Information Systems最新文献_第2页

MDU-Net: Multi-resolution learning and differential clustering fusion for multivariate electricity time series forecasting MDU-Net：多分辨率学习和多元电时间序列预测的差分聚类融合

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2026-01-19 DOI: 10.1016/j.is.2026.102693

Yongming Guan , Chengdong Zheng , Yuliang Shi , Gang Wang , Linfeng Wu , Zhiyong Chen , Hui Li

Artificial intelligence (AI) has demonstrated transformative potential in diverse fields such as healthcare, drug discovery, and natural language processing by enabling advanced pattern recognition and predictive modeling of complex data. Particularly in the power system, where it involves areas such as power load, electricity price, and renewable energy, the application of AI technology to enhance the multivariate electricity time series forecasting tasks is crucial for grid security and economic dispatch. In power systems, multivariate electricity time series forecasting tasks involving power load, electricity prices, and renewable energy are crucial for grid security and economic dispatch. Contemporary forecasting approaches primarily focus on two aspects: modeling multi-scale periodic characteristics within sequences and capturing complex collaborative dependencies among variables. However, existing techniques often fail to simultaneously disentangle multi-scale features and model the dynamically heterogeneous dependencies between variables. To overcome these limitations, this paper proposes MDU-Net, a novel forecasting framework. The framework comprises two core modules: Multi-resolution hierarchical Union learning (MRU) module and Differential Channel Clustering Fusion (DCCF) Module. The MRU module constructs multi-granularity temporal representations through downsampling and achieves effective cross-scale feature fusion by integrating channel-independent operations with seasonal-trend decomposition. The DCCF module adopts first- and second-order derivative approximations to generate soft clustering mask matrices, adaptively capturing asymmetric collaborative dependencies among different variables over time. Experimental results on multiple public datasets (ETT, Electricity) demonstrate that MDU-Net significantly outperforms state-of-the-art baselines in multivariate electricity time series prediction. it achieves 2.7% and 17.1% relative MSE reductions compared to TimeMixer and PatchTST, respectively, with 1.4% and 14.4% lower MAE. Notably, MDU-Net maintains strong generalization capabilities and computational efficiency. The framework also shows promising performance in cross-domain applications such as traffic forecasting.

人工智能（AI）通过支持高级模式识别和复杂数据的预测建模，在医疗保健、药物发现和自然语言处理等多个领域展示了变革潜力。特别是在涉及电力负荷、电价、可再生能源等领域的电力系统中，应用人工智能技术增强多元电力时间序列预测任务对电网安全和经济调度至关重要。在电力系统中，涉及电力负荷、电价和可再生能源的多元电力时间序列预测任务对电网安全和经济调度至关重要。当前的预测方法主要集中在两个方面：对序列内的多尺度周期特征进行建模和捕获变量之间复杂的协同依赖关系。然而，现有的技术往往不能同时解开多尺度特征和建模变量之间的动态异构依赖关系。为了克服这些限制，本文提出了一种新的预测框架MDU-Net。该框架包括两个核心模块：多分辨率分层联合学习（MRU）模块和差分信道聚类融合（DCCF）模块。MRU模块通过下采样构建多粒度时态表示，并将信道无关操作与季节趋势分解相结合，实现有效的跨尺度特征融合。DCCF模块采用一阶和二阶导数近似生成软聚类掩模矩阵，自适应捕获不同变量之间随时间的不对称协同依赖关系。在多个公共数据集（ETT, Electricity）上的实验结果表明，MDU-Net在多变量电力时间序列预测中显著优于最先进的基线。与TimeMixer和PatchTST相比，其相对MSE分别降低了2.7%和17.1%，MAE降低了1.4%和14.4%。值得注意的是，MDU-Net保持了强大的泛化能力和计算效率。该框架在流量预测等跨域应用中也显示出良好的性能。

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引用次数: 0

Example-driven semantic-similarity-aware query intent discovery: Empowering users to cross the SQL barrier through query by example 示例驱动的语义相似度感知查询意图发现：使用户能够通过示例查询跨越SQL障碍

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2026-01-12 DOI: 10.1016/j.is.2026.102687

Anna Fariha , Lucy Cousins , Narges Mahyar , Alexandra Meliou

Traditional relational data interfaces require precise structured queries over potentially complex schemas. These rigid data retrieval mechanisms pose hurdles for nonexpert users, who typically lack programming language expertise and are unfamiliar with the details of the schema. Existing tools assist in formulating queries through keyword search, query recommendation, and query auto-completion, but still require some technical expertise. An alternative method for accessing data is query by example (QBE), where users express their data exploration intent simply by providing examples of their intended data and the system infers the intended query. However, existing QBE approaches focus on the structural similarity of the examples and ignore the richer context present in the data. As a result, they typically produce queries that are too general, and fail to capture the user’s intent effectively. In this article, we present SQuID, a system that performs semantic-similarity-aware query intent discovery from user-provided example tuples.

