Pub Date : 2019-07-01DOI: 10.4018/IJWSR.2019070105
Jiangchuan Chen, Jiajia Jiang, Dan Luo
Clouds provide highly elastic resource provisioning styles through which scientific workflows are allowed to acquire desired resources ahead of the execution and build required software environment on virtual machines (VMs). However, various challenges for cloud workflow, especially its optimal scheduling, are yet to be addressed. Traditional approaches mainly consider VMs to be with non-fluctuating, time-invariant, stochastic, or bounded performance. This work describes workflows to be deployed and executed over distributed infrastructure-as-a-service clouds with time-varying performance of VMs and is aimed at reducing the execution cost of workflow while meeting deadline constraints. For this purpose, the authors employ time-series-based prediction approaches to capture dynamic performance fluctuations, feed an evolutionary algorithm with predicted performance information, and generate schedules at real-time. A case study based on multiple randomly-generated workflow templates and third-party commercial clouds shows that their proposed approach outperforms traditional ones.
{"title":"A Predictive and Evolutionary Approach for Cost-Effective and Deadline-Constrained Workflow Scheduling Over Distributed IaaS Clouds","authors":"Jiangchuan Chen, Jiajia Jiang, Dan Luo","doi":"10.4018/IJWSR.2019070105","DOIUrl":"https://doi.org/10.4018/IJWSR.2019070105","url":null,"abstract":"Clouds provide highly elastic resource provisioning styles through which scientific workflows are allowed to acquire desired resources ahead of the execution and build required software environment on virtual machines (VMs). However, various challenges for cloud workflow, especially its optimal scheduling, are yet to be addressed. Traditional approaches mainly consider VMs to be with non-fluctuating, time-invariant, stochastic, or bounded performance. This work describes workflows to be deployed and executed over distributed infrastructure-as-a-service clouds with time-varying performance of VMs and is aimed at reducing the execution cost of workflow while meeting deadline constraints. For this purpose, the authors employ time-series-based prediction approaches to capture dynamic performance fluctuations, feed an evolutionary algorithm with predicted performance information, and generate schedules at real-time. A case study based on multiple randomly-generated workflow templates and third-party commercial clouds shows that their proposed approach outperforms traditional ones.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"7 1","pages":"78-94"},"PeriodicalIF":1.1,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82556992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.4018/IJWSR.2019070103
Chunkai Zhang, Ao Yin
In this article, the authors propose a novel anomaly detection algorithm based on subspace local density estimation. The key insight of the proposed algorithm is to build multiple trident trees, which can implement the process of building subspace and local density estimation. Each trident tree (T-tree) is constructed recursively by splitting the data outside of 3 sigma into the left or right subtree and splitting the remaining data into the middle subtree. Each node in trident tree records the number of instances that falls on this node, so each trident tree can be used as a local density estimator. The density of each instance is the average of all trident tree evaluation instance densities, and it can be used as the anomaly score of instances. Since each trident tree is constructed according to 3 sigma principle, it can obtain good anomaly detection results without a large tree height. Theoretical analysis and experimental results show that the proposed algorithm is effective and efficient.
{"title":"Anomaly Detection Algorithm Based on Subspace Local Density Estimation","authors":"Chunkai Zhang, Ao Yin","doi":"10.4018/IJWSR.2019070103","DOIUrl":"https://doi.org/10.4018/IJWSR.2019070103","url":null,"abstract":"In this article, the authors propose a novel anomaly detection algorithm based on subspace local density estimation. The key insight of the proposed algorithm is to build multiple trident trees, which can implement the process of building subspace and local density estimation. Each trident tree (T-tree) is constructed recursively by splitting the data outside of 3 sigma into the left or right subtree and splitting the remaining data into the middle subtree. Each node in trident tree records the number of instances that falls on this node, so each trident tree can be used as a local density estimator. The density of each instance is the average of all trident tree evaluation instance densities, and it can be used as the anomaly score of instances. Since each trident tree is constructed according to 3 sigma principle, it can obtain good anomaly detection results without a large tree height. Theoretical analysis and experimental results show that the proposed algorithm is effective and efficient.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"2 1","pages":"44-58"},"PeriodicalIF":1.1,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83408124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.4018/IJWSR.2019070101
Jian Shu, H. Jain, Changyong Liang
The demand for agile and flexible business application systems has sparked interest in using cloud computing technology to respond quickly and effectively to a dynamic business environment. The authors classify the appropriate cloud services as a multi-objectives task scheduling problem in a hybrid cloud service system. In this article, the authors propose a business process (BP) driven task scheduling system that supports multiple clouds, including private ones. A trust-based non-dominated sorting genetic algorithm (NSGA2) is developed to solve the multi-objective task scheduling problem. By sorting populations into different hierarchies based on the ordering of Pareto dominance, they identify a Pareto-optimal multi-dimensional frontier that permits managers to reconcile conflicting objectives when scheduling tasks on cloud resources. The authors illustrate the usability and effectiveness of their approach by applying it to a case study conducting simulated experiments.
