Anomaly detection on sequence dataset typically focuses on the detection of collective anomalies, aiming to find anomalous patterns consisting of sequences of data with specific relationships rather than individual observations. In this survey, existing studies are summarized to align with temporal sequence dataset and spatial sequence dataset. For the first category, the detection can be subdivided into symbolic dataset based and time series dataset based, which include similarity, probabilistic, and trend approaches. For the second category, it can be subdivided into homogeneous datasets based heterogeneous datasets based, which include multi-dataset fusion and joint approaches. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of various representations of collective anomaly in different application field and their corresponding detection methods, representative techniques. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.
{"title":"A Survey of Collective Anomaly Detection on Sequence Dataset","authors":"Xiaodi Huang, Po Yun, Zhongfeng Hu","doi":"10.4018/ijdwm.327363","DOIUrl":"https://doi.org/10.4018/ijdwm.327363","url":null,"abstract":"Anomaly detection on sequence dataset typically focuses on the detection of collective anomalies, aiming to find anomalous patterns consisting of sequences of data with specific relationships rather than individual observations. In this survey, existing studies are summarized to align with temporal sequence dataset and spatial sequence dataset. For the first category, the detection can be subdivided into symbolic dataset based and time series dataset based, which include similarity, probabilistic, and trend approaches. For the second category, it can be subdivided into homogeneous datasets based heterogeneous datasets based, which include multi-dataset fusion and joint approaches. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of various representations of collective anomaly in different application field and their corresponding detection methods, representative techniques. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43124738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.
{"title":"Cybersecurity of Medical Data Based on Big Data and Privacy Protection Method","authors":"Jianhong Li, An Pan, Tongxing Zheng","doi":"10.4018/ijdwm.325222","DOIUrl":"https://doi.org/10.4018/ijdwm.325222","url":null,"abstract":"Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46241194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Skin cancer is affected by the uncommon evolution of skin cells and is a deadly type of cancer. In addition, skin lesion is affected by numerous factors, such as exposure to the sun, infections, allergies, etc. These skin illnesses have become a challenge in therapeutic diagnosis because of virtual resemblances, where image classification is vital to sufficiently diagnose dissimilar lesions. Therefore, early diagnosis is significant and can avert skin cancers like focal cell carcinoma and melanoma. A deep learning-based computer analyzing model can be an automatic solution in medical evaluations to overcome this issue. Hence, this paper suggests an improved chameleon swarm algorithm and convolutional neural networks (ICSA-CNN) for effective skin cancer identification and classification. The data are collected from the Kaggle dataset for classifying skin cancer. Chameleon swarm algorithm is a clustering technique utilized in data mining to the cluster dataset utilizing dynamic systems, and it can resolve constrained and global numerical optimization issues in skin cancer detection.
{"title":"Application of Improved Chameleon Swarm Algorithm and Improved Convolution Neural Network in Diagnosis of Skin Cancer","authors":"Wu Beibei, Nikolaj Jade","doi":"10.4018/ijdwm.325059","DOIUrl":"https://doi.org/10.4018/ijdwm.325059","url":null,"abstract":"Skin cancer is affected by the uncommon evolution of skin cells and is a deadly type of cancer. In addition, skin lesion is affected by numerous factors, such as exposure to the sun, infections, allergies, etc. These skin illnesses have become a challenge in therapeutic diagnosis because of virtual resemblances, where image classification is vital to sufficiently diagnose dissimilar lesions. Therefore, early diagnosis is significant and can avert skin cancers like focal cell carcinoma and melanoma. A deep learning-based computer analyzing model can be an automatic solution in medical evaluations to overcome this issue. Hence, this paper suggests an improved chameleon swarm algorithm and convolutional neural networks (ICSA-CNN) for effective skin cancer identification and classification. The data are collected from the Kaggle dataset for classifying skin cancer. Chameleon swarm algorithm is a clustering technique utilized in data mining to the cluster dataset utilizing dynamic systems, and it can resolve constrained and global numerical optimization issues in skin cancer detection.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136355362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, the authors devised a big data-driven evaluation model to measure the effect of college music education, aiming at filling the gaps of poor accuracy and time-consuming results of music education effect evaluation. Firstly, the authors determined the effect of an evaluation system of music for learning, and the evaluation of this effect. Then, they carried out a simulation experiment. The literature review evidenced that few domestic research reports considered college students' communication fear. Thus, combining the characteristics of current college students' psychological counseling and the theory of communication fear, the authors tried to apply the music system desensitization therapy to address college students' communication fear, from the intervention effect, feasibility, and therapy as a psychological counseling method. The results showed that music system desensitization therapy eliminates college students' fear of communication, reduces speech anxiety, reduces shyness, and improves interpersonal communication skills.
