Big data view materialization enhances the performance of Big data queries. This is a complex problem due to large volume, heterogeneity, high rate of data generation, low integrity and low value of Big data. Big data view materialization is a bi-objective optimization problem with the objectives - minimization of query evaluation time for a set of workload queries over a window of time and minimization of update processing cost of the views. Structure of Big data views can be represented as directed graph, which can be used to identify the candidate Big data views for a given set of queries. Evolutionary algorithms can be used to solve the problem of Big data view materialization. This paper presents an algorithm based on Strength Pareto Evolutionary Algorithm (SPEA-2) to generate a set of optimal solutions to the bi-objective Big data view selection problem.
{"title":"Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary Algorithm","authors":"Akshay Kumar, T. Kumar","doi":"10.4018/jitr.299947","DOIUrl":"https://doi.org/10.4018/jitr.299947","url":null,"abstract":"Big data view materialization enhances the performance of Big data queries. This is a complex problem due to large volume, heterogeneity, high rate of data generation, low integrity and low value of Big data. Big data view materialization is a bi-objective optimization problem with the objectives - minimization of query evaluation time for a set of workload queries over a window of time and minimization of update processing cost of the views. Structure of Big data views can be represented as directed graph, which can be used to identify the candidate Big data views for a given set of queries. Evolutionary algorithms can be used to solve the problem of Big data view materialization. This paper presents an algorithm based on Strength Pareto Evolutionary Algorithm (SPEA-2) to generate a set of optimal solutions to the bi-objective Big data view selection problem.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124964904","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}
Aeronautical information service (AIS) involves manifold correlations among aeronautical events. The data mining technology has been used to extract the characteristics of aeronautical information. With the aeronautical dynamic information of the notice to airmen (NOTAM) as the study case, this paper carries out semantic analysis on NOTAMs, and establishes a spatio-temporal resource description framework (RDF) schema model by combining a three-tuple RDF model and semantic analysis to extract features of aeronautical information. The new model is constructed by Protégé and NOTAM texts are employed to verify the model. Experiments showed that our proposed model could effectively match the samples of NOTAM information and extract the characteristic data from the NOTAM information. The study is expected to provide a basis for further aeronautical information mining based on knowledge graph.
{"title":"A Spatio-Temporal Resource Description Framework Schema Model for Aeronautical Dynamic Information Based on Semantic Analysis","authors":"Xin Lai, Jiwei Zeng, Yi Dai, Shuai Han","doi":"10.4018/jitr.299386","DOIUrl":"https://doi.org/10.4018/jitr.299386","url":null,"abstract":"Aeronautical information service (AIS) involves manifold correlations among aeronautical events. The data mining technology has been used to extract the characteristics of aeronautical information. With the aeronautical dynamic information of the notice to airmen (NOTAM) as the study case, this paper carries out semantic analysis on NOTAMs, and establishes a spatio-temporal resource description framework (RDF) schema model by combining a three-tuple RDF model and semantic analysis to extract features of aeronautical information. The new model is constructed by Protégé and NOTAM texts are employed to verify the model. Experiments showed that our proposed model could effectively match the samples of NOTAM information and extract the characteristic data from the NOTAM information. The study is expected to provide a basis for further aeronautical information mining based on knowledge graph.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126733173","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}
Statistical outlier detection techniques uses academic performance oriented results to find the truly brilliant as well as the weakest amongst a colony of students. Machine Learning allows further partitions within the remaining student community, based on both merit and personality. Present work proposes a decision tree model for predicting three more appropriate categories. It utilizes Text Analytic tools to assess student characteristic traits from their textual responses and feedbacks. The cream of the general pool is chosen to belong to a top class comprising the mentor group, provided they can academically assist the weaker of the lot. But all on the top may not be suited for mentor-ship role - textual assessment data delves to reveal character orientations favouring such decisions. The bulk who can manage their own forms the second class. The bottom of the pool benefits with assistance from the mentor group and comprise the third class.
{"title":"Machine Learning Tool to Predict Student Categories After Outlier Removal","authors":"Anindita Desarkar, Ajanta Das, C. Chaudhuri","doi":"10.4018/jitr.299380","DOIUrl":"https://doi.org/10.4018/jitr.299380","url":null,"abstract":"Statistical outlier detection techniques uses academic performance oriented results to find the truly brilliant as well as the weakest amongst a colony of students. Machine Learning allows further partitions within the remaining student community, based on both merit and personality. Present work proposes a decision tree model for predicting three more appropriate categories. It utilizes Text Analytic tools to assess student characteristic traits from their textual responses and feedbacks. The cream of the general pool is chosen to belong to a top class comprising the mentor group, provided they can academically assist the weaker of the lot. But all on the top may not be suited for mentor-ship role - textual assessment data delves to reveal character orientations favouring such decisions. The bulk who can manage their own forms the second class. The bottom of the pool benefits with assistance from the mentor group and comprise the third class.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126741081","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}
Intelligent manufacturing is an important method for transforming and upgrading enterprise intelligence. Studying the influencing factors of enterprise intelligent manufacturing can help enterprises formulate more targeted intelligent manufacturing development strategies according to their own stage characteristics to accelerate the intelligent development. The concept of intelligent manufacturing ecosystem is proposed. By exploring the evolution process of intelligent manufacturing ecosystems, a three-stage theoretical model of influencing factors of intelligent manufacturing of enterprises is constructed. The theoretical model and related assumptions are verified using the empirical data of manufacturing enterprises of many provinces and cities in China. The results show that most factors in the digital stage, network stage, and intelligent stage significantly affect the development of enterprise intelligent manufacturing systems. This study provides theoretical reference and suggestions for manufacturing enterprises to develop intelligent manufacturing.
