Pub Date : 2016-12-31DOI: 10.14257/IJDTA.2016.9.12.05
M. Singh, Gargi Srivastava, Prabhat Kumar
Internet traffic classification is one of the popular research interest area because of its benefits for many applications like intrusion detection system, congestion avoidance, traffic prediction etc. Internet traffic is classified on the basis of statistical features because port and payload based techniques have their limitations. For statistics based techniques machine learning is used. The statistical feature set is large. Hence, it is a challenge to reduce the large feature set to an optimal feature set. This will reduce the time complexity of the machine learning algorithm. This paper tries to obtain an optimal feature set by using a hybrid approach -An unsupervised clustering algorithm (K-Means) with a supervised feature selection algorithm (Best Feature Selection).
{"title":"Internet Traffic Classification Using Machine Learning","authors":"M. Singh, Gargi Srivastava, Prabhat Kumar","doi":"10.14257/IJDTA.2016.9.12.05","DOIUrl":"https://doi.org/10.14257/IJDTA.2016.9.12.05","url":null,"abstract":"Internet traffic classification is one of the popular research interest area because of its benefits for many applications like intrusion detection system, congestion avoidance, traffic prediction etc. Internet traffic is classified on the basis of statistical features because port and payload based techniques have their limitations. For statistics based techniques machine learning is used. The statistical feature set is large. Hence, it is a challenge to reduce the large feature set to an optimal feature set. This will reduce the time complexity of the machine learning algorithm. This paper tries to obtain an optimal feature set by using a hybrid approach -An unsupervised clustering algorithm (K-Means) with a supervised feature selection algorithm (Best Feature Selection).","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73760497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/ijdta.2016.9.12.27
Jia-Hong Su
The technology of large data analysis has important practical significance to players digging, tactics and training monitoring. In order to improve the performance of basketball training, the big data technology is applied in the training management system. The reform of basketball training is being carried out, and the research on the combination selection mode of the basketball training is being discussed. This is not only from the traditional technology to the combination of training to our physical education, but also from the tactical thinking to cultivate students. This can promote the performance and training interactive quality in basketball sports training and training.
{"title":"Implementation of Basketball Training Management System Based on Big Data Technology","authors":"Jia-Hong Su","doi":"10.14257/ijdta.2016.9.12.27","DOIUrl":"https://doi.org/10.14257/ijdta.2016.9.12.27","url":null,"abstract":"The technology of large data analysis has important practical significance to players digging, tactics and training monitoring. In order to improve the performance of basketball training, the big data technology is applied in the training management system. The reform of basketball training is being carried out, and the research on the combination selection mode of the basketball training is being discussed. This is not only from the traditional technology to the combination of training to our physical education, but also from the tactical thinking to cultivate students. This can promote the performance and training interactive quality in basketball sports training and training.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85865682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/IJDTA.2016.9.12.19
Wenyi Xu
In the paper, with the introduction of data mining algorithm of the classification in detail, and then combining the classification algorithm and incremental learning technology, an incremental decision tree algorithm is proposed to solve the problem of incremental learning and analysis the experimental data for this algorithm. The paper used ID3 and C4.5 algorithm for detailed research. According to two algorithms, combining Bayesian classification algorithm’s incremental learning characteristic, the paper proposed an incremental decision tree algorithm , and by the analysis of experimental data. This algorithm can solve the incremental learning problem of the decision tree algorithm very well.
{"title":"Research on an Improved Decision Tree Classification Algorithm","authors":"Wenyi Xu","doi":"10.14257/IJDTA.2016.9.12.19","DOIUrl":"https://doi.org/10.14257/IJDTA.2016.9.12.19","url":null,"abstract":"In the paper, with the introduction of data mining algorithm of the classification in detail, and then combining the classification algorithm and incremental learning technology, an incremental decision tree algorithm is proposed to solve the problem of incremental learning and analysis the experimental data for this algorithm. The paper used ID3 and C4.5 algorithm for detailed research. According to two algorithms, combining Bayesian classification algorithm’s incremental learning characteristic, the paper proposed an incremental decision tree algorithm , and by the analysis of experimental data. This algorithm can solve the incremental learning problem of the decision tree algorithm very well.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85142276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/IJDTA.2016.9.12.10
A. Maurya, M. Singh
With advancement in internet era, the importance of social media is increasing day by day. It enables users to share their profile data, ideas, videos and any content they have with them. With benefits, it also has several issues related to it. One of the issue is “how to protect users from after effect of friendship over social media?”. This paper proposes a trust model to overcome it. The proposed model calculates trust to assist end users to take decision about accepting friend-request on social media. Trust evaluation is based upon profile similarity analysis. Trust computation uses preferred attribute among profile attributes to evaluate trust of users. The paper analyzes different trust evaluation methods based on the proposed model.
