While compute-storage disaggregation provides elasticity to cloud databases, the prevalent single-primary model restricts write scalability and fault tolerance. Maintaining data consistency becomes a key challenge when multiple primaries handle concurrent writes, especially with hot data cached locally. This paper presents MTC (Multi-Primary Transaction Commit), a scalable and non-intrusive commit mechanism for multiprimary databases. The core idea of MTC is to decouple global transaction ordering from log synchronization. By enabling primary nodes to asynchronously prefetch Global Transaction Sequence Numbers (GTSNs) and assign them to locally completed transactions, MTC implements a highly efficient “check-thenreplicate” workflow. Following a pipelined, row-level conflict detection, validated transactions commit immediately with their logs broadcast asynchronously. Conflicting transactions, however, are rolled back locally with minimal overhead, fundamentally avoiding the network cost of broadcasting logs for transactions destined to abort. Our experimental results demonstrate that MTC significantly improves performance and scalability due to lower network overhead and more efficient conflict detection, even under high contention deployments.

Keyword search, which identifies transactions associated with specified keywords across historical blocks, is a critical query type in blockchain analytics. However, existing approaches, such as on-chain indexing and off-chain synchronization, may lead to significant space overhead or challenges in maintaining data freshness. To address these challenges, we propose BlockSketch, a novel probabilistic data structure (PDS) that adopts a differentiated encoding strategy, aimed at resolving the trade-off between query performance and storage overhead in blockchain indexing. BlockSketch features a hierarchical filtering architecture that combines Bloom filters and Sketches within a binary tree framework, enabling dynamic structural maintenance. Keywords are categorized as "hot" or "cold" based on their on-chain frequency and encoded into the most suitable component to achieve resource-efficient storage and accurate querying. In addition, BlockSketch integrates two distinct query rules, namely "level-down" and "jump," to balance query accuracy and efficiency when processing keywords with varying frequencies. Furthermore, we enhance the query efficiency of BlockSketch by merging inefficient lower-level nodes into more compact ones and pruning redundant node checks during query execution. Extensive experiments on a real-world dataset demonstrate that BlockSketch delivers up to 73% faster query processing, achieves 44.56% of the average false positive rate of baselines at low multiplicity and as low as 1.52% at high multiplicity, and saves 79% in storage compared to state-of-the-art methods.

The query optimizer is a fundamental component of database management systems that determines the most efficient execution strategy for a given query by evaluating alternative query plans. Among its tasks, join optimization plays a central role, as the order of joins in multi-table queries can significantly affect execution performance. However, due to the inherent complexity of join optimization, logical bugs are inevitable and often difficult to detect. While existing fuzzing tools have shown notable success in uncovering crash- and performance-related errors, effectively identifying logical bugs – cases in which the system produces incorrect query results – remains largely unresolved. In this paper, we propose a metamorphic testing approach to detect DBMS bugs related to INNER JOIN optimization through the lens of set theory. For each testing case, equivalent queries are generated based on a basic set operation – intersection – and three semantics-preserving transformation rules, i.e., symmetric join transformation, asymmetric difference transformation, and symmetric difference transformation, are introduced. These rules rewrite a simple NATURAL/INNER JOIN query into a more complex, yet semantically equivalent, form. We implement this design in JoinEquiv, which serves as a testing oracle to systematically uncover logical inconsistencies in DBMS query processing by comparing the results of original and transformed queries. Using JoinEquiv, we uncovered 29 previously unknown issues in mainstream DBMSs (MySQL, TiDB, DuckDB, and Percona), and 27 of them were officially confirmed. JoinEquiv reveals deep logical flaws in DBMS optimizers and executors, underscoring its value in enhancing DBMS robustness.