{"title":"Influencing Factors of Enterprise Intelligent Manufacturing Based on the Three Stages of Intelligent Manufacturing Ecosystems","authors":"Xuehong Ding, Li Shi, M. Shi, Yuan Liu","doi":"10.4018/jitr.299925","DOIUrl":"https://doi.org/10.4018/jitr.299925","url":null,"abstract":"Intelligent manufacturing is an important method for transforming and upgrading enterprise intelligence. Studying the influencing factors of enterprise intelligent manufacturing can help enterprises formulate more targeted intelligent manufacturing development strategies according to their own stage characteristics to accelerate the intelligent development. The concept of intelligent manufacturing ecosystem is proposed. By exploring the evolution process of intelligent manufacturing ecosystems, a three-stage theoretical model of influencing factors of intelligent manufacturing of enterprises is constructed. The theoretical model and related assumptions are verified using the empirical data of manufacturing enterprises of many provinces and cities in China. The results show that most factors in the digital stage, network stage, and intelligent stage significantly affect the development of enterprise intelligent manufacturing systems. This study provides theoretical reference and suggestions for manufacturing enterprises to develop intelligent manufacturing.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"149 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125882196","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 technological advancements and its reach Social media has become an essential part of our daily lives. Using social media platforms allows propagandist to spread the propaganda more effortlessly and faster than ever before. Machine learning and Natural language processing applications to solve the problem of propaganda in social media has invited researchers attention in recent years. Several techniques and tools have been proposed to counter propagation of propaganda over social media. This work pursues to analyse the trends in research studies in the recent past which address this issue. Our purpose is to conduct a comprehensive literature review of studies focusing on this area. We perform meta-analysis, categorization, and classification of several existing scholarly articles to increase the understanding of the state-of-the-art in the mentioned field.
{"title":"A Systematic Comparison of Machine Learning and NLP Techniques to Unveil Propaganda in Social Media","authors":"Deptii D. Chaudhari, A. Pawar","doi":"10.4018/jitr.299384","DOIUrl":"https://doi.org/10.4018/jitr.299384","url":null,"abstract":"With the technological advancements and its reach Social media has become an essential part of our daily lives. Using social media platforms allows propagandist to spread the propaganda more effortlessly and faster than ever before. Machine learning and Natural language processing applications to solve the problem of propaganda in social media has invited researchers attention in recent years. Several techniques and tools have been proposed to counter propagation of propaganda over social media. This work pursues to analyse the trends in research studies in the recent past which address this issue. Our purpose is to conduct a comprehensive literature review of studies focusing on this area. We perform meta-analysis, categorization, and classification of several existing scholarly articles to increase the understanding of the state-of-the-art in the mentioned field.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121589162","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}
Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.
{"title":"Line Segment-Based Clustering Approach With Self-Organizing Maps","authors":"G. Chamundeswari, G. Varma, C. Satyanarayana","doi":"10.4018/jitr.2021100103","DOIUrl":"https://doi.org/10.4018/jitr.2021100103","url":null,"abstract":"Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127820034","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}
An enhanced algorithm to recognize the human face using bi-dimensional fractal codes and deep belief networks is presented in this work. The proposed method is experimentally robust against variations in the appearance of human face images, despite different disturbances affecting the measurements and the acquisition process such as occlusion, changes in lighting, pose, and expression or the presence or absence of structural components. That is mainly based on fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with a deep belief network (DBN) classifier. The evaluation is performed through comparisons using probabilistic neural network (PNN) and nearest neighbours (KNN) approaches on three well-known databases (FERET, ORL, and FEI). The results suggest the effectiveness and robustness of the proposed approach.
{"title":"Face Recognition Based on Fractal Code and Deep Belief Networks","authors":"Mohamed Benouis","doi":"10.4018/jitr.2021100107","DOIUrl":"https://doi.org/10.4018/jitr.2021100107","url":null,"abstract":"An enhanced algorithm to recognize the human face using bi-dimensional fractal codes and deep belief networks is presented in this work. The proposed method is experimentally robust against variations in the appearance of human face images, despite different disturbances affecting the measurements and the acquisition process such as occlusion, changes in lighting, pose, and expression or the presence or absence of structural components. That is mainly based on fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with a deep belief network (DBN) classifier. The evaluation is performed through comparisons using probabilistic neural network (PNN) and nearest neighbours (KNN) approaches on three well-known databases (FERET, ORL, and FEI). The results suggest the effectiveness and robustness of the proposed approach.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125580614","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}
This paper proposes a framework of services selection and classification for an efficient provider's services discovery in a cloud-based supply chain. This framework combines the advantages of the web service technology and agent paradigm to select dynamically the best services among those that operated in a supply chain. It is based on two levels: the UDDI cloud level and the agent one. The UDDI cloud level allows web services, which represent providers' business functionalities, to be classified, discovered, selected, and invoked by agents that are applied to the supply chain construction. The agent level contains an agent society that manages the different steps of cooperation and negotiation between the different business entities in a supply chain, as business-to-business and business-to-customer transactions. On the basis of the characteristics of supply chain, a negotiation protocol between agents has been proposed.