{"title":"Business Process Driven Trust-Based Task Scheduling","authors":"Jian Shu, H. Jain, Changyong Liang","doi":"10.4018/IJWSR.2019070101","DOIUrl":"https://doi.org/10.4018/IJWSR.2019070101","url":null,"abstract":"The demand for agile and flexible business application systems has sparked interest in using cloud computing technology to respond quickly and effectively to a dynamic business environment. The authors classify the appropriate cloud services as a multi-objectives task scheduling problem in a hybrid cloud service system. In this article, the authors propose a business process (BP) driven task scheduling system that supports multiple clouds, including private ones. A trust-based non-dominated sorting genetic algorithm (NSGA2) is developed to solve the multi-objective task scheduling problem. By sorting populations into different hierarchies based on the ordering of Pareto dominance, they identify a Pareto-optimal multi-dimensional frontier that permits managers to reconcile conflicting objectives when scheduling tasks on cloud resources. The authors illustrate the usability and effectiveness of their approach by applying it to a case study conducting simulated experiments.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"186 6 1","pages":"1-28"},"PeriodicalIF":1.1,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81085759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.4018/IJWSR.2019040102
Lin Xiao, Chuanmin Mi
This exploratory study used a qualitative approach to segment consumers in an online group buying context based on benefits pursued. 58 participants who have online group buying experience were interviewed. A cluster analysis was conducted on the interview data. The authors found three sub-groups of consumers: economic shoppers, balanced shoppers, and destination shoppers. A hierarchical decision-making process model was developed for different sub-groups of consumers. The results showed that these three sub-groups of consumers are different in terms of their decision-making process. This study overcomes the shortcomings of traditional segmentation studies by proposing a new segmentation method.
{"title":"A Qualitative Approach to Understand Consumer Groups and Decision-Making Process in Online Group Buying: An Exploratory Study","authors":"Lin Xiao, Chuanmin Mi","doi":"10.4018/IJWSR.2019040102","DOIUrl":"https://doi.org/10.4018/IJWSR.2019040102","url":null,"abstract":"This exploratory study used a qualitative approach to segment consumers in an online group buying context based on benefits pursued. 58 participants who have online group buying experience were interviewed. A cluster analysis was conducted on the interview data. The authors found three sub-groups of consumers: economic shoppers, balanced shoppers, and destination shoppers. A hierarchical decision-making process model was developed for different sub-groups of consumers. The results showed that these three sub-groups of consumers are different in terms of their decision-making process. This study overcomes the shortcomings of traditional segmentation studies by proposing a new segmentation method.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"37 1","pages":"24-46"},"PeriodicalIF":1.1,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73815396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.4018/IJWSR.2019040103
Ahsan Hussain, B. N. Keshavamurthy, Seema V. Wazarkar
Information-disclosure by social-users has increased enormously. Using this information for accurate location-prediction is challenging. Thus, a novel Multi-Layer Ensemble Classification scheme is proposed. It works on un-weighted/weighted majority voting, using novel weight-assignment function. Base learners are selected based on their individual performances for training the model. Main motive is to develop an efficient approach for check-ins-based location-classification of social-users. The proposed model is implemented on Foursquare datasets where a classification accuracy of 94% is achieved, which is higher than other state-of-the-art techniques. Apart from tracking locations of social-users, proposed framework can be useful for detecting malicious users present in various expert and intelligent-system.
