Information Systems最新文献_第7页

Comprehensive characterization of concept drifts in process mining 工艺采矿中概念漂移的综合表征

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-07-19 DOI: 10.1016/j.is.2025.102584

Alexander Kraus , Han van der Aa

{"title":"Comprehensive characterization of concept drifts in process mining","authors":"Alexander Kraus , Han van der Aa","doi":"10.1016/j.is.2025.102584","DOIUrl":"10.1016/j.is.2025.102584","url":null,"abstract":"<div><div>Business processes are subject to changes due to the dynamic environments in which they are executed. These process changes can lead to concept drifts, which are situations when the characteristics of a business process have undergone significant changes, resulting in event logs that contain data on different versions of a process. The accuracy and usefulness of process mining results derived from such event logs may be compromised because they rely on historical data that no longer reflects the current process behavior, or because the results do not distinguish between different process versions. Therefore, concept drift detection in process mining aims to identify drifts recorded in an event log by detecting when they occurred, localizing process modifications, and characterizing how they manifest over time. This paper focuses on the latter task, i.e., drift characterization, which seeks to understand whether changes unfolded suddenly or gradually and if they form complex patterns like incremental or recurring drifts. However, current solutions for automatically detecting concept drifts from event logs lack comprehensive characterization capabilities. Instead, they mainly focus on drift detection and characterization of isolated process changes. This leads to an incomplete understanding of more complex concept drifts, like incremental and recurring drifts, when several process changes are inter-connected. This paper overcomes such limitations by introducing an improved taxonomy for characterizing concept drifts and a three-step framework that provides an automatic characterization of concept drifts from event logs. We evaluated our framework through elaborate evaluation experiments conducted using a large collection of synthetic event logs. The results highlight the effectiveness and accuracy of our proposed framework and show that it outperforms state-of-the-art techniques.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102584"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GlintLM: Graph-Layered Integration with Nodal Topology with Language Models — A Bipartite Approach to Question Answering 基于语言模型的节点拓扑的图层集成——一种二分法的问题回答方法

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-08-20 DOI: 10.1016/j.is.2025.102610

ZhuoFan Chen , Yao Hui Hoon , Renne Ye Kai Ong , Justin Juin Hng Wong

{"title":"GlintLM: Graph-Layered Integration with Nodal Topology with Language Models — A Bipartite Approach to Question Answering","authors":"ZhuoFan Chen , Yao Hui Hoon , Renne Ye Kai Ong , Justin Juin Hng Wong","doi":"10.1016/j.is.2025.102610","DOIUrl":"10.1016/j.is.2025.102610","url":null,"abstract":"<div><div>In modern Question Answering (QA) systems, Language Models (LMs) are often combined with Knowledge Graphs (KGs) to better handle challenges like word ambiguity and complex sentence structures. This combination helps LMs gain a deeper understanding by grounding them in structured knowledge. However, existing approaches often fall short in two areas: (1) they do not fully use the features of Knowledge Graphs and Graph Neural Networks (GNNs) during reasoning, and (2) they miss opportunities to better rank and filter information using the outputs of LMs and GNNs. To address this, we propose GlintLM, a system with two key innovations. First, the Enhanced Topological Node Representation (ETNR) module, which uses graph structure and a custom node feature method to improve reasoning. Second, the Multiplex Contextual Scorer (MCS) module, which combines pre-trained LM outputs with GNN attention to better score and filter relevant nodes. Together, these components create a more effective and adaptable system for QA. GlintLM demonstrates improved performance on common-sense (CommonsenseQA, OpenBookQA) and biomedical (MedQA-USMLE) QA benchmarks, showing improved performance across commonsense and medical domains.<span><span><sup>2</sup></span></span></div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102610"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the evaluation and optimization of LabeledPAM LabeledPAM的评价与优化

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-07-22 DOI: 10.1016/j.is.2025.102580

Miriama Jánošová , Andreas Lang , Petra Budikova , Erich Schubert , Vlastislav Dohnal