{"title":"Efficient Discovery of Provider Services in a Cloud-Based Supply Chain","authors":"Souheila Boudouda, Mahmoud Boufaïda","doi":"10.4018/jitr.2021100101","DOIUrl":"https://doi.org/10.4018/jitr.2021100101","url":null,"abstract":"This paper proposes a framework of services selection and classification for an efficient provider's services discovery in a cloud-based supply chain. This framework combines the advantages of the web service technology and agent paradigm to select dynamically the best services among those that operated in a supply chain. It is based on two levels: the UDDI cloud level and the agent one. The UDDI cloud level allows web services, which represent providers' business functionalities, to be classified, discovered, selected, and invoked by agents that are applied to the supply chain construction. The agent level contains an agent society that manages the different steps of cooperation and negotiation between the different business entities in a supply chain, as business-to-business and business-to-customer transactions. On the basis of the characteristics of supply chain, a negotiation protocol between agents has been proposed.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126339798","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}
Fog computing is used to enrich the ability of cloud computing applications. Fog is a kind of buffer area placed between the data processing location and the data storage equipment in the network and plays a significant role in processing the real time data. The lack of resource provisioning approaches and high demand for IoT services make the fog node overloaded. Load balancing is a method to realize efficient resource utilization to avoid bottlenecks, overload, and fog node failure. This study suggests a concept to compute the probabilistic overloading state of a fog node and identification of fog node for load sharing. Each fog node computes Fstate and sends the message at regular intervals to the fog node coordinator (FNC). FNC maintains a fog that is utilized for offloading in case of fog overloading. A comparative study shows that the proposed model avoids an overloading state by the transfer of a certain number of requests to an underloaded fog node before actual overloading occurs. Numerical results validate theoretical investigation and efficiency of the proposed study.
{"title":"An Overloading State Computation and Load Sharing Mechanism in Fog Computing","authors":"Pushpa Singh, R. Agrawal","doi":"10.4018/jitr.2021100108","DOIUrl":"https://doi.org/10.4018/jitr.2021100108","url":null,"abstract":"Fog computing is used to enrich the ability of cloud computing applications. Fog is a kind of buffer area placed between the data processing location and the data storage equipment in the network and plays a significant role in processing the real time data. The lack of resource provisioning approaches and high demand for IoT services make the fog node overloaded. Load balancing is a method to realize efficient resource utilization to avoid bottlenecks, overload, and fog node failure. This study suggests a concept to compute the probabilistic overloading state of a fog node and identification of fog node for load sharing. Each fog node computes Fstate and sends the message at regular intervals to the fog node coordinator (FNC). FNC maintains a fog that is utilized for offloading in case of fog overloading. A comparative study shows that the proposed model avoids an overloading state by the transfer of a certain number of requests to an underloaded fog node before actual overloading occurs. Numerical results validate theoretical investigation and efficiency of the proposed study.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126659626","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}
Since software aging problems have been found in many areas, how to find an optimal time to rejuvenate is vital for software aging problems. In this paper, the authors propose a newly hybrid method to predict resource depletion of a web server suffered from software aging problems. The proposed method comprises three parts. First, a smoothing method, self-organized map, is used to make resource consumption series glossier. Second, several sub-optimal methods are utilized to fit resource consumption series. Third, an optimization method is proposed to combine all single methods to predict software aging. In experiments, the authors use the real commercial running dataset to validate the effect of the proposed method. And the presented method has a better prediction result for both available memory and heap memory under two metrics: root mean square error and mean average error.
{"title":"Predicting Software Aging With a Hybrid Weight-Based Method","authors":"Yongquan Yan, Yanjun Li, Bin Cheng","doi":"10.4018/jitr.2021100105","DOIUrl":"https://doi.org/10.4018/jitr.2021100105","url":null,"abstract":"Since software aging problems have been found in many areas, how to find an optimal time to rejuvenate is vital for software aging problems. In this paper, the authors propose a newly hybrid method to predict resource depletion of a web server suffered from software aging problems. The proposed method comprises three parts. First, a smoothing method, self-organized map, is used to make resource consumption series glossier. Second, several sub-optimal methods are utilized to fit resource consumption series. Third, an optimization method is proposed to combine all single methods to predict software aging. In experiments, the authors use the real commercial running dataset to validate the effect of the proposed method. And the presented method has a better prediction result for both available memory and heap memory under two metrics: root mean square error and mean average error.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125499347","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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