IEEE Transactions on Knowledge and Data Engineering最新文献

{"title":"S-MGHSTN: Towards An Effective Streaming Traffic Accident Risk Prediction Framework","authors":"Minxiao Chen;Haitao Yuan;Nan Jiang;Zhihan Zheng;Zhifeng Bao;Ao Zhou;Jiaxin Jiang;Shangguang Wang","doi":"10.1109/TKDE.2025.3557864","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3557864","url":null,"abstract":"Traffic accidents pose a significant risk to human health and property safety. To address this issue, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents. In particular, it should adequately consider the streaming nature of data and key related aspects, such as regional background, accurately capture both proximity and similarity while bridging the disparities, and effectively address the sparsity. However, these factors are often overlooked or difficult to incorporate. In this paper, we propose a novel streaming multi-granularity hierarchical spatio-temporal network. Initially, we innovate by incorporating remote sensing data, facilitating the creation of hierarchical multi-granularity structure and the comprehension of regional background. We construct multiple high-level risk prediction tasks to enhance model’s ability to cope with sparsity. Subsequently, to capture and bridge spatial proximity and semantic similarity, region features and multi-view graph undergo encoding processes to distill effective representations, followed by a graph-enhanced representation alignment module that reconciles their disparities. At last, an alternating experience replay with a dual-memory buffer is employed to accommodate streaming data scenarios. Extensive experiments on two real datasets verify the superiority of our model against the state-of-the-art methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 7","pages":"4285-4298"},"PeriodicalIF":8.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232035","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}

{"title":"Knowledge-Centered Dual-Process Reasoning for Math Word Problems With Large Language Models","authors":"Jiayu Liu;Zhenya Huang;Qi Liu;Zhiyuan Ma;Chengxiang Zhai;Enhong Chen","doi":"10.1109/TKDE.2025.3556367","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3556367","url":null,"abstract":"Math word problem (MWP) serves as a critical milestone for assessing the text mining ability and knowledge mastery level of models. Recent advancements have witnessed large language models (LLMs) showcasing remarkable performance on MWP. However, current LLMs still frequently exhibit logical errors, which highlights their inability to fully grasp the knowledge required for genuine step-by-step mathematical reasoning. To this end, in this paper, we propose a novel Knowledge-guided Solver (KNOS) framework that empowers LLMs to simulate human mathematical reasoning, whose core idea is to <italic>Invoke-Verify-Inject</i> necessary knowledge to solve MWP. We draw inspiration from the dual-process theory to construct two cooperative systems: a <italic>Knowledge System</i> and an <italic>Inference System</i>. Specifically, the <italic>Knowledge System</i> employs LLMs as the knowledge base and develops a novel <italic>knowledge invoker</i> that can elicit their relevant knowledge to support the strict step-level mathematical reasoning. In the <italic>Inference System</i>, we propose a <italic>knowledge verifier</i> and a <italic>knowledge injector</i> to evaluate the knowledge rationality and further guide the step-wise symbolic deduction in an interpretable manner based on human cognitive mechanism, respectively. Moreover, to tackle the potential scarcity issue of mathematics-specific knowledge in LLMs, we consider an open-book exam scenario and propose an improved version of KNOS called EKNOS. In EKNOS, we meticulously design <italic>knowledge selectors</i> to extract the most relevant commonsense and math formulas from external knowledge sources for each reasoning step. This knowledge is utilized to assist the <italic>knowledge invoker</i> in better stimulating LLMs’ reasoning abilities. Both KNOS and EKNOS are flexible to empower different LLMs. Our experiments with GPT3, ChatGPT, and GPT4 not only demonstrate their reasoning accuracy improvement but also show how they bring the strict step-wise interpretability of mathematical thinking.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3457-3471"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896230","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}