{"title":"On the evaluation and optimization of LabeledPAM","authors":"Miriama Jánošová , Andreas Lang , Petra Budikova , Erich Schubert , Vlastislav Dohnal","doi":"10.1016/j.is.2025.102580","DOIUrl":"10.1016/j.is.2025.102580","url":null,"abstract":"<div><div>The analysis of complex and weakly labeled data is increasingly popular. Traditional unsupervised clustering aims to uncover interrelated sets of objects based on feature-based similarity. This approach often reaches its limits when dealing with complex multimedia data due to the curse of dimensionality, presenting unique challenges. Semi-supervised clustering, which leverages small amounts of labeled data, has the potential to cope with this problem.</div><div>In this work, we delve into LabeledPAM, a semi-supervised clustering method, which extends FasterPAM, a state-of-the-art <span><math><mi>k</mi></math></span>-medoids clustering algorithm. Our algorithm is designed for both semi-supervised classification, where labels are assigned to clusters with minimal labeled data, and semi-supervised clustering, where new clusters with unknown labels are identified. We propose an optimization to the original LabeledPAM algorithm that reduces its computational complexity. Additionally, we provide an implementation in Rust, which integrates seamlessly with Python libraries.</div><div>To assess LabeledPAM’s performance, we empirically evaluate its properties by comparing it against a range of semi-supervised clustering algorithms, including density-based ones. We conduct experiments on a collection of real-world datasets. Our results demonstrate that LabeledPAM achieves competitive clustering quality while maintaining efficiency across various scenarios, showing its versatility for real-world applications.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102580"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-code solutions for business process dataflows: From modeling to execution 业务流程数据流的低代码解决方案：从建模到执行

IF 3 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-07-18 DOI: 10.1016/j.is.2025.102577

Ali Nour Eldin , Jonathan Baudot , Benjamin Dalmas , Walid Gaaloul

{"title":"Low-code solutions for business process dataflows: From modeling to execution","authors":"Ali Nour Eldin , Jonathan Baudot , Benjamin Dalmas , Walid Gaaloul","doi":"10.1016/j.is.2025.102577","DOIUrl":"10.1016/j.is.2025.102577","url":null,"abstract":"<div><div>Business Process Modeling and Notation (BPMN) is a widely adopted standard for modeling business workflows. However, the increasing complexity and integration of data within business processes demand a modeling language capable of clearly expressing both process and data perspectives. While BPMN effectively represents process control flows, it inadequately addresses critical data-related aspects such as data flow, data dependencies, and data transformations. Moreover, communication gaps and differing interpretations of process requirements frequently arise between developers and business analysts, leading to errors and delays in process implementation and execution.</div><div>To address these limitations, this paper introduces an extension of BPMN, termed the Business Process and Data Modeling Language (BPDML). BPDML is a low-code modeling language specifically designed to capture, model, and execute data-driven business processes. By adopting a low-code approach, BPDML bridges the gap between business analysts and developers, facilitating faster development and delivery of business applications with reduced effort and minimal manual coding. In addition, a specialized modeling tool has been developed to support the creation, validation, and execution of models using BPDML. Both quantitative and qualitative evaluations demonstrate that BPDML significantly enhances the clarity, efficiency, and overall effectiveness of business process modeling and implementation compared to traditional BPMN.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102577"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal relational algebras supporting preferences in temporal relational databases: Definition, properties and evaluation 支持时态关系数据库中首选项的时态关系代数：定义、属性和评估

IF 3 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-07-17 DOI: 10.1016/j.is.2025.102583

Luca Anselma , Antonella Coviello , Davide Cerotti , Erica Raina , Paolo Terenziani

{"title":"Temporal relational algebras supporting preferences in temporal relational databases: Definition, properties and evaluation","authors":"Luca Anselma , Antonella Coviello , Davide Cerotti , Erica Raina , Paolo Terenziani","doi":"10.1016/j.is.2025.102583","DOIUrl":"10.1016/j.is.2025.102583","url":null,"abstract":"<div><div>Despite numerous approaches address the treatment of time within relational contexts, temporal preferences remain unexplored. Many tasks and applications, such as planning, scheduling, workflows, and guidelines, involve scenarios where the exact timing of events is not known — referred to as <em>indeterminate time</em>. In such cases, preferences can be assigned to different possible temporal outcomes. In a recent study, we established the theoretical foundation for handling preferential indeterminate time in temporal relational databases. This includes proposing a temporal relational representation and a corresponding temporal relational algebra, along with an analysis of their theoretical properties, such as correctness and reducibility.</div><div>The contributions of this paper are twofold. First, we extend the above theoretical framework to deal with a more expressive representation of temporal preferences. Second, we assess both theoretical frameworks in terms of performance evaluation along different dimensions, and study the overhead added to cope with preferences with respect to relational approaches without time, with exact time, and with indeterminate time but no preferences.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102583"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CrossER: A robust and adaptable generalized entity resolution framework for diverse and heterogeneous datasets CrossER：一个鲁棒且适应性强的通用实体解析框架，适用于各种异构数据集