{"title":"A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users","authors":"Ahsan Hussain, B. N. Keshavamurthy, Seema V. Wazarkar","doi":"10.4018/IJWSR.2019040103","DOIUrl":"https://doi.org/10.4018/IJWSR.2019040103","url":null,"abstract":"Information-disclosure by social-users has increased enormously. Using this information for accurate location-prediction is challenging. Thus, a novel Multi-Layer Ensemble Classification scheme is proposed. It works on un-weighted/weighted majority voting, using novel weight-assignment function. Base learners are selected based on their individual performances for training the model. Main motive is to develop an efficient approach for check-ins-based location-classification of social-users. The proposed model is implemented on Foursquare datasets where a classification accuracy of 94% is achieved, which is higher than other state-of-the-art techniques. Apart from tracking locations of social-users, proposed framework can be useful for detecting malicious users present in various expert and intelligent-system.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"1 1","pages":"47-64"},"PeriodicalIF":1.1,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78733497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.4018/IJWSR.2019040101
K. K. Fletcher
Typically, users' service requests, which are similar with varying preferences on non-functional attributes, may result in ranked lists of services that partially meet their needs due to conflicting non-functional attributes. The resultant multiple ranked lists of services that partially satisfies the user's request makes it challenging for the user to choose an optimal service, based on his/her preference. This work proposes a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user. Two algorithms are proposed; 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Examples using real-world airline services to evaluate both algorithms show that the results from both proposed algorithms closely represent the sets of ranked lists better than using alternative approaches. Experiments were also carried out to validate their performance.
通常，用户的服务请求是相似的，但在非功能属性上有不同的偏好，这可能会导致由于冲突的非功能属性而部分满足其需求的服务排序列表。由此产生的多个服务排序列表部分满足了用户的请求，这使得用户很难根据自己的偏好选择最优服务。这项工作提出了一种方法，该方法将多个服务排名列表聚合为单个聚合排名列表，其中为用户选择排名最高的服务。提出了两种算法;1) RACoL (Rank Aggregation for Complete Lists)，用于聚合完整的排名表;2)RAIL (Rank Aggregation for Incomplete Lists)用于聚合不完整的排名表。使用现实世界的航空服务来评估这两种算法的示例表明，两种算法的结果都比使用替代方法更接近地表示排名列表集。实验也验证了它们的性能。
{"title":"A Method for Aggregating Ranked Services for Personal Preference Based Selection","authors":"K. K. Fletcher","doi":"10.4018/IJWSR.2019040101","DOIUrl":"https://doi.org/10.4018/IJWSR.2019040101","url":null,"abstract":"Typically, users' service requests, which are similar with varying preferences on non-functional attributes, may result in ranked lists of services that partially meet their needs due to conflicting non-functional attributes. The resultant multiple ranked lists of services that partially satisfies the user's request makes it challenging for the user to choose an optimal service, based on his/her preference. This work proposes a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user. Two algorithms are proposed; 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Examples using real-world airline services to evaluate both algorithms show that the results from both proposed algorithms closely represent the sets of ranked lists better than using alternative approaches. Experiments were also carried out to validate their performance.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"20 1","pages":"1-23"},"PeriodicalIF":1.1,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82403307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.4018/IJWSR.2019040105
Ruilin Liu, Zhongjie Wang, Xiaofei Xu
QoS-aware service composition problem has been drawn great attention in recent years. As an NP-hard problem, high time complexity is inevitable if global optimization algorithms (such as integer programming) are adopted. Researchers applied various evolutionary algorithms to decrease the time complexity by looking for a near-optimum solution. However, each evolutionary algorithm has two or more parameters, the values of which are to be assigned by algorithm designers and likely have impacts on the optimization results (primarily time complexity and optimality). The authors' experiments show that there are some dependencies between the features of a service composition problem, the values of an evolutionary algorithm's parameters, and the optimization results. In this article, the authors propose an improved algorithm called Service-Oriented Artificial Bee Colony algorithm considering Priori Knowledge (S-ABCPK) to solve service composition problem and focus on the S-ABCPK's parameter turning issue. The objective is to identify the potential dependency for designers of a service composition algorithm easily setting up the values of S-ABCPK parameters to obtain a preferable composition solution without many times of tedious attempts. Eight features of the service composition problem and the priori knowledge, five S-ABCPK parameters and two metrics of the final solution are identified. Based on a large volume of experiment data, S-ABCPK parameter tuning for a given service composition problem is conducted using C4.5 algorithm and the dependency between problem features and S-ABCPK parameters are established using the neural network method. An experiment on a validation dataset shows the feasibility of the approach.