{"title":"Inconsistent Multivariate Time Series Forecasting","authors":"Li Shen;Yangzhu Wang;Xuyi Fan;Xu Yang;Huaxin Qiu","doi":"10.1109/TKDE.2025.3556940","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3556940","url":null,"abstract":"Traditional statistical time series forecasting models rely on model identification methods to identify the worthiest model variants to investigate; therefore, the model parameters change with the statistical features of rolling windows to reach optimality. Currently, although deep-learning-based methods achieve promising multivariate forecasting performance, their representations of variable correlations are consistent regardless of the observed local time series properties and dynamic cross-variable relations, rendering them prone to overfitting. To bridge this gap, we propose FPPformer-MD, a novel inconsistent time series forecasting transformer. FPPformer-MD leverages multiresolution analysis to transform each univariate series into multiple frequency scales and evaluate the local variable correlations via their variances. Thus, FPPformer-MD receives richer input features, and its inner inconsistent cross-variable attention mechanism enables the adaptive extraction of cross-variable features. To further alleviate the overfitting problem, we apply dynamic mode decomposition to perform cross-variable data augmentation, which reconstructs the sequence outliers with other correlated sequences during the model training process. Extensive experiments conducted on thirteen real-world benchmarks demonstrate the state-of-the-art performance of FPPformer-MD.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 7","pages":"4117-4130"},"PeriodicalIF":8.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219583","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}

{"title":"Temporal and Spatial Analysis in Early Sepsis Prediction via Causal Disentanglements","authors":"Qiang Li;Dongchen Li;Weizhi Nie;He Jiao;Zhenhua Wu;Anan Liu","doi":"10.1109/TKDE.2025.3569584","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3569584","url":null,"abstract":"Sepsis is one of the main causes of death in ICU patients, and accurate and stable early prediction is essential for clinical intervention. Existing methods mostly rely on traditional time series models (e.g., LSTM, Transformer) or clinical scoring criteria (e.g., SOFA, qSOFA), but face two major challenges: 1) spurious correlations in the data affect the robustness of the model; 2) Lack of modeling the underlying causal relationships in the data space. We propose a Serialized Causal Disentanglement Model (SCDM) that decouples latent variables into sepsis-related factors (<inline-formula><tex-math>$u$</tex-math></inline-formula>), other disease-related factors (<inline-formula><tex-math>$v$</tex-math></inline-formula>), and irrelevant confounders (<inline-formula><tex-math>$s$</tex-math></inline-formula> ). Based on the MIMIC-IV v2.2 dataset (3,511 positive samples and 17,538 negative samples), SCDM took patient clinical indicators, personal information, and clinical notes as input, and achieved an AUC of 0.765-0.928in the prediction task 48 to 0 hours before the onset of sepsis. The performance is significantly better than the baseline models (e.g., Transformer's 0.662-0.910, MGP-AttTCN's 0.692-0.913). Experiments show that optimizing the time window (5 hours of continuous observation) and variable selection (45 key indicators) can improve the performance of the model. The effectiveness of causal unwinding is verified by the visualization of Grad CAM and t-SNE, key clinical indicators such as platelet count, lactic acid, and respiratory rate are further identified to provide interpretable decision support for doctors. Our study provides a high-precision and interpretable causal disentanglement framework for early prediction of sepsis, which is expected to promote the development of intelligent diagnosis and treatment in the ICU.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4860-4872"},"PeriodicalIF":8.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572985","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}

{"title":"SE-GNN: Seed Expanded-Aware Graph Neural Network With Iterative Optimization for Semi-Supervised Entity Alignment","authors":"Tao Meng;Shuo Shan;Hongen Shao;Yuntao Shou;Wei Ai;Keqin Li","doi":"10.1109/TKDE.2025.3555586","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3555586","url":null,"abstract":"Entity alignment aims to use pre-aligned seed pairs to find other equivalent entities from different knowledge graphs and is widely used in graph fusion-related fields. However, as the scale of knowledge graphs increases, manually annotating pre-aligned seed pairs becomes difficult. Existing research utilizes entity embeddings obtained by aggregating single structural information to identify potential seed pairs, thus reducing the reliance on pre-aligned seed pairs. However, due to the structural heterogeneity of KG, the quality of potential seed pairs obtained using only a single structural information is not ideal. In addition, although existing research improves the quality of potential seed pairs through semi-supervised iteration, they underestimate the impact of embedding distortion produced by noisy seed pairs on the alignment effect. In order to solve the above problems, we propose a seed expanded-aware graph neural network with iterative optimization for semi-supervised entity alignment, named SE-GNN. First, we utilize the semantic attributes and structural features of entities, combined with a conditional filtering mechanism, to obtain high-quality initial potential seed pairs. Next, we designed a local and global awareness mechanism. It introduces initial potential seed pairs and combines local and global information to obtain a more comprehensive entity embedding representation, which alleviates the impact of KG structural heterogeneity and lays the foundation for the optimization of initial potential seed pairs. Then, we designed the threshold nearest neighbor embedding correction strategy. It combines the similarity threshold and the bidirectional nearest neighbor method as a filtering mechanism to select iterative potential seed pairs and also uses an embedding correction strategy to eliminate the embedding distortion. Finally, we will reach the optimized potential seeds after iterative rounds to input local and global sensing mechanisms, obtain the final entity embedding, and perform entity alignment. Experimental results on public datasets demonstrate the excellent performance of our SE-GNN, showcasing the effectiveness of the model. Our code is publicly available at <uri>https://github.com/ShuoShan1/SE-GNN</uri>.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3700-3713"},"PeriodicalIF":8.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896460","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}