Our work makes the following contributions: (1) We design SQuID: an end-to-end system that automatically formulates select-project-join queries with optional group-by aggregation and intersection operators – a much larger class than what prior QBE techniques support – from user-provided examples, in an open-world setting. (2) We express the problem of query intent discovery using a probabilistic abduction model that infers a query as the most likely explanation of the provided examples. (3) We introduce the notion of an abduction-ready database, which precomputes semantic properties and related statistics, allowing SQuID to achieve real-time performance. (4) We present an extensive empirical evaluation on three real-world datasets, including user intent case studies, demonstrating that SQuID is efficient and effective, and outperforms machine learning methods, as well as the state of the art in the related query reverse engineering problem. (5) We contrast SQuID with traditional SQL querying through a comparative user study, which demonstrates that users with varying expertise are significantly more effective and efficient with SQuID than SQL. We find that SQuID eliminates the barriers in studying the database schema, formalizing task semantics, and writing syntactically correct SQL queries, and, thus, substantially alleviates the need for technical expertise in data exploration.

传统的关系数据接口需要对可能复杂的模式进行精确的结构化查询。这些严格的数据检索机制给非专业用户带来了障碍，这些用户通常缺乏编程语言专业知识，并且不熟悉模式的细节。现有的工具通过关键字搜索、查询推荐和查询自动完成来帮助制定查询，但仍然需要一些技术专长。访问数据的另一种方法是按例查询（QBE），其中用户通过提供预期数据的示例来表达其数据探索意图，系统推断出预期的查询。然而，现有的QBE方法侧重于示例的结构相似性，而忽略了数据中存在的更丰富的上下文。因此，它们通常生成的查询过于笼统，无法有效地捕捉用户的意图。在本文中，我们介绍SQuID，这是一个从用户提供的示例元组中执行语义相似度感知查询意图发现的系统。我们的工作做出了以下贡献：(1)我们设计了SQuID：一个端到端系统，可以在开放世界环境中，从用户提供的示例中自动制定带有可选的group-by聚合和交集操作符的select-project-join查询——这是一个比之前的QBE技术支持的大得多的类。(2)我们使用概率溯因模型来表达查询意图发现问题，该模型将查询推断为所提供示例的最可能解释。(3)我们引入了可溯性数据库的概念，它可以预先计算语义属性和相关统计数据，从而使SQuID实现实时性能。(4)我们对三个真实世界的数据集进行了广泛的实证评估，包括用户意图案例研究，表明SQuID是高效和有效的，并且优于机器学习方法，以及相关查询逆向工程问题的最新技术。(5)我们通过用户对比研究将SQuID与传统的SQL查询进行了对比，结果表明，不同专业知识的用户使用SQuID的效率明显高于使用SQL。我们发现SQuID消除了在研究数据库模式、形式化任务语义和编写语法正确的SQL查询方面的障碍，因此，大大减轻了对数据探索技术专业知识的需求。

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引用次数: 0

Automated decision-making for dynamic task assignment at scale 大规模动态任务分配的自动决策

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2026-01-22 DOI: 10.1016/j.is.2026.102694

Riccardo Lo Bianco , Willem van Jaarsveld , Jeroen Middelhuis , Luca Begnardi , Remco Dijkman

