A geoportal is a basic component of a spatial data infrastructure, used for searching, viewing, and downloading spatial data and services. It can be considered as a web application acting as an access point to the shared geographic information, being the place where distributed geographic data and services can be discovered. A geoportal offers the opportunity for different type of organizations to make their data and services accessible for the whole community of internet users and geoportals usability is considered a key concept. From the organizations point of view, usability is related to how the geoportal can support people to perform their tasks effectively and efficiently. From the end-users' point of view, usability concerns to how a geoportal is perceived, in a satisfying manner, to support users tasks. In this paper we present a mining approach to discover patterns related to geoportals usability evaluation. A framework is proposed based on several data sources in order to identify geoportals strengths and weaknesses affecting usability. The requirements emerging and its implications are analyzed. A geoportal evaluation case study is presented, performing web server logs analysis and System Usability Scale (SUS) questionnaire experimental data analysis applying factor analysis and association rules.
{"title":"A Mining Approach to Evaluate Geoportals Usability","authors":"Esther Hochsztain","doi":"10.1109/DMIA.2015.22","DOIUrl":"https://doi.org/10.1109/DMIA.2015.22","url":null,"abstract":"A geoportal is a basic component of a spatial data infrastructure, used for searching, viewing, and downloading spatial data and services. It can be considered as a web application acting as an access point to the shared geographic information, being the place where distributed geographic data and services can be discovered. A geoportal offers the opportunity for different type of organizations to make their data and services accessible for the whole community of internet users and geoportals usability is considered a key concept. From the organizations point of view, usability is related to how the geoportal can support people to perform their tasks effectively and efficiently. From the end-users' point of view, usability concerns to how a geoportal is perceived, in a satisfying manner, to support users tasks. In this paper we present a mining approach to discover patterns related to geoportals usability evaluation. A framework is proposed based on several data sources in order to identify geoportals strengths and weaknesses affecting usability. The requirements emerging and its implications are analyzed. A geoportal evaluation case study is presented, performing web server logs analysis and System Usability Scale (SUS) questionnaire experimental data analysis applying factor analysis and association rules.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134253118","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}
M. García-Torres, Francisco Gómez-Vela, D. Becerra-Alonso, B. Melián-Batista, Marcos Moreno-Vega
In classification tasks, as the dimensionality increases, the performance of the classifier improves until an optimal number of features is reached. Further increases of the dimensionality without increasing the number of training samples results in a degradation in classifier performance. This fact, called the curse of dimensionality, has become more relevant with the advent of larger datasets and the demands of Knowledge Discovery from Big Data. In this context, feature grouping has become an effective approach to provide additional information about relationships between features. In this work, we propose a greedy strategy, called GreedyPGG, that groups features based on the concept of Markov blankets. To such aim, we introduce the idea of predominant group of features. We also present an adaptation of the Variable Neighborhood Search (VNS) to high-dimensional feature selection that uses the GreedyPGG to reduce the search space. We test the effectiveness of the GreedyPGG on synthetic datasets and the VNS on microarray datasets. We compare VNS with popular and competitive strategies. Results show that GreedyPGG groups correlated features in an efficient way and that VNS is a competitive strategy, capable of finding a small number of features with high predictive power.
{"title":"Feature Grouping and Selection on High-Dimensional Microarray Data","authors":"M. García-Torres, Francisco Gómez-Vela, D. Becerra-Alonso, B. Melián-Batista, Marcos Moreno-Vega","doi":"10.1109/DMIA.2015.18","DOIUrl":"https://doi.org/10.1109/DMIA.2015.18","url":null,"abstract":"In classification tasks, as the dimensionality increases, the performance of the classifier improves until an optimal number of features is reached. Further increases of the dimensionality without increasing the number of training samples results in a degradation in classifier performance. This fact, called the curse of dimensionality, has become more relevant with the advent of larger datasets and the demands of Knowledge Discovery from Big Data. In this context, feature grouping has become an effective approach to provide additional information about relationships between features. In this work, we propose a greedy strategy, called GreedyPGG, that groups features based on the concept of Markov blankets. To such aim, we introduce the idea of predominant group of features. We also present an adaptation of the Variable Neighborhood Search (VNS) to high-dimensional feature selection that uses the GreedyPGG to reduce the search space. We test the effectiveness of the GreedyPGG on synthetic datasets and the VNS on microarray datasets. We compare VNS with popular and competitive strategies. Results show that GreedyPGG groups correlated features in an efficient way and that VNS is a competitive strategy, capable of finding a small number of features with high predictive power.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127674196","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 IT management project must face many challenges, including the identification of the main criteria that lead to success or failure. Project managers generate a lot of data, which is stored in different formats, but in most organizations its use is not systematized with the aim of "learning from data" and generating reusable knowledge. The objective of this article is to present a framework based on Business Intelligence techniques that contributes improving the management of IT projects.