{"title":"Ten Challenging Problems in Federated Foundation Models","authors":"Tao Fan;Hanlin Gu;Xuemei Cao;Chee Seng Chan;Qian Chen;Yiqiang Chen;Yihui Feng;Yang Gu;Jiaxiang Geng;Bing Luo;Shuoling Liu;Win Kent Ong;Chao Ren;Jiaqi Shao;Chuan Sun;Xiaoli Tang;Hong Xi Tae;Yongxin Tong;Shuyue Wei;Fan Wu;Wei Xi;Mingcong Xu;He Yang;Xin Yang;Jiangpeng Yan;Hao Yu;Han Yu;Teng Zhang;Yifei Zhang;Xiaojin Zhang;Zhenzhe Zheng;Lixin Fan;Qiang Yang","doi":"10.1109/TKDE.2025.3555328","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3555328","url":null,"abstract":"Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the small local domain models at the remote clients to learn from each other in a teacher-student learning setting. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency. The ten challenging problems manifest in five pivotal aspects: “Foundational Theory,” which aims to establish a coherent and unifying theoretical framework for FedFMs. “Data,” addressing the difficulties in leveraging domain-specific knowledge from private data while maintaining privacy; “Heterogeneity,” examining variations in data, model, and computational resources across clients; “Security and Privacy,” focusing on defenses against malicious attacks and model theft; and “Efficiency,” highlighting the need for improvements in training, communication, and parameter efficiency. For each problem, we offer a clear mathematical definition on the objective function, analyze existing methods, and discuss the key challenges and potential solutions. This in-depth exploration aims to advance the theoretical foundations of FedFMs, guide practical implementations, and inspire future research to overcome these obstacles, thereby enabling the robust, efficient, and privacy-preserving FedFMs in various real-world applications.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 7","pages":"4314-4337"},"PeriodicalIF":8.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232122","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}

{"title":"Per-Flow Quantile Estimation Using M4 Framework","authors":"Zhuochen Fan;Yalun Cai;Siyuan Dong;Qiuheng Yin;Tianyu Bai;Hanyu Xue;Peiqing Chen;Yuhan Wu;Tong Yang;Bin Cui","doi":"10.1109/TKDE.2025.3573812","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3573812","url":null,"abstract":"This paper introduces a novel framework, M4, designed to estimate per-flow quantiles in data streams accurately. M4 is a versatile framework that can be integrated with a wide array of single-flow quantile estimation algorithms, thereby enabling them to perform per-flow estimation. The framework employs a sketch-based approach to provide a space-efficient method for recording and extracting distribution information. M4 incorporates two techniques: <i>MINIMUM</i> and <i>SUM</i>. The <i>MINIMUM</i> technique minimizes the noise on a flow from other flows caused by hash collisions, while the <i>SUM</i> technique efficiently categorizes flows based on their sizes and customizes treatment strategies accordingly. We demonstrate the application of M4 on three single-flow quantile estimation algorithms (DDSketch, <inline-formula><tex-math>$t$</tex-math></inline-formula>-digest, and ReqSketch), detailing the specific implementation of the <i>MINIMUM</i> and <i>SUM</i> techniques. We provide theoretical proof that M4 delivers high accuracy while utilizing limited memory. Additionally, we conduct extensive experiments to evaluate the performance of M4 regarding accuracy and speed. The experimental results indicate that across all three example algorithms, M4 significantly outperforms two comparison frameworks in terms of accuracy for per-flow quantile estimation while maintaining comparable speed.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4758-4775"},"PeriodicalIF":8.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572987","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}

