Online marketplaces such as Amazon allow people to share their experiences about purchased products using textual comments known as product reviews. These reviews have become a common tool that users rely on to get insights on the quality and functionality of products and services from online consumers. However, like any other online information, reviewers raise serious questions concerning the credibility and reliability, since anyone can post reviews, which might impact the reliability of the information. This paper tackles the phenomenon of Bulk reviewers. We first analyze a large dataset of reviews from Amazon aiming to spot bulk reviewers according to their behavior. We then apply a what-if analysis to assess the effect of bulk reviews on the online marketplaces using a metric called Net Promoter Score to measure the willingness of users to recommend products. Our Results reveal that bulk users (i.e., users that review multiple times) have same distribution of ratings as non-bulk users indicating that a bulk reviewer is not automatically a fake reviewer. Yet, we discover that bulk users do inflate NPS metric and thus contribute to overestimate the level of customer satisfaction.
{"title":"Towards Amazon Fake Reviewers Detection: The Effect of Bulk Users","authors":"Youssef Esseddiq Ouatiti, Noureddine Kerzazi","doi":"10.1145/3419604.3419800","DOIUrl":"https://doi.org/10.1145/3419604.3419800","url":null,"abstract":"Online marketplaces such as Amazon allow people to share their experiences about purchased products using textual comments known as product reviews. These reviews have become a common tool that users rely on to get insights on the quality and functionality of products and services from online consumers. However, like any other online information, reviewers raise serious questions concerning the credibility and reliability, since anyone can post reviews, which might impact the reliability of the information. This paper tackles the phenomenon of Bulk reviewers. We first analyze a large dataset of reviews from Amazon aiming to spot bulk reviewers according to their behavior. We then apply a what-if analysis to assess the effect of bulk reviews on the online marketplaces using a metric called Net Promoter Score to measure the willingness of users to recommend products. Our Results reveal that bulk users (i.e., users that review multiple times) have same distribution of ratings as non-bulk users indicating that a bulk reviewer is not automatically a fake reviewer. Yet, we discover that bulk users do inflate NPS metric and thus contribute to overestimate the level of customer satisfaction.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125289674","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 ubiquity and the fast growth of online resources has made it more and more difficult to try to respect the differences between learners in terms of cognitive ability and knowledge structure. This is even clearer with recommendation algorithms that use traditional collaborative filtering as they struggle through identifying more helpful, user friendly and easy learning resources. On top of that, the incoherent recommended content and the compound and nonlinear data on online learning users cannot be effectively handled, thus making the recommendations less efficient. To increase the level of efficiency of learning resource recommendations, this paper introduces a two steps efficient resource recommendation model. this model is based on unsupervised deep learning machine to identify learning styles and users' clusters, and a sentiment analyzer bonus system, based on user experience, to improve or decrease recommender items list classification. The model integrates also teachers to incite them to enhance the quality and the success rate of appropriate selected items. The elaboration of such a model requires the use of a considerable quantity of data learners' features, course content and assessment attributes. Furthermore, this model needs to incorporate learner interactions features. These are the requirements to build Learner features vector as input for the first step and Learner-Content ratings vector to choose the more efficient learning resource to recommend.
{"title":"Recommender E-Learning platform using sentiment analysis aggregation","authors":"Jamal Mawane, A. Naji, M. Ramdani","doi":"10.1145/3419604.3419784","DOIUrl":"https://doi.org/10.1145/3419604.3419784","url":null,"abstract":"The ubiquity and the fast growth of online resources has made it more and more difficult to try to respect the differences between learners in terms of cognitive ability and knowledge structure. This is even clearer with recommendation algorithms that use traditional collaborative filtering as they struggle through identifying more helpful, user friendly and easy learning resources. On top of that, the incoherent recommended content and the compound and nonlinear data on online learning users cannot be effectively handled, thus making the recommendations less efficient. To increase the level of efficiency of learning resource recommendations, this paper introduces a two steps efficient resource recommendation model. this model is based on unsupervised deep learning machine to identify learning styles and users' clusters, and a sentiment analyzer bonus system, based on user experience, to improve or decrease recommender items list classification. The model integrates also teachers to incite them to enhance the quality and the success rate of appropriate selected items. The elaboration of such a model requires the use of a considerable quantity of data learners' features, course content and assessment attributes. Furthermore, this model needs to incorporate learner interactions features. These are the requirements to build Learner features vector as input for the first step and Learner-Content ratings vector to choose the more efficient learning resource to recommend.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123427043","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}
Jallal Talaghzi, Abdellah Bennane, M. Himmi, M. Bellafkih, Aziza Benomar
In recent years, most e-learning platforms include tools that adapt learning materials to learners in order to offer them personalized learning content. Researchers around the world have worked on this topic to find solutions that help teachers to create pedagogical content and learning object that are tailored to each learner's skills, abilities, and preferences. The purpose of this study is to review the literature of works and publications on adaptive learning in E-learning platforms. More specifically, we have dealt with a set of questions relating to the adapted object, the adaptation criteria, the adaptation parameters and the adaptation methods / algorithms in online learning platforms. moreover, this study will allow us to statistically define the promising research areas in online adaptive learning and to present a vision on the use of adaptation criteria.