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-08-14 DOI: 10.1016/j.is.2025.102609

Yunong Tian , Ning Wang , Anshun Zhou

{"title":"CrossER: A robust and adaptable generalized entity resolution framework for diverse and heterogeneous datasets","authors":"Yunong Tian , Ning Wang , Anshun Zhou","doi":"10.1016/j.is.2025.102609","DOIUrl":"10.1016/j.is.2025.102609","url":null,"abstract":"<div><div>Entity Resolution (ER) is a critical task in data cleaning and integration, traditionally focusing on structured relational tables with aligned schemas. However, real-world applications often involve diverse data formats, leading to the emergence of Generalized Entity Resolution, which addresses structured, semi-structured, and unstructured data. While prompt-based methods have shown promise in improving entity resolution, they suffer from significant limitations such as sensitivity to prompt design and instability across heterogeneous data formats. To address these challenges, we propose CrossER, a novel framework that integrates cross-attention mechanisms, contrastive learning, and data augmentation. CrossER employs a cross-attention module to dynamically align attributes across heterogeneous data sources, enabling accurate entity resolution. To enhance robustness, contrastive learning constructs discriminative feature representations, and data augmentation introduces variability to improve adaptability to noisy and complex datasets. Experimental results on multiple real-world datasets demonstrate that CrossER significantly outperforms state-of-the-art Generalized Entity Resolution methods in F1 scores while maintaining computational efficiency. Furthermore, CrossER exhibits minimal dependency on specific pre-trained language models and delivers superior recall rates compared to baseline methods, especially in challenging heterogeneous datasets.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102609"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Density based learned spatial index for clustered data 基于密度的聚类数据学习空间索引

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-08-11 DOI: 10.1016/j.is.2025.102606

Xiaofei Zhao, Kam-Yiu Lam

{"title":"Density based learned spatial index for clustered data","authors":"Xiaofei Zhao, Kam-Yiu Lam","doi":"10.1016/j.is.2025.102606","DOIUrl":"10.1016/j.is.2025.102606","url":null,"abstract":"<div><div>Retrieving spatial points, such as GPS records or Point of Interests, that satisfy specific location-based query criteria is a core operation in location-based services. Recent studies have shown that learned indexes can outperform traditional indexing methods in both query performance and space efficiency by leveraging data distribution to construct compact predictive models. On the other hand, traditional indexes typically make minimal assumptions about the underlying data distribution. In real-world spatial databases, data is often non-uniformly distributed and tends to cluster in specific regions or along road networks. Adaptivity to such data patterns may bring performance benefits.</div><div>In this paper, we explore the construction of efficient learned indexes that exploit the clustering characteristics of spatial datasets. Specifically, we propose a Density-based Grid Learning Spatial Index (DGLSI), which partitions the spatial domain based on point density and utilizes learned models, including multiple recursive model indexes to predict the grid cell IDs of query points. We evaluate DGLSI’s performance on real-world GPS datasets and demonstrate that the proposed methods outperform analogous grid-based indexes across various query workloads, including nearest point queries and range queries while maintaining high space efficiency.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102606"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Personalized Recommendation Systems: A systematic Review 自适应个性化推荐系统：系统综述

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-01-01 Epub Date: 2025-08-21 DOI: 10.1016/j.is.2025.102594