{"title":"SAGoG: Similarity-Aware Graph of Graphs Neural Networks for Multivariate Time Series Classification","authors":"Shun Wang;Yong Zhang;Xuanqi Lin;Yongli Hu;Qingming Huang;Baocai Yin","doi":"10.1109/TKDE.2025.3572216","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3572216","url":null,"abstract":"Multivariate Time Series Classification (MTSC) has important research significance and practical value. Deep learning models have achieved considerable success in addressing MTSC problems. However, a key challenge faced by existing classification models is how to effectively consider the correlations between time series instances and across channels simultaneously, as well as how to capture the dynamic of these inter-channel correlations over time. Current methods often fall short in these aspects: on one hand, they fail to fully account for the combined effects of inter-instance and inter-channel correlations; on the other hand, they largely overlook the dynamic nature of how inter-channel correlations change over time. To address these issues, we propose a novel graph neural network model, called Similarity-Aware Graph of Graphs neural networks (SAGoG), for multivariate time series classification. This model can comprehensively consider the dependencies between channel-level and instance-level time series, it dynamically learns dependency features through graph structure evolution and graph pooling layers. We conduct experiments on the UEA dataset to validate the SAGoG model, and the results demonstrate its outstanding performance in multivariate time series classification tasks.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4820-4832"},"PeriodicalIF":8.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573010","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}

{"title":"User-Friendly and Expressive Forward-Secure Attribute-Based Signature With Server-Aided Signature and Outsourced Verification","authors":"Chao Guo;Yang Lu;Nian Xia;Jiguo Li","doi":"10.1109/TKDE.2025.3554973","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3554973","url":null,"abstract":"Attribute-based signature (ABS) is an attractive variation of digital signature that enables signers to sign messages with fine-grained signature predicates. In ABS, a signer is able to perform signing operations without revealing personal attributes, and verifiers can only confirm that the signature was created by someone with attributes satisfying a specific signature predicate. However, traditional ABS suffers from key exposure, and the compromise of a signer’s signature key results in invalidating all signatures from him/her. To address this problem, forward-secure ABS (FS-ABS) was introduced. Nevertheless, existing FS-ABS schemes have the shortcomings of low policy expressiveness and high computation costs, and thus are not suitable to be employed on mobile devices with limited resources. In this paper, we propose a user-friendly and expressive FS-ABS (UEFS-ABS) scheme that is proven secure in the standard model. The proposed scheme not only supports expressive signature predicates based on the linear secret sharing scheme, but also provides server-aided signature and outsourced verification functions, significantly reducing the workload of user terminals at both signature generation and verification stages. The experiments indicate that compared with the up-to-date FS-ABS scheme, our scheme reduces the computation costs for signature generation (on signers’ devices) and verification (on verifiers’ devices) by about 85% and 68%, respectively. This makes our scheme more suitable for user terminals in mobile computing scenarios.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3794-3809"},"PeriodicalIF":8.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896390","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}

{"title":"Nonconvex Low-Rank Tensor Representation for Multi-View Subspace Clustering With Insufficient Observed Samples","authors":"Meng Ding;Jing-Hua Yang;Xi-Le Zhao;Jie Zhang;Michael K. Ng","doi":"10.1109/TKDE.2025.3555043","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3555043","url":null,"abstract":"Multi-view subspace clustering (MVSC) separates the data with multiple views into multiple clusters, and each cluster corresponds to one certain subspace. Existing tensor-based MVSC methods construct self-representation subspace coefficient matrices of all views as a tensor, and introduce the tensor nuclear norm (TNN) to capture the complementary information hidden in different views. The key assumption is that the data samples of each subspace must be sufficient for subspace representation. This work proposes a nonconvex latent transformed low-rank tensor representation framework for MVSC. To deal with the insufficient sample problem, we study the latent low-rank representation in the multi-view case to supplement underlying observed samples. Moreover, we propose to use data-driven transformed TNN (TTNN), resulting from the intrinsic structure of multi-view samples, to preserve the consensus and complementary information in the transformed domain. Meanwhile, the proposed unified nonconvex low-rank tensor representation framework can better learn the high correlation among different views. To resolve the proposed nonconvex optimization model, we propose an effective algorithm under the framework of the alternating direction method of multipliers and theoretically prove that the iteration sequences converge to the critical point. Experiments on various datasets showcase outstanding performance.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3583-3597"},"PeriodicalIF":8.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896224","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}