The Dynamic Task Assignment Problem (DTAP) concerns matching resources to tasks in real time while minimizing some objectives, like resource costs or task cycle time. In this work, we consider a DTAP variant where every task is a case composed of a stochastic sequence of activities. The DTAP, in this case, involves the decision of which employee to assign to which activity to process requests as quickly as possible. In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising tool for tackling this DTAP variant, but most research is limited to solving small-scale, synthetic problems, neglecting the challenges posed by real-world use cases. To bridge this gap, this work proposes a DRL-based Decision Support System (DSS) for real-world scale DTAPs. To this end, we introduce a DRL agent with two novel elements: a graph structure for observations and actions that can effectively represent any DTAP and a reward function that is provably equivalent to the objective of minimizing the average cycle time of tasks. The combination of these two novelties allows the agent to learn effective and generalizable assignment policies for real-world scale DTAPs. The proposed DSS is evaluated on five DTAP instances whose parameters are extracted from real-world logs through process mining. The experimental evaluation shows how the proposed DRL agent matches or outperforms the best baseline in all DTAP instances and generalizes on different time horizons and across instances.

动态任务分配问题（DTAP）关注的是将资源与任务实时匹配，同时最小化某些目标，如资源成本或任务周期时间。在这项工作中，我们考虑一个DTAP变体，其中每个任务都是由随机活动序列组成的情况。在这种情况下，DTAP涉及到将哪个员工分配到哪个活动以尽可能快地处理请求的决策。近年来，深度强化学习（DRL）已经成为解决这种DTAP变体的有前途的工具，但大多数研究仅限于解决小规模的综合问题，忽视了现实世界用例带来的挑战。为了弥补这一差距，本工作提出了一个基于drl的决策支持系统（DSS），用于现实世界规模的dtap。为此，我们引入了一个具有两个新元素的DRL代理：一个用于观察和动作的图结构，可以有效地表示任何DTAP，以及一个可证明等同于最小化任务平均周期时间目标的奖励函数。这两种新特性的结合使智能体能够为现实世界规模的dtap学习有效且可推广的分配策略。通过过程挖掘从真实日志中提取参数的五个DTAP实例对所提出的DSS进行了评估。实验评估显示了所提出的DRL代理如何在所有DTAP实例中匹配或优于最佳基线，并在不同的时间范围和跨实例上进行泛化。

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引用次数: 0

Reflection on compliance monitoring in business processes: Functionalities, application, and tool-support 对业务流程中的遵从性监视的反思：功能、应用程序和工具支持

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-04 DOI: 10.1016/j.is.2025.102650

Linh Thao Ly , Fabrizio Maria Maggi , Marco Montali , Stefanie Rinderle-Ma , Wil M.P. van der Aalst

Together with Information Systems, we celebrate the journal’s 50th anniversary and the 10th anniversary of our joint work on a systematic framework for compliance monitoring functionalities.

我们与《信息系统》杂志一起庆祝该杂志创刊50周年，以及我们就合规监测功能的系统框架共同开展工作10周年。

引用次数: 0

VCR: Interpretable and interactive debugging of object detection models with visual concepts 具有可视化概念的对象检测模型的可解释和交互式调试

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-12 DOI: 10.1016/j.is.2025.102652

Jie Jeff Xu , Saahir Dhanani , Jorge Piazentin Ono , Wenbin He , Liu Ren , Kexin Rong

Computer vision models can make systematic errors, performing well on average but substantially worse on particular subsets (or slices) of data. In this work, we introduce Visual Concept Reviewer (VCR), a human-in-the-loop slice discovery framework that enables practitioners to interactively discover and understand systematic errors in object-detection models via novel use of visual concepts–semantically meaningful and frequently recurring image segments representing objects, parts, or abstract properties.

Leveraging recent advances in vision foundation models, VCR automatically generates segment-level visual concepts that serve as interpretable primitives for diagnosing issues in object-detection models, while also supporting lightweight human supervision when needed. VCR combines visual concepts with metadata in a tabular format and adapts frequent itemset mining techniques to identify common absences and presences of concepts associated with poor model performance at interactive speeds. VCR also keeps humans in the loop for interpretation and refinement at each step of the slice discovery process. We demonstrate VCR’s effectiveness and scalability through a new evaluation benchmark with 1713 slice discovery settings across three datasets. A user study with six expert industry machine learning scientists and engineers provides qualitative evidence of VCR’s utility in real-world workflows.