This paper investigates the feasibility of achieving zero-knowledge verifiability for graph databases, enabling database owners to cryptographically prove the query execution correctness without disclosing the underlying data. Although similar capabilities have been explored for relational databases, their implementation for graph databases presents unique challenges. This is mainly attributed to the relatively large complexity of queries in graph databases. When translating graph queries into arithmetic circuits, the circuit scale can be too large to be practically evaluated. To address this issue, we propose to break down graph queries into more fine-grained, primitive operators, enabling a step-by-step evaluation through smaller-scale circuits. Accordingly, the verification with ZKP circuits of complex graph queries can be decomposed into a series of composable cryptographic primitives, each designed to verify a fundamental structural property such as path ordering or edge directionality. Especially, having noticed that the graph expansion (i.e., traversing from nodes to their neighbors along edges) operation serves as the backbone of graph query evaluation, we design the expansion centric operator decomposition. In addition to constructing circuits for the expansion primitives, we also design specialized ZKP circuits for the various attributes that augment this traversal. The circuits are meticulously designed to take advantage of PLONKish arithmetization. By integrating these optimized circuits, we implement ZKGraph, a system that provides verifiable query processing while preserving data privacy. Performance evaluation indicates that ZKGraph significantly outperforms naive in circuit implementations of graph operators, achieving substantial improvements in both runtime and memory consumption.

Educational recommendation systems have been a fundamental component for alleviating learning disorientation in self-paced learning. While existing studies mainly leverage cognitive theories to guide learning motivation modeling, they critically overlook the role of social influences. Through empirical analysis, we identify social homophily as an additional driver of learning behaviors, i.e., learners tend to adopt resources validated by their social cohort. However, two challenges impede effective social homophily modeling: (1) the absence and sparsity of predefined social relations in online education, and (2) the deep entanglement of social homophily with cognitive homophily in behavioral data. To tackle these challenges, we propose a graph-based framework EdGCL that explicitly disentangles social homophily and cognitive homophily. EdGCL infers implicit social relations from learners' social behaviors and encodes them via a graph transformer, generating social-view representations. Simultaneously, it constructs a heterogeneous learning graph to model cognitive homophily, which is enhanced by a type-aware aggregator and cognitive diagnosis loss. To ensure the semantic distinctiveness of dual-view homophily modeling, a cross-view contrastive disentanglement mechanism is designed to pull intra-view representations closer while pushing inter-view representations away. Evaluation on two real-world educational datasets demonstrates the superior recommendation performance of EdGCL, highlighting the necessity of dual homophily modeling for understanding the motivations behind learning behaviors.

Graph queries are ubiquitous in graph databases and knowledge graphs, supporting applications such as recommender systems, question answering, and semantic search. In recent years, worst-case optimal join (WCOJ) algorithms play a critical role in evaluating graph queries. However, their practical performance is hindered by load imbalance in parallelization and poor execution order of join variables. In this paper, we propose APEX, Adaptive variable-wise Parallel EXecution for WCOJ on graph queries. First, APEX parallelizes the core intersection operations in Leapfrog Triejoin by materializing and organizing all intersection results for a variable at once. This is enabled by a candidate propagation graph, which captures dependencies among variables and their intersection results. Second, we further propose a reinforcement learning policy with a graph convolutional network encoder that selects the next variable based on query structure and runtime feedback. To accelerate lookups on the candidate propagation graph, we introduce two techniques: (i) dependency-source detection to prune the traversal space, and (ii) a Bloom filter to avoid redundant checks. Extensive experiments on diverse real-world and synthetic datasets demonstrate that APEX achieves up to $\mathbf{2. 4 2 - 5. 7 9} \boldsymbol{\times}$ speedup over state-of-the-art baselines.

