System, to address these challenges. The basic idea behind PALF is to co-design the logging system with the entire database for supporting database-specific functions and to abstract the functions as PALF primitives to power other distributed systems. Many database functions, including transaction processing, database restore, and physical standby databases, have been built based on PALF primitives. Evaluation shows that PALF greatly outperforms well-known implementations of consensus protocols and is fully competent for distributed database workloads. PALF has been deployed as a component of the OceanBase 4.0 database and has been made open-source along with it.

Parametric Query Optimization (PQO) is crucial for efficiently handling parametrized queries (PQ) in many database applications. This paper addresses two key challenges in existing PQO techniques, focusing on plan set generation and best plan selection. Regarding plan set generation, existing methods rely on modifying sub-plan cardinalities, often resulting in inefficiency and sub-optimal performance due to unclear extents of modifications needed. To overcome this issue, we propose a hybrid plan enumeration algorithm that adeptly adjusts both cardinality and join order. Regarding best plan selection, recent methods rely on machine learning models to choose plans with minimum predicted latency, but they struggle with accurate predictions when parameter bindings vary. Even minor variations in parameters can significantly impact cardinality, affecting plan optimality. To overcome this issue, we propose to utilize a learning-to-rank model, which uses relative rankings as a more reliable performance indicator. Our approach, integrated into PostgreSQL, undergoes extensive experiments on real datasets, showcasing significant improvements in both efficiency and accuracy, as compared to baselines. Specifically, it accelerates the PostgreSQL optimizer by up to 2.57× and surpasses the best existing baseline by up to 1.36×.

Native distributed databases, crucial for scalable applications, offer transactional and analytical prowess but face data intricacies and network challenges. Under the CAP theorem's constraints, latency and replication issues necessitate creative approaches to maintenance, security, and upgrades. Progress in consistency algorithms, network technology, automation, and machine learning for optimization presents significant potential. Embracing hybrid transactional/analytical processing (HTAP), these databases represent an evolutionary leap in data management, aiming to reconcile performance with the complexities inherent in distributed environments. OceanBase is introduced as a case study, and its strong TPC-C and TPC-H benchmark performances underscore Ocean-Base as a top-tier distributed database. We also discuss possible opportunities for native distributed databases.

This paper proposes a federated, fair, and fast k-means algorithm (F3KM) to solve the fair clustering problem efficiently in scenarios where data cannot be shared among different parties. The proposed algorithm decomposes the fair k-means problem into multiple subproblems and assigns each subproblem to a client for local computation. Our algorithm allows each client to possess multiple sensitive attributes (or have no sensitive attributes). We propose an in-processing method that employs the alternating direction method of multipliers (ADMM) to solve each subproblem. During the procedure of solving subproblems, only the computation results are exchanged between the server and the clients, without exchanging the raw data. Our theoretical analysis shows that F3KM is efficient in terms of both communication and computation complexities. Specifically, it achieves a better trade-off between utility and communication complexity, and reduces the computation complexity to linear with respect to the dataset size. Our experiments show that F3KM achieves a better trade-off between utility and fairness than other methods. Moreover, F3KM is able to cluster five million points in one hour, highlighting its impressive efficiency.

In the ongoing evolution of the OceanBase database system, it is essential to enhance its adaptability to small-scale enterprises. The OceanBase database system has demonstrated its stability and effectiveness within the Ant Group and other commercial organizations, besides through the TPC-C and TPC-H tests. In this paper, we have designed a stand-alone and distributed integrated architecture named Paetica to address the overhead caused by the distributed components in the stand-alone mode, with respect to the OceanBase system. Paetica enables adaptive configuration of the database that allows OceanBase to support both serial and parallel executions in stand-alone and distributed scenarios, thus providing efficiency and economy. This design has been implemented in version 4.0 of the OceanBase system, and the experiments show that Paetica exhibits notable scalability and outperforms alternative stand-alone or distributed databases. Furthermore, it enables the transition of OceanBase from primarily serving large enterprises to truly catering to small and medium enterprises, by employing a single OceanBase database for the successive stages of enterprise or business development, without the requirement for migration. Our experiments confirm that Paetica has achieved linear scalability with the increasing CPU core number within the stand-alone mode. It also outperforms MySQL and Greenplum in the Sysbench and TPC-H evaluations.

Countrywide and worldwide business, like gaming and social networks, drives the popularity of inter-data-center transactions. To support inter-data-center transaction processing and data center fault tolerance simultaneously, existing protocols suffer from significant performance degradation due to high-latency and unstable networks. In this paper, we propose RedT, a novel distributed transaction processing protocol that works in heterogeneous networks. In detail, nodes within a data center are inter-connected via the RDMA-capable network and nodes across data centers are inter-connected via TCP/IP networks. RedT extends two-phase commit (2PC) by decomposing transactions into sub-transactions in terms of the data center granularity, and proposing a pre-write-log mechanism that is able to reduce the number of inter-data-center round-trips from a maximal of 6 to 2. Extensive evaluation against state-of-the-art protocols shows that RedT can achieve up to 1.57× higher throughputs and 0.56× lower latency.

We have designed and developed OceanBase, a distributed relational database system from the very basics for a decade. Being a scale-out multi-tenant system, OceanBase is cross-region fault tolerant, which is based on the shared-nothing architecture. Besides sharing many similar goals with alternative distributed DBMS, such as horizontal scalability, fault-tolerance, etc., our design has been driven by the demands of typical RDBMS compatibility as well as both on-premise and off-premise deployments. OceanBase has fulfilled its design goal. It implements the salient features of certain mainstream classical RDBMS, and most applications on them can run on OceanBase, with or without a few minor modifications. Tens of thousands of OceanBase servers have been deployed in Alipay.com as well as many other commercial organizations. It has also successfully passed the TPC-C benchmark test and seized the first place with more than 707 million tpmC. This paper presents the goals, design criteria, infrastructure, and key components of OceanBase including its engines for storage and transaction processing. Further, it details how OceanBase achieves the above leading TPC-C benchmark in a distributed cluster with more than 1,500 servers from 3 zones. It also describes lessons what we have learnt in building OceanBase for more than a decade.