{"title":"Application of Business Intelligence Techniques to Analyze IT Project Management Data","authors":"A. Tasistro","doi":"10.1109/DMIA.2015.15","DOIUrl":"https://doi.org/10.1109/DMIA.2015.15","url":null,"abstract":"The IT management project must face many challenges, including the identification of the main criteria that lead to success or failure. Project managers generate a lot of data, which is stored in different formats, but in most organizations its use is not systematized with the aim of \"learning from data\" and generating reusable knowledge. The objective of this article is to present a framework based on Business Intelligence techniques that contributes improving the management of IT projects.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443254","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 main body of the literature states that Artificial Neural Networks must be regarded as a "black box" without further interpretation due to the inherent difficulties for analyze the weights and bias terms. Some authors claim that ANN trained as a regression device tend to organize itself by specializing some neurons to learn the main relationships embedded in the training set, while other neurons are more concerned with the noise. We suggest here a rule to identify the "noise-related" neurons in multilayer perceptron ANN, and we assume that those neurons are activated only when some unusual values (or combination of values) are present. We consider those events as candidates to hold an outlier. The use of the ANN as outlier detector does not require further training, and can be easily applied.
{"title":"Investigating the Role of Individual Neurons as Outlier Detectors","authors":"C. López-Vázquez","doi":"10.1109/DMIA.2015.11","DOIUrl":"https://doi.org/10.1109/DMIA.2015.11","url":null,"abstract":"The main body of the literature states that Artificial Neural Networks must be regarded as a \"black box\" without further interpretation due to the inherent difficulties for analyze the weights and bias terms. Some authors claim that ANN trained as a regression device tend to organize itself by specializing some neurons to learn the main relationships embedded in the training set, while other neurons are more concerned with the noise. We suggest here a rule to identify the \"noise-related\" neurons in multilayer perceptron ANN, and we assume that those neurons are activated only when some unusual values (or combination of values) are present. We consider those events as candidates to hold an outlier. The use of the ANN as outlier detector does not require further training, and can be easily applied.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134486138","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}
Rafael Arias-Michel, M. García-Torres, C. Schaerer, F. Divina
Feature selection has become an important research area in machine learning due to rapid advances in technology. In high-dimensional spaces, the difficulty of classification is intrinsically caused by the existence of irrelevant and redundant features that, in general, degrade the performance of a classifier. Moreover, finding the optimal subset of features becomes intractable even for low-dimensional datasets. In this context, Markov blanket discovery can be used to identify such subset. The approximated Markov blanket (AMb) is an efficient and effective approach to induce Markov blankets from data. However, this approach only considers pairwise comparisons of features. In this paper, we redefine the AMb to consider the interaction among features of a given subset of features. We use the Correlation based Feature Selection (CFS) function to measure such interactions and, as search strategy, the Fast Correlation based Filter (FCBF). The proposal, denoted as FCBFCFS, is compared with the FCBF and tested on synthetic and real-world datasets from the microarray domain. Results show that the inclusion of interactions among features in a subset may led to smaller subsets of features without degrading the classification task.
{"title":"Feature Selection via Approximated Markov Blankets Using the CFS Method","authors":"Rafael Arias-Michel, M. García-Torres, C. Schaerer, F. Divina","doi":"10.1109/DMIA.2015.17","DOIUrl":"https://doi.org/10.1109/DMIA.2015.17","url":null,"abstract":"Feature selection has become an important research area in machine learning due to rapid advances in technology. In high-dimensional spaces, the difficulty of classification is intrinsically caused by the existence of irrelevant and redundant features that, in general, degrade the performance of a classifier. Moreover, finding the optimal subset of features becomes intractable even for low-dimensional datasets. In this context, Markov blanket discovery can be used to identify such subset. The approximated Markov blanket (AMb) is an efficient and effective approach to induce Markov blankets from data. However, this approach only considers pairwise comparisons of features. In this paper, we redefine the AMb to consider the interaction among features of a given subset of features. We use the Correlation based Feature Selection (CFS) function to measure such interactions and, as search strategy, the Fast Correlation based Filter (FCBF). The proposal, denoted as FCBFCFS, is compared with the FCBF and tested on synthetic and real-world datasets from the microarray domain. Results show that the inclusion of interactions among features in a subset may led to smaller subsets of features without degrading the classification task.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016513","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}
M. Diván, Yanina Bellini Saibene, Maria de los Ángeles Martín, María Laura Belmonte, Guillermo Lafuente, J. Caldera
The Weather Radar (WR) of the Experimental Agricultural Station (EAS) INTA Anguil produces daily a volume of 17GB of data, which represents about 6.2 Tb annually. The use of such data when they are generated, as well as its subsequent management, use and the possibility of providing services to the public represent a challenge in terms of volume and complexity. The Strategy for Data Stream Processing based on Measurement Metadata (SDSPbMM) is a data stream manager sustained in a measurement and evaluation framework, which incorporates detective and predictive behavior, through the use of measurements and associated metadata. This paper proposes a processing architecture that extends the SDSPbMM to incorporate the processing of big data. This would provide the WR of a detective and predictive behavior on online data, as well as include a layer of public services, which encourages the consumption of data generated by the WR of INTA Anguil.