Open source communities have a wealth of digital talents, who are urgently needed by various industries under the digitalization process of the entire society. However, barriers exist between digital talents in open source communities and employers. On one hand, open source contributors wonder whether their expertise matches the requirements of specific jobs; On the other hand, developers working on small open source projects are less likely to get recognition from employers, compared with those contributing to well-known projects. To bridge this gap, we propose a new task, matching digital talents and job titles in open source communities, which measures the matching degrees between digital talents with open source experience and job titles requiring digital skills. To solve the task, we construct a heterogeneous information network connecting open source communities and job markets, and propose a semi-supervised network alignment model to augment the connectivity of the network. Then we employ a graph neural network to learn the representations of the digital talents and the job titles from the augmented network, based on which we measure the matching degrees between them. Experimental results demonstrate that our method achieves improvements of at least 5.34, 3.52, 2.37, 2.93, and 8.21 in accuracy, precision, recall, F1, and AUC compared to other possible solutions.

Educational recommendations empower personalized learning by suggesting suitable learning resources to learners, and the graph-based recommenders are widely adopted. Existing methods are mainly ID-based, which initialize learners and resources with trainable identifiers and optimize their representations solely from the interaction graph. As a result, the lack of semantic understanding of learning resources and learning paths hinders further improvements in recommendation accuracy. To alleviate the problem, we propose KLU4EduRec, which leverages large language models (LLMs) to understand resource knowledge and learning paths, thereby enhancing traditional graph-based educational recommender systems. Specifically, for learning path understanding, we segment a learning path by detecting learning pattern drift in resource knowledge sequence, and prompt LLMs to infer learners' learning patterns within each segment. The segment-level patterns are then chronologically aggregated to represent the overall learning path. Besides, we prompt LLMs to summarize the core knowledge of learning resources from their content as complementary semantic signals. Finally, the resulting semantic representations are aligned and fused with structural representations learned by a graph-based recommender to enable more accurate recommendations. We conduct extensive experiments to show that KLU4EduRec greatly outperforms existing methods, including traditional ID-based methods and recent LLM-powered methods. A case study shows how the understanding of pattern drift in a learning path leads to more suitable recommendations. A reproducibility package is available at https://anonymous.4open.science/r/KLU4EduRec-C08C.

Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves applications in the domains of healthcare, commerce, education, and so on. Particularly, NLP has been widely applied to the education domain and its applications have enormous potential to help teaching and learning. In this survey, we review recent advances in NLP with a focus on solving problems relevant to the education domain. In detail, we begin with introducing the related background and the real-world scenarios in education to which NLP techniques could contribute. Then, we present a taxonomy of NLP in the education domain and highlight typical NLP applications including question answering, question construction, automated assessment, and error correction. Next, we illustrate the task definition, challenges, and corresponding cutting-edge techniques based on the above taxonomy. In particular, LLM-involved methods are included for discussion due to the wide usage of LLMs in diverse NLP applications. After that, we showcase some off-the-shelf demonstrations in this domain, which are designed for educators or researchers. At last, we conclude with five promising directions for future research, including generalization over subjects and languages, deployed LLM-based systems for education, adaptive learning for teaching and learning, interpretability for education, and ethical consideration of NLP techniques. We organize all relevant datasets and papers in the open-available Github Link for better review https://github.com/LiXinyuan1015/NLP-for-Education.

Time series anomaly detection is important in modern large-scale systems and is applied in a variety of domains to analyze and monitor the operation of diverse systems. Unsupervised approaches have received widespread interest, as they do not require anomaly labels during training, thus avoiding potentially high costs and having wider applications. Among these, autoencoders have received extensive attention. They use reconstruction errors from compressed representations to define anomaly scores. However, representations learned by autoencoders are sensitive to anomalies in training time series, causing reduced accuracy. We propose a novel encode-then-decompose paradigm, where we decompose the encoded representation into stable and auxiliary representations, thereby enhancing the robustness when training with contaminated time series. In addition, we propose a novel mutual information based metric to replace the reconstruction errors for identifying anomalies. Our proposal demonstrates competitive or state-of-the-art performance on eight commonly used multi- and univariate time series benchmarks and exhibits robustness to time series with different contamination ratios.