{"title":"Parameter Tuning for S-ABCPK: An Improved Service Composition Algorithm Considering Priori Knowledge","authors":"Ruilin Liu, Zhongjie Wang, Xiaofei Xu","doi":"10.4018/IJWSR.2019040105","DOIUrl":"https://doi.org/10.4018/IJWSR.2019040105","url":null,"abstract":"QoS-aware service composition problem has been drawn great attention in recent years. As an NP-hard problem, high time complexity is inevitable if global optimization algorithms (such as integer programming) are adopted. Researchers applied various evolutionary algorithms to decrease the time complexity by looking for a near-optimum solution. However, each evolutionary algorithm has two or more parameters, the values of which are to be assigned by algorithm designers and likely have impacts on the optimization results (primarily time complexity and optimality). The authors' experiments show that there are some dependencies between the features of a service composition problem, the values of an evolutionary algorithm's parameters, and the optimization results. In this article, the authors propose an improved algorithm called Service-Oriented Artificial Bee Colony algorithm considering Priori Knowledge (S-ABCPK) to solve service composition problem and focus on the S-ABCPK's parameter turning issue. The objective is to identify the potential dependency for designers of a service composition algorithm easily setting up the values of S-ABCPK parameters to obtain a preferable composition solution without many times of tedious attempts. Eight features of the service composition problem and the priori knowledge, five S-ABCPK parameters and two metrics of the final solution are identified. Based on a large volume of experiment data, S-ABCPK parameter tuning for a given service composition problem is conducted using C4.5 algorithm and the dependency between problem features and S-ABCPK parameters are established using the neural network method. An experiment on a validation dataset shows the feasibility of the approach.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"65 4","pages":"88-109"},"PeriodicalIF":1.1,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72599129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.4018/IJWSR.2019040104
F. M. Bouyakoub, Abdelkader Belkhir, Amina Belkacemnacer, Sara Harfouche
The article presents an electronic negotiation agent, integrated within a multiagent system for an electronic tourism platform. The e-negotiation process is based on a winner-winner approach, using a bargaining protocol. However, with the proliferation of services, the task of searching for relevant services becomes more and more difficult. Thus, the authors also propose a search agent to find tourism services corresponding to the client request and profile. The discovery process uses a quantitative similarity measure.
{"title":"An E-negotiation Agent for an E-tourism Platform","authors":"F. M. Bouyakoub, Abdelkader Belkhir, Amina Belkacemnacer, Sara Harfouche","doi":"10.4018/IJWSR.2019040104","DOIUrl":"https://doi.org/10.4018/IJWSR.2019040104","url":null,"abstract":"The article presents an electronic negotiation agent, integrated within a multiagent system for an electronic tourism platform. The e-negotiation process is based on a winner-winner approach, using a bargaining protocol. However, with the proliferation of services, the task of searching for relevant services becomes more and more difficult. Thus, the authors also propose a search agent to find tourism services corresponding to the client request and profile. The discovery process uses a quantitative similarity measure.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"56 1","pages":"65-87"},"PeriodicalIF":1.1,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73401156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-7501-6.ch078
Surajit Bag
The study considers samples from the South African engineering companies who are strategic suppliers to mining and minerals industry and further explores the uncertainties persisting in the supply chain network. Further investigation was done to understand the role of big data and predictive analysis (BDPA) in managing the supply uncertainties. The paper finally uses partial least square regression analysis to study the relationship among buyer-supplier relationship, big data and predictive analysis and supply chain performance. The analysis supported the second and third hypothesis. Therefore, it is established that firstly, there is a positive relationship between big data, predictive analysis and supply chain performance and secondly, there is a positive relationship between and big data, predictive analysis and buyer-supplier relationship. The study is a unique contribution to the current literature by shedding light on the practical problems persisting in the South African context.
{"title":"Big Data and Predictive Analysis Is Key to Superior Supply Chain Performance","authors":"Surajit Bag","doi":"10.4018/978-1-5225-7501-6.ch078","DOIUrl":"https://doi.org/10.4018/978-1-5225-7501-6.ch078","url":null,"abstract":"The study considers samples from the South African engineering companies who are strategic suppliers to mining and minerals industry and further explores the uncertainties persisting in the supply chain network. Further investigation was done to understand the role of big data and predictive analysis (BDPA) in managing the supply uncertainties. The paper finally uses partial least square regression analysis to study the relationship among buyer-supplier relationship, big data and predictive analysis and supply chain performance. The analysis supported the second and third hypothesis. Therefore, it is established that firstly, there is a positive relationship between big data, predictive analysis and supply chain performance and secondly, there is a positive relationship between and big data, predictive analysis and buyer-supplier relationship. The study is a unique contribution to the current literature by shedding light on the practical problems persisting in the South African context.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"149 ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73105639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-7501-6.ch027
Mohanad Halaweh, Ahmed El Massry
The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.
{"title":"A Synergetic Model for Implementing Big Data in Organizations","authors":"Mohanad Halaweh, Ahmed El Massry","doi":"10.4018/978-1-5225-7501-6.ch027","DOIUrl":"https://doi.org/10.4018/978-1-5225-7501-6.ch027","url":null,"abstract":"The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"24 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83721952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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