计算机视觉模型可能会产生系统错误，平均表现良好，但在特定的数据子集（或切片）上表现得更差。在这项工作中，我们介绍了视觉概念审查器（VCR），这是一个人在循环中的切片发现框架，使从业者能够通过新颖地使用视觉概念（表示对象、部件或抽象属性的语义上有意义且经常重复出现的图像片段）来交互式地发现和理解对象检测模型中的系统错误。利用视觉基础模型的最新进展，VCR自动生成分段级视觉概念，作为对象检测模型中诊断问题的可解释原语，同时在需要时还支持轻量级的人工监督。VCR将可视化概念与表格格式的元数据结合起来，并采用频繁的项集挖掘技术来识别与交互速度较差的模型性能相关的概念的常见缺失和存在。VCR还使人类在切片发现过程的每一步都能进行解释和改进。我们通过一个新的评估基准，在三个数据集上使用1713个切片发现设置，展示了VCR的有效性和可扩展性。六位行业机器学习专家和工程师的用户研究为VCR在实际工作流程中的实用性提供了定性证据。

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引用次数: 0

Exploring cultural commonsense in multilingual large language models: A survey 探索多语言大语言模型中的文化常识：综述

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-01 DOI: 10.1016/j.is.2025.102649

Geleta Negasa Binegde, Huaping Zhang

Large language models (LLMs) have demonstrated impressive proficiency in multilingual natural language processing (NLP), yet they frequently struggle with cultural commonsense—the implicit knowledge shaped by societal norms, traditions, and shared experiences. As these models are deployed in diverse linguistic and cultural settings, their ability to understand and apply cultural commonsense becomes crucial for ensuring fairness, inclusivity, and contextual accuracy. This paper presents a systematic review and a large-scale empirical benchmark for evaluating cultural commonsense in multilingual LLMs. Through a comprehensive evaluation of 15 models on the BLEnD dataset, our analysis reveals a critical performance gap of 64.2% between high-resource and low-resource cultures. The results demonstrate significant disparities across model architectures: encoder-only models show more consistent but lower overall performance compared to decoder-based models. We identify key limitations, including data scarcity, representational bias, and inadequate cross-lingual knowledge transfer. Finally, we propose future research directions, such as culturally diverse dataset curation, hybrid knowledge graph architectures, and fairness-aware fine-tuning. The primary contributions of this work are (1) a systematic review of challenges and mitigation strategies for cultural commonsense; (2) a large-scale empirical benchmark that evaluates 15 multilingual LLMs across 13 languages and 16 countries, revealing significant performance disparities; and (3) concrete findings on the effects of model architecture and the limitations of scale in cultural understanding. This research underscores the urgent need to advance cultural commonsense in multilingual LLMs to ensure the development of fair, inclusive, and contextually accurate AI systems globally.

大型语言模型（llm）在多语言自然语言处理（NLP）方面表现出了令人印象深刻的熟练程度，但它们经常与文化常识（由社会规范、传统和共享经验形成的隐性知识）作斗争。由于这些模型被部署在不同的语言和文化环境中，它们理解和应用文化常识的能力对于确保公平性、包容性和上下文准确性至关重要。本文提出了一个系统的审查和大规模的经验基准评估文化常识在多语言法学硕士。通过对BLEnD数据集上的15个模型进行综合评估，我们的分析显示，高资源文化与低资源文化之间的关键绩效差距为64.2%。结果显示了模型架构之间的显著差异：与基于解码器的模型相比，只有编码器的模型显示出更一致但更低的整体性能。我们确定了关键的限制，包括数据稀缺、代表性偏见和跨语言知识转移不足。最后，我们提出了未来的研究方向，如多元文化数据集管理、混合知识图谱架构和公平感知微调。这项工作的主要贡献是：(1)对文化常识的挑战和缓解策略进行了系统的回顾；(2)对16个国家、13种语言的15名多语种法学硕士进行了大规模的实证基准评估，结果显示出显著的绩效差异；(3)关于模型建筑的作用和尺度在文化理解中的局限性的具体发现。这项研究强调了迫切需要在多语言法学硕士中推进文化常识，以确保在全球范围内开发公平、包容和准确的人工智能系统。

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引用次数: 0

Reflection on the convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT) 人工智能驱动的物联网中边缘、雾、云的融合与互动思考

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-03 DOI: 10.1016/j.is.2025.102662

Farshad Firouzi , Bahar Farahani , Alexander Marinšek

As the Information Systems Journal celebrates its 50th Anniversary, we are honored to reflect on the journey and legacy of our 2022 article, “The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)”. The paper introduced a unified architectural framework that advanced the integration of computing, intelligence, and connectivity across the edge–fog–cloud continuum, establishing a foundational model for scalable, adaptive, context-aware, and trustworthy AI-enabled systems. This reflection highlights how the work has shaped our research trajectories, influenced developments within the broader scientific community, and guided innovation, education, and industrial practice.