{"title":"Online Adaptive Learning: A Review of Literature","authors":"Jallal Talaghzi, Abdellah Bennane, M. Himmi, M. Bellafkih, Aziza Benomar","doi":"10.1145/3419604.3419759","DOIUrl":"https://doi.org/10.1145/3419604.3419759","url":null,"abstract":"In recent years, most e-learning platforms include tools that adapt learning materials to learners in order to offer them personalized learning content. Researchers around the world have worked on this topic to find solutions that help teachers to create pedagogical content and learning object that are tailored to each learner's skills, abilities, and preferences. The purpose of this study is to review the literature of works and publications on adaptive learning in E-learning platforms. More specifically, we have dealt with a set of questions relating to the adapted object, the adaptation criteria, the adaptation parameters and the adaptation methods / algorithms in online learning platforms. moreover, this study will allow us to statistically define the promising research areas in online adaptive learning and to present a vision on the use of adaptation criteria.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122997868","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 the latest years, service selection is becoming more and more important due to the significant effect of internet based services in the telecom industry. When it comes to selecting the best service, different candidate services with similar settings are proposed by different service providers. The selection should take into consideration the respect of the constraints of consumers in terms of Service Level Agreement contracts, what makes the modelling of the preferences of decision-makers for choice problems the main focus of this work. In order to model these preferences, we propose contextual preference functions based on machine learning techniques from neural networks. It will therefore be possible to further explain and decode preferences in order to facilitate negotiation and thus decision-making, thereby improving the quality of service providers while being on customer preferences.
{"title":"Neural network for learning and analyzing preferences for Multi-Criteria services","authors":"Imane Haddar, B. Raouyane, M. Bellafkih","doi":"10.1145/3419604.3419799","DOIUrl":"https://doi.org/10.1145/3419604.3419799","url":null,"abstract":"In the latest years, service selection is becoming more and more important due to the significant effect of internet based services in the telecom industry. When it comes to selecting the best service, different candidate services with similar settings are proposed by different service providers. The selection should take into consideration the respect of the constraints of consumers in terms of Service Level Agreement contracts, what makes the modelling of the preferences of decision-makers for choice problems the main focus of this work. In order to model these preferences, we propose contextual preference functions based on machine learning techniques from neural networks. It will therefore be possible to further explain and decode preferences in order to facilitate negotiation and thus decision-making, thereby improving the quality of service providers while being on customer preferences.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123026495","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}
Sidna Jeddou, Amine Baïna, Abdallah Najid, H. E. Alami
The communication protocols are an essential part for the data communication of Internet of Things (IoT) applications. However, the selection of a communication protocol is challenging because it depends on the nature of the IoT system and its data transmission system. Copious communications protocols have been developed and employed by researchers based on their requirements in the last decade. Though, none of them is able to support all criteria requirements, like energy efficiency, security, quality of service, etc. Of all types of IoT systems, communication protocols are an ongoing dilemma for the IoT industry; consequently, it is important to analyze the comportments and mechanisms of this latter to determine their best-fit scenarios. Therefore, this paper presents an evaluation of established communication protocols HTTP, MQTT, DDS, XMPP, AMQP and CoAP for IoT applications. Firstly, it presents the broad comparison among these communication protocols to introduce their characteristics comparatively. Subsequently, it performs a detailed and in-depth analysis of the related process of gaining an understanding of their strengths and limitations. Therefore, based on this detailed evaluation, the user can determine their appropriate use for various IoT applications depending on their needs, efficiency, and suitability.