Bachir Asri, Sara Qassimi, Said Rakrak

{"title":"Adaptive Personalized Recommendation Systems: A systematic Review","authors":"Bachir Asri, Sara Qassimi, Said Rakrak","doi":"10.1016/j.is.2025.102594","DOIUrl":"10.1016/j.is.2025.102594","url":null,"abstract":"<div><div>Recommender systems assist users in navigating the vast selection of choices by offering personalized suggestions based on preferences. Originally used in e-commerce and streaming services, these systems are now applied in various sectors such as healthcare, education, and more, making them increasingly important. Despite their growth, recommender systems still face challenges, especially when addressing users whose preferences change over time.</div><div>This paper presents a review of recent research on recommender systems that deliver personalized and adaptive recommendations for users with evolving preferences. Analyzing 97 studies published between 2020 and 2024, the review categorizes them across multiple dimensions to address key research questions.</div><div>The findings reveal a diverse landscape of evaluation metrics, datasets, adaptation mechanisms, and application domains within adaptive personalized recommender systems (AdPRSs), with MovieLens as the most widely used dataset and the attention mechanism as the predominant adaptation approach. Furthermore, the review introduces a novel categorization of AdPRSs based on adaptation mechanism. By synthesizing current research, this review highlights key challenges faced in the field and identifies future directions for enhancing the efficiency and effectiveness of AdPRSs. These insights are of significant value to both practitioners and academic researchers, providing a foundation for advancing the development and optimization of AdPRSs.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"135 ","pages":"Article 102594"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PARK: Personalized academic retrieval with knowledge-graphs 个性化学术检索与知识图谱

IF 3 2区计算机科学

Information Systems Pub Date : 2025-10-01 Epub Date: 2025-06-03 DOI: 10.1016/j.is.2025.102574

Pranav Kasela , Gabriella Pasi , Raffaele Perego

{"title":"PARK: Personalized academic retrieval with knowledge-graphs","authors":"Pranav Kasela , Gabriella Pasi , Raffaele Perego","doi":"10.1016/j.is.2025.102574","DOIUrl":"10.1016/j.is.2025.102574","url":null,"abstract":"<div><div>Academic Search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers. Personalization in this context meets individual researchers’ needs by leveraging, through user profiles, the user related information (e.g. documents authored by a researcher), to improve search effectiveness and to reduce the information overload. While citation graphs are a valuable means to support the outcome of recommender systems, their use in personalized academic search (with, e.g. nodes as papers and edges as citations) is still under-explored.</div><div>Existing personalized models for academic search often struggle to fully capture users’ academic interests. To address this, we propose a two-step approach: first, training a neural language model for retrieval, then converting the academic graph into a knowledge graph and embedding it into a shared semantic space with the language model using translational embedding techniques. This allows user models to capture both explicit relationships and hidden structures in citation graphs and paper content. We evaluate our approach in four academic search domains, outperforming traditional graph-based and personalized models in three out of four, with up to a 10% improvement in MAP@100 over the second-best model. This highlights the potential of knowledge graph-based user models to enhance retrieval effectiveness.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"134 ","pages":"Article 102574"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven prescriptive analytics applications: A comprehensive survey 数据驱动的规范分析应用程序：全面调查

IF 3 2区计算机科学

Information Systems Pub Date : 2025-10-01 Epub Date: 2025-06-17 DOI: 10.1016/j.is.2025.102576

Martin Moesmann, Torben Bach Pedersen

{"title":"Data-driven prescriptive analytics applications: A comprehensive survey","authors":"Martin Moesmann, Torben Bach Pedersen","doi":"10.1016/j.is.2025.102576","DOIUrl":"10.1016/j.is.2025.102576","url":null,"abstract":"<div><div>Prescriptive Analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a comprehensive survey of existing applications within PSA in terms of their use cases, methodologies, and possible future research directions. To ensure a manageable scope, we focus on PSA applications that develop data-driven, automatic workflows, i.e., <em>Data-Driven PSA (DPSA)</em>. Following a systematic methodology, we identify and include 104 papers in our survey. As our key contributions, we derive a number of novel taxonomies of the field and use them to analyse the field’s temporal development. In terms of use cases, we derive <em>10 application domains</em> for DPSA, from Healthcare to Manufacturing, and subsumed problem types within each. In terms of <em>individual</em> method usage, we derive <em>5 method types</em> and map them to a comprehensive taxonomy of method usage within DPSA applications, covering <em>mathematical optimization</em>, <em>data mining and machine learning</em>, <em>probabilistic modelling</em>, <em>domain expertise</em>, as well as <em>simulations</em>. As for <em>combined</em> method usage, we provide a statistical overview of how different method usage combinations are distributed and derive <em>2 generic workflow patterns</em> along with subsumed workflow patterns, combining methods by either sequential or simultaneous relationships. Finally, we derive <em>5 possible research directions</em> based on frequently recurring issues among surveyed papers, suggesting new frontiers in terms of methods, tools, and use cases.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"134 ","pages":"Article 102576"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0