在《信息系统杂志》庆祝创刊50周年之际，我们很荣幸地回顾我们2022年的文章《人工智能驱动的物联网（IoT）中边缘、雾和云的融合和相互作用》的历程和遗产。本文介绍了一个统一的架构框架，该框架推进了跨边缘雾云连续体的计算、智能和连接的集成，为可扩展、自适应、上下文感知和可信赖的人工智能支持系统建立了一个基础模型。这种反思强调了这项工作如何塑造了我们的研究轨迹，影响了更广泛的科学界的发展，并指导了创新、教育和工业实践。

引用次数: 0

Applying organizational mining to discover agent systems from event data 应用组织挖掘技术从事件数据中发现代理系统

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-31 DOI: 10.1016/j.is.2025.102669

Qingtan Shen , Artem Polyvyanyy , Nir Lipovetzky , Timotheus Kampik

Agent system mining is a recently introduced type of process mining that takes a bottom-up approach to the data-driven analysis of socio-technical systems that execute business processes in organizations. Instead of the top-down approach used in conventional process mining that studies a system in terms of its global state evolution, agent system mining analyzes the system as if it is composed of autonomous agents, each with its local state and behavior, interacting with other agents and the environment to contribute to the emerging global behavior of the business process. Recently, Agent Miner, the first algorithm for discovering agent systems from event data generated by process-aware information systems, has been proposed. The quality of the agent systems discovered by this algorithm depends on the quality of the agent types (or agents), which are identified from the available information about agent instances in the data. In this paper, we study the suitability and benefits of using methods from the organizational mining subarea of process mining for identifying agent types. The experiments we conduct over real-world datasets confirm the usefulness of such methods for discovering simple, modular, and accurate agent systems. These conclusions are grounded in quality metrics such as the size of discovered models (simplicity), Louvain modularity and the Gini coefficient (modularity), and precision and recall (accuracy). The results confirm the benefits of using organizational mining for identifying agent types when discovering agent systems from event data, leading to the construction of models of superior quality in precision, recall, and simplicity compared to models constructed by state-of-the-art conventional process discovery algorithms.

代理系统挖掘是最近引入的一种流程挖掘类型，它采用自底向上的方法对组织中执行业务流程的社会技术系统进行数据驱动分析。与传统流程挖掘中使用的从全局状态演变研究系统的自顶向下方法不同，代理系统挖掘将系统视为由自治代理组成，每个代理都具有其局部状态和行为，与其他代理和环境相互作用，以促进业务流程的新兴全局行为。最近提出了Agent Miner算法，这是第一个从进程感知信息系统生成的事件数据中发现Agent系统的算法。该算法发现的代理系统的质量取决于代理类型（或代理）的质量，这些类型是从数据中关于代理实例的可用信息中识别出来的。在本文中，我们研究了使用过程挖掘的组织挖掘子领域的方法来识别代理类型的适用性和效益。我们在真实世界数据集上进行的实验证实了这些方法对于发现简单、模块化和准确的代理系统的有用性。这些结论是基于质量指标，如发现模型的大小（简单性），鲁文模块化和基尼系数（模块化），以及精度和召回率（准确性）。结果证实了在从事件数据中发现代理系统时使用组织挖掘来识别代理类型的好处，与使用最先进的常规流程发现算法构建的模型相比，可以构建精度、召回率和简单性更高的模型。

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引用次数: 0

Graph-based similarity measures for the structural comparison of process traces 用于过程轨迹结构比较的基于图的相似性度量

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-26 DOI: 10.1016/j.is.2025.102671

Clemens Schreiber , Amine Abbad-Andaloussi , Andrea Burattin , Andreas Oberweis , Barbara Weber

Similarity measures are commonly applied for a variety of process mining techniques, such as trace clustering, conformance checking, and event abstraction. Yet, these measures generally fail to recognize similarity based on structural process features, such as the order of activities, loops, skips, choices, and parallelism. To make this more explicit, we propose a set of properties that allow to evaluate, what kind of structural features are reflected by a similarity measure. We further propose a novel approach leveraging existing graph-based algorithms and instance graphs to extract high-level structural features (loops, skips, choices, and parallelism) from traces, such that they can be used to extend and improve existing similarity measures. These algorithms are well-established in graph theory and can be computed efficiently. Finally, we provide an evaluation of the proposed approach based on synthetic and real-world datasets. The evaluation provides evidence that the additional graph-based features can substantially improve the similarity comparison of traces in several cases. This applies in particular for the comparison of user behavior (e.g., based on eye tracking data) where structural features enable the detection of specific behavioral patterns.

