Pub Date : 2018-12-01DOI: 10.6025/jdim/2018/16/6/289-307
Bassim Chabibi, A. Anwar, M. Nassar
In system engineering process, descriptive system models seem to be insufficient in order to perform a system verification which fulfils various stakeholders’ requirements. This aspect is well handled by simulation process through the use of several simulation techniques or algorithms. As a consequence, design process efficiency is considerably reduced by the fact that both system modeling and simulation tools are often used separately. This study introduces an integration process to unify the potential provided by systems modeling languages and simulation environments, through the definition of a Domain Specific Language, namely Simulation Modeling Language, that is built on the basis ofa deep study of common constructs, semantics and modeling methodologies of several simulation environments, in addition to the specification of a model transformation between this language and a simulation environment (MATLAB/SImulink) in order to illustrate both its importance et its efficiency in our integration approach. Through the specification of its syntaxes and semantics, the defined intermediate modeling language allows modeling systems by using common constructs and modeling methodologies of simulation process in order to ensure their modeling with simulation environments and, thus, con duct experiences and system verifications. The definition of this language constitutes the basis of our integration approach aiming to bridge the gap between system modeling and simulation aspects in order to benefit from the strengths and potentials of both approaches. The integration approach consists on the specification of a bidirectional transformation, based on the concepts of ModelDriven Engineering, to perform in future works. Subject Categories and Descriptors I.6 [Simulation and Modeling] I.6.5 [Model Development]; Modeling methodologies ; F.3.2 [Semantics of Programming Languages] General Terms: Modeling language, Simulation, System Engineering, Model Driven Engineering
{"title":"Model Integration Approach from SysML to MATLAB/Simulink","authors":"Bassim Chabibi, A. Anwar, M. Nassar","doi":"10.6025/jdim/2018/16/6/289-307","DOIUrl":"https://doi.org/10.6025/jdim/2018/16/6/289-307","url":null,"abstract":"In system engineering process, descriptive system models seem to be insufficient in order to perform a system verification which fulfils various stakeholders’ requirements. This aspect is well handled by simulation process through the use of several simulation techniques or algorithms. As a consequence, design process efficiency is considerably reduced by the fact that both system modeling and simulation tools are often used separately. This study introduces an integration process to unify the potential provided by systems modeling languages and simulation environments, through the definition of a Domain Specific Language, namely Simulation Modeling Language, that is built on the basis ofa deep study of common constructs, semantics and modeling methodologies of several simulation environments, in addition to the specification of a model transformation between this language and a simulation environment (MATLAB/SImulink) in order to illustrate both its importance et its efficiency in our integration approach. Through the specification of its syntaxes and semantics, the defined intermediate modeling language allows modeling systems by using common constructs and modeling methodologies of simulation process in order to ensure their modeling with simulation environments and, thus, con duct experiences and system verifications. The definition of this language constitutes the basis of our integration approach aiming to bridge the gap between system modeling and simulation aspects in order to benefit from the strengths and potentials of both approaches. The integration approach consists on the specification of a bidirectional transformation, based on the concepts of ModelDriven Engineering, to perform in future works. Subject Categories and Descriptors I.6 [Simulation and Modeling] I.6.5 [Model Development]; Modeling methodologies ; F.3.2 [Semantics of Programming Languages] General Terms: Modeling language, Simulation, System Engineering, Model Driven Engineering","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114313831","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 : 2018-12-01DOI: 10.6025/JDIM/2018/16/6/279-288
Johannes Fernandes Andry, Gary Juliawan, Y. Christian, J. Leonardo, Nicolas
{"title":"Parking System Development Using Extreme Programming Method","authors":"Johannes Fernandes Andry, Gary Juliawan, Y. Christian, J. Leonardo, Nicolas","doi":"10.6025/JDIM/2018/16/6/279-288","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/6/279-288","url":null,"abstract":"","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116314003","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 : 2018-12-01DOI: 10.6025/JDIM/2018/16/6/308-323
Tarik Chaghrouchni, Issam Mohammed Kabbaj, Z. Bakkoury
{"title":"Machine Learning in Predicting the Appropriate Model of Software Process-Models Deviation","authors":"Tarik Chaghrouchni, Issam Mohammed Kabbaj, Z. Bakkoury","doi":"10.6025/JDIM/2018/16/6/308-323","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/6/308-323","url":null,"abstract":"","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123112159","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 : 2018-10-01DOI: 10.6025/JDIM/2018/16/5/213-222
Hussien Ahmad, S. Dowaji
Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and analysis over data streams is a necessity, the existing solutions are no longer effective and sufficient. While many existing algorithms and approaches consider the content of the data stream, there are few approaches which consider the context and conditions in which the content has been produced. In this paper, we propose a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier detection rather than using the stream content alone or applying the contextual detection on content anomalies only. The detection algorithm incorporates two approaches; the first, a supervised detection method and the other, an unsupervised, which allows the detection process to adapt to the normal change in the stream behavior over time. The detected outliers are either both content and contextual outliers or contextual outliers only. The proposed contextual detection approach prunes the false positive outliers and detects the true negative outliers at the same time. Moreover, in this framework, the detection engine preserves both outliers and context values in which those outliers were detected to be used in the engine self-training and in outliers modeling in order to enhance the outlier prediction accuracy. Journal of Digital Information Management Subject Categories and Descriptors H.2 [Database Management] H.2.8 Database Applications];