{"title":"Trust Evaluation on Social Media based on Different Similarity Metrics","authors":"A. Maurya, M. Singh","doi":"10.14257/IJDTA.2016.9.12.10","DOIUrl":"https://doi.org/10.14257/IJDTA.2016.9.12.10","url":null,"abstract":"With advancement in internet era, the importance of social media is increasing day by day. It enables users to share their profile data, ideas, videos and any content they have with them. With benefits, it also has several issues related to it. One of the issue is “how to protect users from after effect of friendship over social media?”. This paper proposes a trust model to overcome it. The proposed model calculates trust to assist end users to take decision about accepting friend-request on social media. Trust evaluation is based upon profile similarity analysis. Trust computation uses preferred attribute among profile attributes to evaluate trust of users. The paper analyzes different trust evaluation methods based on the proposed model.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88420856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/IJDTA.2016.9.12.07
Kai Xu, Hong Zhen, Yan Li, L. Yue
This paper aims for the transparency and visualization of the international intermodal cargo transportation throughout its whole process, achieving a comprehensive monitoring on the multiple transportation means such as by ocean, by air, by land or by rail. Based on Internet-of-Things-based distributed data acquisition technology and the cloud-computing-based big data analysis technology, this paper gives out a Multimodal Monitoring technology that can uniformly solve the comprehensive management of multiple transportation vehicles, which includes a service functionality model, a network hierarchy model and a technology system model. By building a Generic Target Monitoring System, it proves the multimodal monitoring resolution is able to effectively monitor the multiple transportation means and provide a fair good database platform of later analysis, distribution and optimization of those vehicles.
{"title":"Big Data Acquisition and Analysis Platform for Intermodal Transport","authors":"Kai Xu, Hong Zhen, Yan Li, L. Yue","doi":"10.14257/IJDTA.2016.9.12.07","DOIUrl":"https://doi.org/10.14257/IJDTA.2016.9.12.07","url":null,"abstract":"This paper aims for the transparency and visualization of the international intermodal cargo transportation throughout its whole process, achieving a comprehensive monitoring on the multiple transportation means such as by ocean, by air, by land or by rail. Based on Internet-of-Things-based distributed data acquisition technology and the cloud-computing-based big data analysis technology, this paper gives out a Multimodal Monitoring technology that can uniformly solve the comprehensive management of multiple transportation vehicles, which includes a service functionality model, a network hierarchy model and a technology system model. By building a Generic Target Monitoring System, it proves the multimodal monitoring resolution is able to effectively monitor the multiple transportation means and provide a fair good database platform of later analysis, distribution and optimization of those vehicles.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88215066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/ijdta.2016.9.12.20
Runtao Lv, Jin Zhao, Yu Li
We extended the online learning strategy and scalable clustering technique to soft subspace clustering, and propose two online soft subspace clustering methods, OFWSC and OEWSC. The proposed evolving soft subspace clustering algorithms can not only reveal the important local subspace characteristics of high dimensional data, but also leverage on the effectiveness of online learning scheme, as well as the ability of scalable clustering methods for the large or streaming data. Furthermore, we apply our proposed algorithms to text clustering of information retrieval, gene expression data clustering, face image classification and the problem of predicting disulfide connectivity.
{"title":"Research on Spatial Clustering Algorithm based on Data Mining","authors":"Runtao Lv, Jin Zhao, Yu Li","doi":"10.14257/ijdta.2016.9.12.20","DOIUrl":"https://doi.org/10.14257/ijdta.2016.9.12.20","url":null,"abstract":"We extended the online learning strategy and scalable clustering technique to soft subspace clustering, and propose two online soft subspace clustering methods, OFWSC and OEWSC. The proposed evolving soft subspace clustering algorithms can not only reveal the important local subspace characteristics of high dimensional data, but also leverage on the effectiveness of online learning scheme, as well as the ability of scalable clustering methods for the large or streaming data. Furthermore, we apply our proposed algorithms to text clustering of information retrieval, gene expression data clustering, face image classification and the problem of predicting disulfide connectivity.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80187819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/IJDTA.2016.9.12.16
Peng Ya-mian, J. Nan, Zhang Huancheng
Parameter identification problem has important research background and research value, has become in recent years inverse problem of heat conduction of top priority. This paper studies the Parabolic Equation Inverse Problems of parameter identification problem, and applies PSO to solve research. Firstly, this paper establishes the model of the inverse problem of partial differential equations. The content and classification of the inverse problem of partial differential equations are explained. Frequently, the construction and solution of the finite difference method for parabolic equations are studied, and two stable schemes for one dimensional parabolic equation are given. And two numerical simulations were given. Partial differential equation discretization was with difference quotient instead of partial derivative. The partial differential equations with initial boundary value problem into algebraic equations, and then solving the resulting algebraic equations. Then, the basic principles of PSO and its improved algorithms are studied and compared. Particle swarm optimization algorithm program implementation. Finally, the Parabolic Equation Inverse Problems of particle swarm optimization algorithm performed three simulations. We use a set of basis functions gradually approaching the true solution, selection of initial value. The reaction is converted into direct problem question, then use difference method Solution of the direct problem. The solution of the problem with the additional conditions has being compared. The reaction optimization problem is transformed into the final particle swarm optimization algorithm to solve. Verify the Parabolic Equation Inverse Problems of particle swarm optimization algorithm correctness and applicability.