{"title":"Towards a Data Processing Architecture for the Weather Radar of the INTA Anguil","authors":"M. Diván, Yanina Bellini Saibene, Maria de los Ángeles Martín, María Laura Belmonte, Guillermo Lafuente, J. Caldera","doi":"10.1109/DMIA.2015.12","DOIUrl":"https://doi.org/10.1109/DMIA.2015.12","url":null,"abstract":"The Weather Radar (WR) of the Experimental Agricultural Station (EAS) INTA Anguil produces daily a volume of 17GB of data, which represents about 6.2 Tb annually. The use of such data when they are generated, as well as its subsequent management, use and the possibility of providing services to the public represent a challenge in terms of volume and complexity. The Strategy for Data Stream Processing based on Measurement Metadata (SDSPbMM) is a data stream manager sustained in a measurement and evaluation framework, which incorporates detective and predictive behavior, through the use of measurements and associated metadata. This paper proposes a processing architecture that extends the SDSPbMM to incorporate the processing of big data. This would provide the WR of a detective and predictive behavior on online data, as well as include a layer of public services, which encourages the consumption of data generated by the WR of INTA Anguil.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116621859","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}
Most data mining, business intelligence and data warehousing university courses are focused in techniques and modeling. But they fail in teaching business understanding, prototyping and how to involve business users from the first stages of a business intelligence project. In this paper we review research related to business intelligence teaching identifying strengths and weaknesses of most common ways of teaching and learning. Following we describe our experience teaching business intelligence in different areas. We continue proposing a method to test the project business applications and the return of investment study.
{"title":"Teaching an Learning Business Intelligence: Business Evaluation Last but Not Least","authors":"Esther Hochsztain, A. Tasistro","doi":"10.1109/DMIA.2015.19","DOIUrl":"https://doi.org/10.1109/DMIA.2015.19","url":null,"abstract":"Most data mining, business intelligence and data warehousing university courses are focused in techniques and modeling. But they fail in teaching business understanding, prototyping and how to involve business users from the first stages of a business intelligence project. In this paper we review research related to business intelligence teaching identifying strengths and weaknesses of most common ways of teaching and learning. Following we describe our experience teaching business intelligence in different areas. We continue proposing a method to test the project business applications and the return of investment study.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116549053","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}
Data election to build a representative model able to explain socio-economic phenomena is a challenge within the model construction stage itself. Knowing what data to include within the studies and what to discard is a challenge, and again, at the same time, a great amount of possible factors affecting each variable behavior must be found. In complex phenomena, the number of factors affecting a variable is enormous, and isolating a variable can become a hopeless effort. Besides, there are also factors that are difficultly observable or inherently not observable that must be considered, those ones known as errors or perturbations in a relation that have influence in the constructed model outputs. Techniques applied in data mining can give support to the studies in the moment of analyzing the socio-economic phenomena and demonstrate results obtained through a scientific and reliable way. Data mining is proposed as a valid option in the study of indicators contrasting the traditional methodology (econometrics). An experiment was conducted to contrast two cultures in the use of statistical modeling. One assumes that the data are generated by stochastic GIVEN data model (Data Modeling Culture). The other one uses algorithmic models and treats the data as unknown mechanism (Algorithmic Modeling Culture).