{"title":"Construction and Application of a Big Data Analysis Platform for College Music Education for College Students' Mental Health","authors":"Xiaochen Wang, Tao Wang","doi":"10.4018/ijdwm.324060","DOIUrl":"https://doi.org/10.4018/ijdwm.324060","url":null,"abstract":"In this study, the authors devised a big data-driven evaluation model to measure the effect of college music education, aiming at filling the gaps of poor accuracy and time-consuming results of music education effect evaluation. Firstly, the authors determined the effect of an evaluation system of music for learning, and the evaluation of this effect. Then, they carried out a simulation experiment. The literature review evidenced that few domestic research reports considered college students' communication fear. Thus, combining the characteristics of current college students' psychological counseling and the theory of communication fear, the authors tried to apply the music system desensitization therapy to address college students' communication fear, from the intervention effect, feasibility, and therapy as a psychological counseling method. The results showed that music system desensitization therapy eliminates college students' fear of communication, reduces speech anxiety, reduces shyness, and improves interpersonal communication skills.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45213567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since its birth, supply chain finance (SCF) has made contributions to the development of small and medium-sized enterprises, but it also faces many challenges in the development process. With the development and continuous progress of the internet and information technology, it has also opened up new ways for urban development and innovation. This article introduced the background of intelligent business model, conducted academic research and summary on the keywords of SCF and the internet of things (IOT), and then summarized urban analysis by combining AI and big data. Then it put forward the business model factor analysis of SCF and modern logistics enterprises. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average transaction cost of the new business model was 3.5 lower than that of the traditional business model. With the continuous development of artificial intelligence technology and big data technology, urban planning is also facing new opportunities and challenges.
{"title":"Assistance of Internet of Things to Intelligent Business Management Model of Supply Chain Finance and Modern Logistics Enterprises","authors":"Qing Li","doi":"10.4018/ijdwm.323189","DOIUrl":"https://doi.org/10.4018/ijdwm.323189","url":null,"abstract":"Since its birth, supply chain finance (SCF) has made contributions to the development of small and medium-sized enterprises, but it also faces many challenges in the development process. With the development and continuous progress of the internet and information technology, it has also opened up new ways for urban development and innovation. This article introduced the background of intelligent business model, conducted academic research and summary on the keywords of SCF and the internet of things (IOT), and then summarized urban analysis by combining AI and big data. Then it put forward the business model factor analysis of SCF and modern logistics enterprises. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average transaction cost of the new business model was 3.5 lower than that of the traditional business model. With the continuous development of artificial intelligence technology and big data technology, urban planning is also facing new opportunities and challenges.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44177922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liuyang Zhao, Yezhou Sha, Kaiwen Zhang, Jiaxin Yang
Blockchain and distributed ledger technologies have attracted massive attention from both legal communities and businesses. Asset securitization is the procedure in which an issuer designs a financial instrument that is marketable by combining or merging different financial assets into one group. However, most securitization occurs with loans and other assets that generate receivables, such as consumer or business debt of various types. This article discusses the possible benefits of blockchain during the securitization process using the deep learning-based adaptive online intelligent framework (DLAOIF). The benefits can be significant, from reduced costs, time, and fraud risks to increased safety, trust, and accuracy. Tracking financial assets on a blockchain can reduce dependence on credit rating organizations and allow investors to monitor asset performance and the associated risk more carefully. It should improve investor confidence and increase secondary market interest.