{"title":"A Novel Framework for Context-aware Outlier Detection in Big Data Streams","authors":"Hussien Ahmad, S. Dowaji","doi":"10.6025/JDIM/2018/16/5/213-222","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/5/213-222","url":null,"abstract":"Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and analysis over data streams is a necessity, the existing solutions are no longer effective and sufficient. While many existing algorithms and approaches consider the content of the data stream, there are few approaches which consider the context and conditions in which the content has been produced. In this paper, we propose a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier detection rather than using the stream content alone or applying the contextual detection on content anomalies only. The detection algorithm incorporates two approaches; the first, a supervised detection method and the other, an unsupervised, which allows the detection process to adapt to the normal change in the stream behavior over time. The detected outliers are either both content and contextual outliers or contextual outliers only. The proposed contextual detection approach prunes the false positive outliers and detects the true negative outliers at the same time. Moreover, in this framework, the detection engine preserves both outliers and context values in which those outliers were detected to be used in the engine self-training and in outliers modeling in order to enhance the outlier prediction accuracy. Journal of Digital Information Management Subject Categories and Descriptors H.2 [Database Management] H.2.8 Database Applications];","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114101593","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 : 2018-10-01DOI: 10.6025/JDIM/2018/16/5/230-245
Gangyi Hu, Sumeth Yuenyong, Jian Qu, Jiang Rong, W. Kou
{"title":"A Noise-Robust Image Encryption Algorithm Based on Hyper Chaotic Cellular Neural Network","authors":"Gangyi Hu, Sumeth Yuenyong, Jian Qu, Jiang Rong, W. Kou","doi":"10.6025/JDIM/2018/16/5/230-245","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/5/230-245","url":null,"abstract":"","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123649854","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 : 2018-10-01DOI: 10.6025/JDIM/2018/16/5/223-229
Lyes Badis, M. Amad, D. Aïssani
{"title":"A Log Based Update of Replicated Profiles in Decentralized Social Networks","authors":"Lyes Badis, M. Amad, D. Aïssani","doi":"10.6025/JDIM/2018/16/5/223-229","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/5/223-229","url":null,"abstract":"","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":" 28","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132040339","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 : 2018-10-01DOI: 10.6025/JDIM/2018/16/5/246-257
Dounya Kassimi, O. Kazar, O. Boussaid, Abdelhak Merizig
The advantages and the solutions proposed with the appearance of the new technologies either Cloud computing or Big Data force the logical and physical structures of mass data storage move towards these technologies. In addition, the solutions offered presented in storage and processing of various operations. However, these solutions do not cover all the previous issues specially the one related to the security problem [1].
{"title":"New Approach for Intrusion Detection in Big Data as a Service in the Cloud","authors":"Dounya Kassimi, O. Kazar, O. Boussaid, Abdelhak Merizig","doi":"10.6025/JDIM/2018/16/5/246-257","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/5/246-257","url":null,"abstract":"The advantages and the solutions proposed with the appearance of the new technologies either Cloud computing or Big Data force the logical and physical structures of mass data storage move towards these technologies. In addition, the solutions offered presented in storage and processing of various operations. However, these solutions do not cover all the previous issues specially the one related to the security problem [1].","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123031237","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 : 2018-08-01DOI: 10.6025/JDIM/2018/16/4/157-168
R. Kohar
Different services have different values for their different properties (functional and non-functional). Moreover, consumers have different preferences for these properties. Since these preferences may be incomplete/ vague/imprecise, this inevitably can in turn cause selection of a non-optimal service for the consumer which may not have the desired value for these properties for the consumer. Therefore optimal web service selection based on the consumer’s preferences is a challenging task. In this paper, a new service selection model is proposed which considers consumer preferences for different service properties as well as the actual values of its properties. Services are evaluated and priority weights are computed separately for functional and non-functional properties using modified Fuzzy Extended Analytic Hierarchy Process (FEAHP) and Weighted Sum Method (WSM) respectively. Real time data is used to conduct experiments. Subject Categories and Descriptors [H.3.5 Online Information Services]; Web-based services: [I.2.3 Deduction and Theorem Proving]; Fuzzy, and probabilistic reasoning General Terms: Fuzzy Process; Web Services