{"title":"Analysis and evaluation of communication Protocols for IoT Applications","authors":"Sidna Jeddou, Amine Baïna, Abdallah Najid, H. E. Alami","doi":"10.1145/3419604.3419754","DOIUrl":"https://doi.org/10.1145/3419604.3419754","url":null,"abstract":"The communication protocols are an essential part for the data communication of Internet of Things (IoT) applications. However, the selection of a communication protocol is challenging because it depends on the nature of the IoT system and its data transmission system. Copious communications protocols have been developed and employed by researchers based on their requirements in the last decade. Though, none of them is able to support all criteria requirements, like energy efficiency, security, quality of service, etc. Of all types of IoT systems, communication protocols are an ongoing dilemma for the IoT industry; consequently, it is important to analyze the comportments and mechanisms of this latter to determine their best-fit scenarios. Therefore, this paper presents an evaluation of established communication protocols HTTP, MQTT, DDS, XMPP, AMQP and CoAP for IoT applications. Firstly, it presents the broad comparison among these communication protocols to introduce their characteristics comparatively. Subsequently, it performs a detailed and in-depth analysis of the related process of gaining an understanding of their strengths and limitations. Therefore, based on this detailed evaluation, the user can determine their appropriate use for various IoT applications depending on their needs, efficiency, and suitability.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134633153","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}
As with any business, the bankruptcy of a bank manifests itself in the cessation of payments. At this point, the bank must be closed and put into liquidation. Such a situation would cause considerable prejudice to the customers and to to the economy as a whole. Hence, over the years there has been many works in the literature in regards to bankruptcy prediction. However research exploring deep learning for this problem are scares. This paper presents an empirical study of banks failures prediction, by proposing the use of deep learning in comparison with traditional methods. Using a sample of 1100 of FDIC-insured US commercial banks, over a 14-year period from 2004 to 2018, we extracted and constructed 40 performance ratios known to have an influence on banks performance and forecasting bankruptcy. We then investigated the efficiency of prediction techniques already used in the literature and the performance of a Deep Autoencoder in relation to these methods. Experimental results prove that our proposed model, based on a deep neural network, outperforms the typical statistical and machine learning methods, in terms of the Matthews Correlation Coefficient and F1Score.
{"title":"Bank Failure Prediction: A Deep Learning Approach","authors":"Youness Abakarim, M. Lahby, Abdelbaki Attioui","doi":"10.1145/3419604.3419792","DOIUrl":"https://doi.org/10.1145/3419604.3419792","url":null,"abstract":"As with any business, the bankruptcy of a bank manifests itself in the cessation of payments. At this point, the bank must be closed and put into liquidation. Such a situation would cause considerable prejudice to the customers and to to the economy as a whole. Hence, over the years there has been many works in the literature in regards to bankruptcy prediction. However research exploring deep learning for this problem are scares. This paper presents an empirical study of banks failures prediction, by proposing the use of deep learning in comparison with traditional methods. Using a sample of 1100 of FDIC-insured US commercial banks, over a 14-year period from 2004 to 2018, we extracted and constructed 40 performance ratios known to have an influence on banks performance and forecasting bankruptcy. We then investigated the efficiency of prediction techniques already used in the literature and the performance of a Deep Autoencoder in relation to these methods. Experimental results prove that our proposed model, based on a deep neural network, outperforms the typical statistical and machine learning methods, in terms of the Matthews Correlation Coefficient and F1Score.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126635044","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}
With the rapid progress of online learning technology, e-learning environments offer an increasing number of learning resources and platforms. One standing problem of e-learning is delivering the same learning for all learners within a particular course without taking into consideration their individual learning needs. Ideally, the content must be adjusted to fit the individual learner's learning characteristics. To this end, this study proposes a framework for adaptive learning that can align course content with each learner's learning style. Given that the important rule of placement tests for assessing prior knowledge, we propose an automatic prediction learning style at the beginning of online learning session. To overcome the cold start issue, our placement test aims: (1) to principally evaluate students' pre-requisites and to (2) implicitly provide an extensive knowledge about their predominant learning style at the beginning of training. To do that, we used two methods: a placement test based on item response theory (IRT) and a rule-based method. To classify learners according to their learning style, we adopted The Felder-Silverman learning style model (FSLM) as the basis of classification for our proposed system.
{"title":"Automatic Prediction of Learning Style Based On Prior Knowledge Using IRT and FSLM","authors":"Samia Rami, S. Bennani, Mohammed Khalidi","doi":"10.1145/3419604.3419767","DOIUrl":"https://doi.org/10.1145/3419604.3419767","url":null,"abstract":"With the rapid progress of online learning technology, e-learning environments offer an increasing number of learning resources and platforms. One standing problem of e-learning is delivering the same learning for all learners within a particular course without taking into consideration their individual learning needs. Ideally, the content must be adjusted to fit the individual learner's learning characteristics. To this end, this study proposes a framework for adaptive learning that can align course content with each learner's learning style. Given that the important rule of placement tests for assessing prior knowledge, we propose an automatic prediction learning style at the beginning of online learning session. To overcome the cold start issue, our placement test aims: (1) to principally evaluate students' pre-requisites and to (2) implicitly provide an extensive knowledge about their predominant learning style at the beginning of training. To do that, we used two methods: a placement test based on item response theory (IRT) and a rule-based method. To classify learners according to their learning style, we adopted The Felder-Silverman learning style model (FSLM) as the basis of classification for our proposed system.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122137048","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}
Wireless Body Area Networks (WBANs) are composed of a set of sensors and devices, that collect, store, process, and send physiological information. The collected data can be used to monitor elderly people, the activity of an athlete and many other benefits. However, physiological, and medical information are classified as private data, thus, WBANs applications must implement security models to protect the collected information during collection, transmission, and storage procedures. Indeed, several security requirements must be addressed in WBANs. This paper aims to present the architecture of WBANs, review the security requirements, and discuss the type of attacks and threats that can influence the performance and efficiency of WBANs.