{"title":"Deep Learning-Based Adaptive Online Intelligent Framework for a Blockchain Application in Risk Control of Asset Securitization","authors":"Liuyang Zhao, Yezhou Sha, Kaiwen Zhang, Jiaxin Yang","doi":"10.4018/ijdwm.323182","DOIUrl":"https://doi.org/10.4018/ijdwm.323182","url":null,"abstract":"Blockchain and distributed ledger technologies have attracted massive attention from both legal communities and businesses. Asset securitization is the procedure in which an issuer designs a financial instrument that is marketable by combining or merging different financial assets into one group. However, most securitization occurs with loans and other assets that generate receivables, such as consumer or business debt of various types. This article discusses the possible benefits of blockchain during the securitization process using the deep learning-based adaptive online intelligent framework (DLAOIF). The benefits can be significant, from reduced costs, time, and fraud risks to increased safety, trust, and accuracy. Tracking financial assets on a blockchain can reduce dependence on credit rating organizations and allow investors to monitor asset performance and the associated risk more carefully. It should improve investor confidence and increase secondary market interest.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46545889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the acceleration of the urbanization process, the traditional urban management has become increasingly unable to meet the needs of urban management and development. At the same time, with the rapid development of artificial intelligence (AI) and big data (BD), the use of AI and BD to analyze cities has been gradually emerging. Therefore, this paper used AI and BD to study the optimization method of sustainable development of smart city public management. The research showed that the respondents in N, Z, and S cities were 60.67%, 60.07%, and 60.31% satisfied with the handling of events by urban public management subjects, respectively. The experts' evaluation scores on the feasibility and effectiveness of urban public management optimization strategies were 88.79 and 92.82, respectively. The public's satisfaction with the smart city public management subject's handling of events was still not high enough. The optimization strategy for sustainable development of smart city public management proposed in this paper with BD had certain practical value.
{"title":"Optimization Method for Sustainable Development of Smart City Public Management Based on Big Data Analysis","authors":"Wen Wang, Lin Li","doi":"10.4018/ijdwm.322757","DOIUrl":"https://doi.org/10.4018/ijdwm.322757","url":null,"abstract":"With the acceleration of the urbanization process, the traditional urban management has become increasingly unable to meet the needs of urban management and development. At the same time, with the rapid development of artificial intelligence (AI) and big data (BD), the use of AI and BD to analyze cities has been gradually emerging. Therefore, this paper used AI and BD to study the optimization method of sustainable development of smart city public management. The research showed that the respondents in N, Z, and S cities were 60.67%, 60.07%, and 60.31% satisfied with the handling of events by urban public management subjects, respectively. The experts' evaluation scores on the feasibility and effectiveness of urban public management optimization strategies were 88.79 and 92.82, respectively. The public's satisfaction with the smart city public management subject's handling of events was still not high enough. The optimization strategy for sustainable development of smart city public management proposed in this paper with BD had certain practical value.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"68 1","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86772466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the innovation of information technology, digital stadiums and gymnasiums are based on independent innovation, information management, and intelligent cultivation and development, which promotes the construction of a new ecology of intelligent sports and also determines the development situation and development mode of traditional sports. In the construction of intelligent service mode of digital stadiums and gymnasiums in the context of smart cities, the management information system, as the application support of stadium information technology, is the key channel to improve the intelligent level of stadiums and gymnasiums. The establishment and promotion of a smart management information system is an inevitable trend for smart stadiums and gymnasiums to complete key technologies, consumer experience, and efficient functional innovation of management methods, which is also an important content of science and technology-enabling stadiums and gymnasiums. This paper proposed the research on intelligent service mode of digital stadiums and gymnasiums under the background of smart cities.