{"title":"Improved PSO Research for Solving the Inverse Problem of Parabolic Equation","authors":"Peng Ya-mian, J. Nan, Zhang Huancheng","doi":"10.14257/IJDTA.2016.9.12.16","DOIUrl":"https://doi.org/10.14257/IJDTA.2016.9.12.16","url":null,"abstract":"Parameter identification problem has important research background and research value, has become in recent years inverse problem of heat conduction of top priority. This paper studies the Parabolic Equation Inverse Problems of parameter identification problem, and applies PSO to solve research. Firstly, this paper establishes the model of the inverse problem of partial differential equations. The content and classification of the inverse problem of partial differential equations are explained. Frequently, the construction and solution of the finite difference method for parabolic equations are studied, and two stable schemes for one dimensional parabolic equation are given. And two numerical simulations were given. Partial differential equation discretization was with difference quotient instead of partial derivative. The partial differential equations with initial boundary value problem into algebraic equations, and then solving the resulting algebraic equations. Then, the basic principles of PSO and its improved algorithms are studied and compared. Particle swarm optimization algorithm program implementation. Finally, the Parabolic Equation Inverse Problems of particle swarm optimization algorithm performed three simulations. We use a set of basis functions gradually approaching the true solution, selection of initial value. The reaction is converted into direct problem question, then use difference method Solution of the direct problem. The solution of the problem with the additional conditions has being compared. The reaction optimization problem is transformed into the final particle swarm optimization algorithm to solve. Verify the Parabolic Equation Inverse Problems of particle swarm optimization algorithm correctness and applicability.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82518911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/IJDTA.2016.9.12.25
Haixin Yao, Jinmei Shao
Sports video metadata extraction system based on the content of basic goal use an automated or semi-automated interactive means to obtain video data as complete features and attributes for efficient retrieval mechanism. For fast access to video information needed, sports video ornamental create conditions. Firstly, video-based layered metadata description model, we discuss the structure of the video processing technology, and an increase in the time domain and airspace video object motion information on this basis. Low-level visual features for video and high-level semantic features presents a particular field of video information for video implicit hierarchical division method. Video automated visual feature extraction, semantic feature places marked attracted achieve human-computer interaction. Focus on the sports information descriptors and visual content descriptors, descriptor structure video. Video data based on hierarchical structure model and video features standard video content description model.