Truck-cargo matching is one of the core tasks of online freight platforms, where the primary objective is to optimally assign each cargo task to the most suitable truck. The existing matching strategies seek to maximize total transported weight of cargoes by increasing the number of truck-cargo pairings. However, these strategies fail to ensure global matching pair maximization across all regions due to the heterogeneous spatial distribution of truck supply and cargo transporting demand. This limitation necessitates the incorporation of regional supply–demand gap prediction into the matching process. Two critical challenges emerge in achieving optimal matching: (1) the prediction accuracy of supply–demand gaps is influenced by multiple complex factors, and (2) the multi-objective optimization conflicts within current matching decisions may adversely impact subsequent matching performance. To address these challenges, we propose a hierarchical reinforcement learning framework for multi-objective truck-cargo matching, comprising two key components: a supply–demand gap prediction module and a multi-objective optimization matching module.For accurate supply–demand gap prediction, we develop a hypergraph attention network model incorporating an adaptive confidence interval optimization mechanism to capture complex relationships among various predictive factors. Furthermore, to mitigate negative effects of multi-objective conflicts on long-term matching performance, we design a hierarchical deep Q network model that dynamically adjusts objective weights based on predicted long-term benefits. Extensive experiments conducted on two real-world logistics datasets demonstrate that our proposed method achieves a 10.7% higher competitive ratio compared to state-of-the-art approaches, validating the effectiveness of our supply–demand balance guided matching strategy in practical operational scenarios.

Accurate land deformation prediction using InSAR (Interferometric Synthetic Aperture Radar) technology is crucial for early warning of geological disasters. However, existing prediction methods face two major challenges: cross-area association bottleneck and inadequate handling of temporal distribution heterogeneity. To address these challenges, we propose Multi-modal Adaptive Association Learning framework (MAAL). For the spatial knowledge transfer challenge, we introduce a cross-area multi-modal association learning module that integrates multi-modal (InSAR and geological text) data to enable knowledge transfer between areas with similar geological characteristics. For temporal distribution heterogeneity, we develop an adaptive evolution stage recognition module that uses distribution routers to identify different temporal patterns, then applies corresponding linear extractors to model the heterogeneous landslide evolution. Experimental validation on 889 hazardous areas demonstrates that MAAL outperforms baselines.

Smart contracts have expanded blockchain applications, but permissioned blockchain systems face severe through-put challenges, especially with the increasing complexity of nested contract transactions. These transactions, involving cross-contract interactions and deep call chains, intensify execution conflicts and rollback overhead, ultimately limiting parallelism. We propose Loom, a deterministic execution framework that enhances the efficiency of nested contract transactions. Loom employs snapshot-based concurrent pre-execution to decompose transactions into fine-grained subtransactions. To reduce rollback overhead, it introduces a two-phase rollback algorithm to minimize computational redundancy and fine-grained rescheduling to improve subtransaction-level parallelism during re-execution. Additionally, a multi-phase parallelism mechanism optimizes resource utilization across transaction blocks. Experimental results show that Loom achieves 6.1 × to $10.2\times$ higher throughput while reducing rollback overhead by 89.9% to 98.4%, significantly outperforming state-of-the-art solutions.

Update-intensive workloads are prevalent in contemporary OLTP and AI/ML scenarios. An update operation typically involves deleting the old version of the target record and then inserting a new version. In this work, we demonstrate that an LSM-tree faces two issues when dealing with update-intensive workloads. Firstly, the deleted old versions are not promptly garbage collected until they merge with their new versions during compaction. This may lead to space waste and write amplification. Secondly, it is common for an update operation to modify only a small fraction of a data record, such as one of a hundred attributes. However, state-of-the-art LSM-trees fail to effectively utilize the incremental storage strategy, which involves storing only the updated fraction rather than the entire new version to enhance efficiency. In this paper, we propose two techniques, active and fast garbage collection, and adaptive incremental updating, to address these issues, respectively. Active and fast garbage collection probes the distribution of invalid data versions in an LSM-tree and performs garbage collection in a more promptly manner. Adaptive incremental updating applies different storage modes to the update operation to achieve balanced write and read amplification ratios as much as possible. Based on the techniques, we introduce SylphDB implemented based on the codebase of RocksDB and optimized for update-intensive workloads. Experimental results demonstrated that, compared to traditional LSM-tree based systems, SylphDB can improve the efficiency of garbage collection by 2× and reduce write amplification by 20%.