{"title":"Investigation of Intelligent Service Mode of Digital Stadiums and Gymnasiums in the Context of Smart Cities","authors":"W. Zhang","doi":"10.4018/ijdwm.322393","DOIUrl":"https://doi.org/10.4018/ijdwm.322393","url":null,"abstract":"With the innovation of information technology, digital stadiums and gymnasiums are based on independent innovation, information management, and intelligent cultivation and development, which promotes the construction of a new ecology of intelligent sports and also determines the development situation and development mode of traditional sports. In the construction of intelligent service mode of digital stadiums and gymnasiums in the context of smart cities, the management information system, as the application support of stadium information technology, is the key channel to improve the intelligent level of stadiums and gymnasiums. The establishment and promotion of a smart management information system is an inevitable trend for smart stadiums and gymnasiums to complete key technologies, consumer experience, and efficient functional innovation of management methods, which is also an important content of science and technology-enabling stadiums and gymnasiums. This paper proposed the research on intelligent service mode of digital stadiums and gymnasiums under the background of smart cities.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"1 1","pages":"1-14"},"PeriodicalIF":1.2,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77303367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion words. The application of syntactic dependency trees is one such resolution. However, the widely-used dependency parsers still have challenges in obtaining a solid sentiment classification result. In this work, an information propagation graph convolutional network based on syntactic structure optimization is proposed on the task of ABSA. To further complement the syntactic information, the semantic information is incorporated to learn the representations using graph information propagation mechanism. In addition, the effects of syntactic and semantic information are adapted via feature separation. Experimental results on three benchmark datasets show that the proposed model achieves satisfying performance against the state-of-the-art methods, indicating that the model can precisely build the relation between aspect and its context words.
{"title":"Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis","authors":"Weihao Huang, Shaohua Cai, Haoran Li, Qianhua Cai","doi":"10.4018/ijdwm.321107","DOIUrl":"https://doi.org/10.4018/ijdwm.321107","url":null,"abstract":"The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion words. The application of syntactic dependency trees is one such resolution. However, the widely-used dependency parsers still have challenges in obtaining a solid sentiment classification result. In this work, an information propagation graph convolutional network based on syntactic structure optimization is proposed on the task of ABSA. To further complement the syntactic information, the semantic information is incorporated to learn the representations using graph information propagation mechanism. In addition, the effects of syntactic and semantic information are adapted via feature separation. Experimental results on three benchmark datasets show that the proposed model achieves satisfying performance against the state-of-the-art methods, indicating that the model can precisely build the relation between aspect and its context words.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"47 1","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76880828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Identifying the places an infected person has visited during a virus incubation time in order to conduct contact tracing is currently done using manual interviews since proximity-based contact tracing methods do not store geolocation information due to privacy concerns. During the incubation time, an infected person might visit several locations and either forget where they went or are reluctant to disclose their trip details. To minimize manual location tracing while preserving the user's privacy, the authors propose a mesh block sequence method where the trajectories are transformed into a mesh block sequence before being shared with health authorities. These simulations show that this a useful method by which to protect user privacy by concealing specific details related to a trajectory. While this simulation uses an Australian administrative region structure, this method is applicable in countries which implement similar administrative hierarchical building blocks.
{"title":"Contact Tracing With District-Based Trajectories","authors":"Kiki Adhinugraha, W. Rahayu, Nasser Allheeib","doi":"10.4018/ijdwm.321197","DOIUrl":"https://doi.org/10.4018/ijdwm.321197","url":null,"abstract":"Identifying the places an infected person has visited during a virus incubation time in order to conduct contact tracing is currently done using manual interviews since proximity-based contact tracing methods do not store geolocation information due to privacy concerns. During the incubation time, an infected person might visit several locations and either forget where they went or are reluctant to disclose their trip details. To minimize manual location tracing while preserving the user's privacy, the authors propose a mesh block sequence method where the trajectories are transformed into a mesh block sequence before being shared with health authorities. These simulations show that this a useful method by which to protect user privacy by concealing specific details related to a trajectory. While this simulation uses an Australian administrative region structure, this method is applicable in countries which implement similar administrative hierarchical building blocks.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"95 1","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84196746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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