{"title":"Research on Online Sports Metadata Extraction System based on Video Processing Technology","authors":"Haixin Yao, Jinmei Shao","doi":"10.14257/IJDTA.2016.9.12.25","DOIUrl":"https://doi.org/10.14257/IJDTA.2016.9.12.25","url":null,"abstract":"Sports video metadata extraction system based on the content of basic goal use an automated or semi-automated interactive means to obtain video data as complete features and attributes for efficient retrieval mechanism. For fast access to video information needed, sports video ornamental create conditions. Firstly, video-based layered metadata description model, we discuss the structure of the video processing technology, and an increase in the time domain and airspace video object motion information on this basis. Low-level visual features for video and high-level semantic features presents a particular field of video information for video implicit hierarchical division method. Video automated visual feature extraction, semantic feature places marked attracted achieve human-computer interaction. Focus on the sports information descriptors and visual content descriptors, descriptor structure video. Video data based on hierarchical structure model and video features standard video content description model.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80195943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-31DOI: 10.14257/ijdta.2016.9.12.17
Teng Lv, Ping Yan, Weimin He
Uncertainty in data is caused by various reasons including data itself, data mapping, and data policy. For data itself, data are uncertain because of various reasons. For example, data from a sensor network, Internet of Things or Radio Frequency Identification is often inaccurate and uncertain because of devices or environmental factors. For data mapping, integrated data from various heterogonous data sources is commonly uncertain because of uncertain data mapping, data inconsistency, missing data, and dirty data. For data policy, data is modified or hided for policies of data privacy and data confidentiality in an organization. But traditional deterministic data management mainly deals with deterministic data which is precise and certain, and cannot process uncertain data. Modeling uncertain data is a foundation of other technologies for further processing data, such as indexing, querying, searching, mapping, integrating, and mining data, etc. Probabilistic data models of relational databases, XML data and graph data are widely used in many applications and areas today, such as World Wide Web, semantic web, sensor networks, Internet of Things, mobile ad-hoc networks, social networks, traffic networks, biological networks, genome databases, and medical records, etc. This paper presents a survey study of different probabilistic models of uncertain data in relational databases, XML data, and graph data, respectively. The advantages and disadvantages of each kind of probabilistic modes are analyzed and compared. Further open topics of modeling uncertain probabilistic data such as semantic and computation aspects are discussed in the paper. Criteria for modeling uncertain data, such as expressive power, complexity, efficiency, extension are also proposed in the paper.
{"title":"On Uncertain Probabilistic Data Modeling","authors":"Teng Lv, Ping Yan, Weimin He","doi":"10.14257/ijdta.2016.9.12.17","DOIUrl":"https://doi.org/10.14257/ijdta.2016.9.12.17","url":null,"abstract":"Uncertainty in data is caused by various reasons including data itself, data mapping, and data policy. For data itself, data are uncertain because of various reasons. For example, data from a sensor network, Internet of Things or Radio Frequency Identification is often inaccurate and uncertain because of devices or environmental factors. For data mapping, integrated data from various heterogonous data sources is commonly uncertain because of uncertain data mapping, data inconsistency, missing data, and dirty data. For data policy, data is modified or hided for policies of data privacy and data confidentiality in an organization. But traditional deterministic data management mainly deals with deterministic data which is precise and certain, and cannot process uncertain data. Modeling uncertain data is a foundation of other technologies for further processing data, such as indexing, querying, searching, mapping, integrating, and mining data, etc. Probabilistic data models of relational databases, XML data and graph data are widely used in many applications and areas today, such as World Wide Web, semantic web, sensor networks, Internet of Things, mobile ad-hoc networks, social networks, traffic networks, biological networks, genome databases, and medical records, etc. This paper presents a survey study of different probabilistic models of uncertain data in relational databases, XML data, and graph data, respectively. The advantages and disadvantages of each kind of probabilistic modes are analyzed and compared. Further open topics of modeling uncertain probabilistic data such as semantic and computation aspects are discussed in the paper. Criteria for modeling uncertain data, such as expressive power, complexity, efficiency, extension are also proposed in the paper.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83180407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The problem of text plagiarism has increased because of the digital resources available on the World Wide Web. Source Retrieval and Text Alignment are two core tasks of plagiarism detection. A plagiarism source retrieval and text alignment system based on relevance ranking model is described in this paper. Not only the source retrieval task but also the text alignment task is all regarded as a process of information retrieval, and the relevance ranking is used to search the plagiarism sources and obtain the candidate plagiarism seeds. For source retrieval, BM25 model is used, while for text alignment, Vector Space Model is exploited. Furthermore, a plagiarism detection system named HawkEyes is developed based on the proposed methods and some demonstrations of HawkEyes are given.
{"title":"A Method of Plagiarism Source Retrieval and Text Alignment Based on Relevance Ranking Model","authors":"Lei-lei Kong, Zicheng Zhao, Zhimao Lu, Haoliang Qi, Feng Zhao","doi":"10.14257/IJDTA.2016.9.12.04","DOIUrl":"https://doi.org/10.14257/IJDTA.2016.9.12.04","url":null,"abstract":"The problem of text plagiarism has increased because of the digital resources available on the World Wide Web. Source Retrieval and Text Alignment are two core tasks of plagiarism detection. A plagiarism source retrieval and text alignment system based on relevance ranking model is described in this paper. Not only the source retrieval task but also the text alignment task is all regarded as a process of information retrieval, and the relevance ranking is used to search the plagiarism sources and obtain the candidate plagiarism seeds. For source retrieval, BM25 model is used, while for text alignment, Vector Space Model is exploited. Furthermore, a plagiarism detection system named HawkEyes is developed based on the proposed methods and some demonstrations of HawkEyes are given.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79628710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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