相似性度量通常应用于各种过程挖掘技术，例如跟踪聚类、一致性检查和事件抽象。然而，这些措施通常不能识别基于结构过程特征的相似性，如活动顺序、循环、跳过、选择和并行性。为了使这一点更明确，我们提出了一组属性，允许评估什么样的结构特征是由相似性度量反映出来的。我们进一步提出了一种新的方法，利用现有的基于图的算法和实例图从轨迹中提取高级结构特征（循环、跳过、选择和并行性），这样它们就可以用来扩展和改进现有的相似性度量。这些算法在图论中已经得到了很好的验证，并且可以进行高效的计算。最后，我们基于合成和真实世界的数据集对所提出的方法进行了评估。评估提供的证据表明，在一些情况下，附加的基于图的特征可以大大提高轨迹的相似性比较。这尤其适用于用户行为的比较（例如，基于眼动追踪数据），其中结构特征可以检测特定的行为模式。

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引用次数: 0

DynaHash: An efficient blocking structure for streaming record linkage DynaHash：一种高效的流记录链接阻塞结构

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2026-06-01 Epub Date: 2026-01-16 DOI: 10.1016/j.is.2026.102692

Dimitrios Karapiperis , Christos Tjortjis , Vassilios S. Verykios

Record linkage holds a crucial position in data management and analysis by identifying and merging records from disparate data sets that pertain to the same real-world entity. As data volumes grow, the intricacies of record linkage amplify, presenting challenges, such as potential redundancies and computational complexities. This paper introduces DynaHash, a novel randomized record linkage mechanism that utilizes (a) the MinHash technique to generate compact representations of blocking keys and (b) Hamming Locality-Sensitive Hashing (LSH) to construct the blocking structure from these vectors. By employing these methods, DynaHash offers theoretical guarantees of accuracy and achieves sublinear runtime complexities, with appropriate parameter tuning. It comprises two key components: a persistent storage system for permanently storing the blocking structure to ensure complete results, and an in-memory component for generating very fast partial results by summarizing the persisted blocking structure. Additionally, DynaHash leverages Multi-Probe matching to scan multiple neighboring blocks, in terms of their Hamming distances, in order to find matches. Our theoretical work derives a decrease factor in the space requirements, which depends on the Hamming threshold, compared with the baseline LSH. Our experimental evaluation against three state-of-the-art methods on six real-world data sets demonstrates DynaHash’s exceptional recall rates and query times, which are at least

2 \times

faster than its competitors and do not depend on the size of the underlying data sets.

记录链接在数据管理和分析中占有至关重要的地位，它通过识别和合并来自属于同一个现实世界实体的不同数据集的记录。随着数据量的增长，记录链接的复杂性也随之增加，带来了挑战，比如潜在的冗余和计算复杂性。本文介绍了DynaHash，一种新的随机记录链接机制，它利用(a) MinHash技术生成阻塞键的紧凑表示，(b) Hamming位置敏感哈希（LSH）从这些向量构建阻塞结构。通过使用这些方法，DynaHash提供了准确性的理论保证，并通过适当的参数调优实现了亚线性运行时复杂性。它包括两个关键组件：用于永久存储块结构以确保完整结果的持久存储系统，以及用于通过汇总持久块结构生成非常快的部分结果的内存组件。此外，DynaHash利用Multi-Probe匹配来扫描多个相邻块（根据它们的汉明距离），以便找到匹配项。与基线LSH相比，我们的理论工作导出了空间需求的减少因子，这取决于汉明阈值。我们在六个真实数据集上对三种最先进的方法进行的实验评估表明，DynaHash具有出色的召回率和查询时间，比其竞争对手至少快2倍，并且不依赖于底层数据集的大小。

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引用次数: 0