{"title":"Predictive Models of Economic Systems Based on Data Mining","authors":"J. Cazal","doi":"10.1109/DMIA.2015.20","DOIUrl":"https://doi.org/10.1109/DMIA.2015.20","url":null,"abstract":"Data election to build a representative model able to explain socio-economic phenomena is a challenge within the model construction stage itself. Knowing what data to include within the studies and what to discard is a challenge, and again, at the same time, a great amount of possible factors affecting each variable behavior must be found. In complex phenomena, the number of factors affecting a variable is enormous, and isolating a variable can become a hopeless effort. Besides, there are also factors that are difficultly observable or inherently not observable that must be considered, those ones known as errors or perturbations in a relation that have influence in the constructed model outputs. Techniques applied in data mining can give support to the studies in the moment of analyzing the socio-economic phenomena and demonstrate results obtained through a scientific and reliable way. Data mining is proposed as a valid option in the study of indicators contrasting the traditional methodology (econometrics). An experiment was conducted to contrast two cultures in the use of statistical modeling. One assumes that the data are generated by stochastic GIVEN data model (Data Modeling Culture). The other one uses algorithmic models and treats the data as unknown mechanism (Algorithmic Modeling Culture).","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133601760","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}
Creative entrepreneurship is considered an important factor in economic development achievement, specially in the knowledge-based society. Universities play a fundamental role in the process of entrepreneurial development and the entrepreneurship ecosystem.CCEEmprende is a program to support entrepreneurs developed by Facultad de Ciencias Económicas y de Administración - Universidad de la República, Uruguay. In this paper we present the use of data mining to improve decision making in entrepreneurship management, based on CCEEmprende projects data. A case study using several data mining and statistical techniques (association rules, decision trees, logistic regression) is developed with two goals: anticipating project success and identifying the most important factors related to project success/failure.
创造性创业被认为是经济发展成就的重要因素，特别是在知识型社会。大学在创业发展过程和创业生态系统中起着基础性作用。CCEEmprende是由乌拉圭科学学院Económicas y de Administración - República大学开发的一个支持企业家的项目。本文以CCEEmprende项目数据为基础，介绍了数据挖掘在创业管理决策中的应用。使用几种数据挖掘和统计技术(关联规则、决策树、逻辑回归)的案例研究有两个目标:预测项目成功和确定与项目成功/失败相关的最重要因素。
{"title":"Data Mining Applications in Entrepreneurship Analysis","authors":"Esther Hochsztain, A. Tasistro, M. Messina","doi":"10.1109/DMIA.2015.21","DOIUrl":"https://doi.org/10.1109/DMIA.2015.21","url":null,"abstract":"Creative entrepreneurship is considered an important factor in economic development achievement, specially in the knowledge-based society. Universities play a fundamental role in the process of entrepreneurial development and the entrepreneurship ecosystem.CCEEmprende is a program to support entrepreneurs developed by Facultad de Ciencias Económicas y de Administración - Universidad de la República, Uruguay. In this paper we present the use of data mining to improve decision making in entrepreneurship management, based on CCEEmprende projects data. A case study using several data mining and statistical techniques (association rules, decision trees, logistic regression) is developed with two goals: anticipating project success and identifying the most important factors related to project success/failure.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261911","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}
In this work we proposes an Integrated Strategy based in Processes, Requirements, Measurement and Evaluation, whose objective is to identify and maintain a traceability of such requirements at early stages in data warehouse projects. Our strategy starts with the process formalization using SPEM to improve its communicability and extensibility. From the process formalization, we continue with the definition of the measurement and evaluation (M&E) project to quantify the behavior of each process and its necessities. This enables progress in the project scoping, the early identification of its risks, and at the same time establishes a traceability mechanism between the decisions and the artifacts that may generate throughout its life cycle. This represents an important compliment regarding life cycles as proposed by Kimball, in which the requirement phase is not formalized and there is no strategy to clearly define the aspects to quantify and/or analyze to the effects of supporting a decision making process. Finally, an example of the strategy application for one process of the Ministry of Education of Ecuador is shown.
{"title":"An Integrated Strategy Based in Processes, Requirements, Measurement and Evaluation for the Formalization of Necessities in Data Warehouse Projects","authors":"Avalos Veronica Nathali, Diván Mario José","doi":"10.1109/DMIA.2015.13","DOIUrl":"https://doi.org/10.1109/DMIA.2015.13","url":null,"abstract":"In this work we proposes an Integrated Strategy based in Processes, Requirements, Measurement and Evaluation, whose objective is to identify and maintain a traceability of such requirements at early stages in data warehouse projects. Our strategy starts with the process formalization using SPEM to improve its communicability and extensibility. From the process formalization, we continue with the definition of the measurement and evaluation (M&E) project to quantify the behavior of each process and its necessities. This enables progress in the project scoping, the early identification of its risks, and at the same time establishes a traceability mechanism between the decisions and the artifacts that may generate throughout its life cycle. This represents an important compliment regarding life cycles as proposed by Kimball, in which the requirement phase is not formalized and there is no strategy to clearly define the aspects to quantify and/or analyze to the effects of supporting a decision making process. Finally, an example of the strategy application for one process of the Ministry of Education of Ecuador is shown.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133712530","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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