{"title":"Security Challenges of Wireless Body Area Networks: Threats and Solution","authors":"Khalil Boukri, N. Naja, E. Belmekki","doi":"10.1145/3419604.3419774","DOIUrl":"https://doi.org/10.1145/3419604.3419774","url":null,"abstract":"Wireless Body Area Networks (WBANs) are composed of a set of sensors and devices, that collect, store, process, and send physiological information. The collected data can be used to monitor elderly people, the activity of an athlete and many other benefits. However, physiological, and medical information are classified as private data, thus, WBANs applications must implement security models to protect the collected information during collection, transmission, and storage procedures. Indeed, several security requirements must be addressed in WBANs. This paper aims to present the architecture of WBANs, review the security requirements, and discuss the type of attacks and threats that can influence the performance and efficiency of WBANs.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132585991","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}
Regularized Class Association Rules (RCAR) is an algorithm which produces rules based classifier in a categorical data space. The main goal of RCAR algorithm is to build classifiers which are as accurate as the state of the art algorithms, while improving the interpretability and allowing end-users to maintain and understand its outcome easily and without statistical modeling background. In this work, first, we introduce the RCAR framework, second, we provide the main functions which extract Class Association Rules (CARs), prune irrelevant rules, and rank the conserved CARs according to a set of weights calculated for each CAR. The RCAR framework also consists of multiple visualization techniques that traces the steps of the model building according to its parameters, which facilitates the model elaboration and tuning parameter for simple users. Eventually, we implemented the RCAR algorithm in the RCAR's R package.
{"title":"RCAR Framework: Building a Regularized Class Association Rules Model in a Categorical Data Space","authors":"Mohamed Azmi, A. Berrado","doi":"10.1145/3419604.3419762","DOIUrl":"https://doi.org/10.1145/3419604.3419762","url":null,"abstract":"Regularized Class Association Rules (RCAR) is an algorithm which produces rules based classifier in a categorical data space. The main goal of RCAR algorithm is to build classifiers which are as accurate as the state of the art algorithms, while improving the interpretability and allowing end-users to maintain and understand its outcome easily and without statistical modeling background. In this work, first, we introduce the RCAR framework, second, we provide the main functions which extract Class Association Rules (CARs), prune irrelevant rules, and rank the conserved CARs according to a set of weights calculated for each CAR. The RCAR framework also consists of multiple visualization techniques that traces the steps of the model building according to its parameters, which facilitates the model elaboration and tuning parameter for simple users. Eventually, we implemented the RCAR algorithm in the RCAR's R package.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132663622","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 recent years, as more and more data sources have become available and the volumes of data potentially accessible have increased, the assessment of data quality has taken a central role whether at the academic, professional or any other sector. Given that users are often concerned with the need to filter a large amount of data to better satisfy their requirements and needs, and that data analysis can be based on inaccurate, incomplete, ambiguous, duplicated and of poor quality, it makes everyone wonder what the results of these analyses will really be like. However, there is a very complex process involved in the identification of new, valid, potentially useful and meaningful data from a large data collection and various information systems, and is critically dependent on a number of measures to be developed to ensure data quality. To this end, the main objective of this paper is to introduce a general study on data quality related with big data, by providing what other researchers came up with on that subject. The paper will be finalized by a comparative study between the different existing data quality models.
{"title":"Towards a Data Quality Assessment in Big Data","authors":"Oumaima Reda, Imad Sassi, A. Zellou, S. Anter","doi":"10.1145/3419604.3419803","DOIUrl":"https://doi.org/10.1145/3419604.3419803","url":null,"abstract":"In recent years, as more and more data sources have become available and the volumes of data potentially accessible have increased, the assessment of data quality has taken a central role whether at the academic, professional or any other sector. Given that users are often concerned with the need to filter a large amount of data to better satisfy their requirements and needs, and that data analysis can be based on inaccurate, incomplete, ambiguous, duplicated and of poor quality, it makes everyone wonder what the results of these analyses will really be like. However, there is a very complex process involved in the identification of new, valid, potentially useful and meaningful data from a large data collection and various information systems, and is critically dependent on a number of measures to be developed to ensure data quality. To this end, the main objective of this paper is to introduce a general study on data quality related with big data, by providing what other researchers came up with on that subject. The paper will be finalized by a comparative study between the different existing data quality models.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127623826","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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