Multi-layer Perceptron (MLP)-based models have been widely exploited by modern recommendation applications. In practice, industrial recommendation scenarios frequently launch continuous incremental training jobs with only one epoch to capture real-time user features. This kind of job is shorter than full training and has a larger proportion of feature processing time. To fully utilize fragmentation resources, our model engineering team at Tencent explores resource-constrained CPU clusters to perform such incremental training workloads. To improve the efficiency of such workloads, we notice scheduling optimizations by overlapping feature processing and model training at the level of data processing pipelines. In particular, we propose an intra-pipeline scheduling strategy, which prefetches feature processing operators dynamically to fill the idle time of CPUs during the communication of embedding lookup. Furthermore, we propose an inter-pipeline scheduling strategy, which balances the resource demands of different pipelines. It prioritizes the execution of critical pipelines and overlaps the communication in critical pipelines with the execution of non-critical pipelines. Based on the two scheduling strategies, we implement a novel incremental recommendation training framework called RECS on top of TensorFlow. In our experimental studies, RECS achieves a speedup of 1.36x over existing solutions on industrial workloads.

Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time series data will be available, leading also to growing demands for TSAD. While many TSAD methods already exist, new and better methods are still desirable. However, effective progress hinges on the availability of reliable means of evaluating new methods and comparing them with existing methods. We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods.

Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets collected in the preexisting time series forecasting benchmark (TFB). This is achieved by leveraging TFB's flexible and consistent evaluation pipeline. Second, when practitioners must perform forecasting on a new dataset, a nontrivial first step is often to find an appropriate forecasting method. EasyTime provides an Automated Ensemble module that combines the promising forecasting methods to yield superior forecasting accuracy compared to individual methods. Third, EasyTime offers a natural language Q A module leveraging large language models. Given a question like "Which method is best for long term forecasting on time series with strong seasonality?", EasyTime converts the question into SQL queries on the database of results obtained by TFB and then returns an answer in natural language and charts. By demonstrating EasyTime, we intend to show how it is possible to simplify the use of time series forecasting and to offer better support for the development of new generations of time series forecasting methods.

Knowledge concept recommendation (KCR) has recently emerged as a crucial task for achieving personalized learning within online education systems. Existing studies basically construct heterogeneous information networks (HINs) from students' learning behaviors, and leverage graph representation learning to model their interests, i.e., preference status. However, current approaches overlook two critical aspects. First, students' learning motivation is inherently dynamic, yet existing methods typically operate on static HINs, failing to capture the temporal evolution of learning behaviors. Second, learning motivation stems not only from personal interests but also from knowledge deficits, which can be inferred from learners' knowledge status through their assessment behaviors. Consequently, accurately modeling students' learning motivation requires dynamically capturing both their preference status and knowledge status. To address these limitations, we propose DISKRec, a novel model that dynamically disentangles and integrates students' preference and knowledge status for knowledge concept recommendation. Specifically, we first construct a continuous-time heterogeneous information network (CTHIN) based on students' hybrid behaviors, naturally preserving the underlying temporal dynamics and complex behavioral patterns. Then, we develop a dual dynamic graph neural networks (Dual-DGNNs) module to disentangle preference status and knowledge status from the CTHIN. To dynamically integrate these status, we specially design both implicit dual-state integration and explicit status integration mechanisms. Additionally, we adopt a live-update strategy to improve the training efficiency of DISKRec, substantially reducing GPU memory consumption without sacrificing performance. Extensive experiments demonstrate that DISKRec significantly outperforms existing state-of-the-art KCR models.