{"title":"Optimal Web Service Selection Model using Fuzzy Extended AHP and Weighted Sum Method","authors":"R. Kohar","doi":"10.6025/JDIM/2018/16/4/157-168","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/4/157-168","url":null,"abstract":"Different services have different values for their different properties (functional and non-functional). Moreover, consumers have different preferences for these properties. Since these preferences may be incomplete/ vague/imprecise, this inevitably can in turn cause selection of a non-optimal service for the consumer which may not have the desired value for these properties for the consumer. Therefore optimal web service selection based on the consumer’s preferences is a challenging task. In this paper, a new service selection model is proposed which considers consumer preferences for different service properties as well as the actual values of its properties. Services are evaluated and priority weights are computed separately for functional and non-functional properties using modified Fuzzy Extended Analytic Hierarchy Process (FEAHP) and Weighted Sum Method (WSM) respectively. Real time data is used to conduct experiments. Subject Categories and Descriptors [H.3.5 Online Information Services]; Web-based services: [I.2.3 Deduction and Theorem Proving]; Fuzzy, and probabilistic reasoning General Terms: Fuzzy Process; Web Services","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132403643","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 : 2018-08-01DOI: 10.6025/jdim/2018/16/4/203-209
Yang Hui
Cloud computing characterizes a methodology for computing communications in a much effective manner, and a business paradigm for trading computing resources and services. Alternatively, these difficult and distributed planning’s turn a striking objective for intruders. Cloud computing provides huge latent for enhancing production and decrease expenditures. However it simultaneously acquires several novel security risks. Intrusion Detection Systems (IDS) have been employed broadly for identifying malicious actions in network communication and hosts. In this work, an artificial bee colony-BP neural network algorithm is applied to the detection module, in order to detect the complicated aggressive behaviors. Through example verification, the artificial bee colony-BP network algorithm has improved intrusion detection efficiency and classification precision, and can effectively guarantee the safety of the cloud computing environment. Subject Categories and Descriptors [D.4.6 Security and Protection] [C.2 Cmputer Communication Networks] Security and protection [F.1.1 Models of Computation]; Neural networks General Terms: Cloud Computing, Neural Networks, IDS
{"title":"Cloud Computing Intrusion Detection Using Artificial Bee Colony-BP Network Algorithm","authors":"Yang Hui","doi":"10.6025/jdim/2018/16/4/203-209","DOIUrl":"https://doi.org/10.6025/jdim/2018/16/4/203-209","url":null,"abstract":"Cloud computing characterizes a methodology for computing communications in a much effective manner, and a business paradigm for trading computing resources and services. Alternatively, these difficult and distributed planning’s turn a striking objective for intruders. Cloud computing provides huge latent for enhancing production and decrease expenditures. However it simultaneously acquires several novel security risks. Intrusion Detection Systems (IDS) have been employed broadly for identifying malicious actions in network communication and hosts. In this work, an artificial bee colony-BP neural network algorithm is applied to the detection module, in order to detect the complicated aggressive behaviors. Through example verification, the artificial bee colony-BP network algorithm has improved intrusion detection efficiency and classification precision, and can effectively guarantee the safety of the cloud computing environment. Subject Categories and Descriptors [D.4.6 Security and Protection] [C.2 Cmputer Communication Networks] Security and protection [F.1.1 Models of Computation]; Neural networks General Terms: Cloud Computing, Neural Networks, IDS","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117244698","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 : 2018-08-01DOI: 10.6025/JDIM/2018/16/4/192-202
Nazem Malkawi, K. Omari, Azmi Halasa
This paper aimed to study intellectual capital as a core competency for competitive advantage at pharmaceutical companies in Jordan. To complete the study, a case study was instituted by analyzing the responses of the pharmaceutical companies; the data collected were analyzed with a statistical package. The study concluded some results, the most appearing were; pharmaceutical companies depend on intellectual capital at a high level, competitive advantage also high, and there is a significant statistical effect of intellectual capital on competitive advantage as a whole and on all its indicators (leadership, human recourse, innovation, processes, and financial excellence) at (α ≤ 0.00). Researchers recommend pharmaceutical companies management and staff to reinforce using intellectual capital at all levels and functions and use it as a main source for competitive advantage in all its areas. Subject Categories and Descriptors: [G.3 Probability And Statistics]: [J.1 Administrative Data Processing ]; Business
{"title":"Intellectual Capital as a Core Competency for Competitive Advantage: A Case Study","authors":"Nazem Malkawi, K. Omari, Azmi Halasa","doi":"10.6025/JDIM/2018/16/4/192-202","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/4/192-202","url":null,"abstract":"This paper aimed to study intellectual capital as a core competency for competitive advantage at pharmaceutical companies in Jordan. To complete the study, a case study was instituted by analyzing the responses of the pharmaceutical companies; the data collected were analyzed with a statistical package. The study concluded some results, the most appearing were; pharmaceutical companies depend on intellectual capital at a high level, competitive advantage also high, and there is a significant statistical effect of intellectual capital on competitive advantage as a whole and on all its indicators (leadership, human recourse, innovation, processes, and financial excellence) at (α ≤ 0.00). Researchers recommend pharmaceutical companies management and staff to reinforce using intellectual capital at all levels and functions and use it as a main source for competitive advantage in all its areas. Subject Categories and Descriptors: [G.3 Probability And Statistics]: [J.1 Administrative Data Processing ]; Business","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124557934","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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