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International Journal of Web Based Communities (6 papers in press)
The effect of e-retailers innovations on shoppers impulsiveness and addiction in web-based communities: The case of Amazons Prime Now by Zahy Ramadan, Maya Farah, Shireen Daouk Abstract: In the era of exponential growth of online shopping, e-commerce has accelerated consumers shift from offline to online shopping, specifically in the FMCG industry. In 2015, Amazon launched the Amazon Prime Now service based on same-day delivery. The research objective is to understand the implications of this ordering tool on the consumer journey from the shopper web-based community, the retailer and brand perspectives. In order to investigate the usage of Amazon Prime Now and its impact on the overall consumer journey, a survey was devised and completed by 25 Prime users who reside in the UK. The findings show that clients gratification is due to the outstanding customer service Amazon offers, which as a result established a trust and love relationship with the retailer. Moreover, the findings show that while the minimum order fees have reduced shoppers impulsive behaviour in web-based communities, an overall addiction toward the use of Amazon Prime Now was taking place. Keywords: Amazon; retailing; shopper web-based community; UK. DOI: 10.1504/IJWBC.2019.10022389
Special Issue on: Cloud-Based Opportunities for Online Communities
Automated question extraction and tagging for cloud-based online communities by Saikishor Jangiti, G. Swathi, Logesh Ravi, V. Vijayakumar, V. Subramaniyaswamy Abstract: Crowd-based question answering forums and cloud-based community question answering platforms provide us with the dais to post questions and answers online. This helps the users to get desired answers from expert users. It is a challenge for a person with mobility needs to go out and explore. The 'wheelchair accessible' information provided by Google Maps is useful to explore before going out. Local guides share this knowledge on Google Maps by answering quick questionnaire. Automated question generation is a key challenge that we face with regard to natural languages in the context of users visited locations, already reviewed places, likes, interests and user experience. In this paper, we have implemented an automatic question generation system that comprises of part-of-speech (POS) tagger, text-to-question generation task using syntactic analysis and a named entity extraction. The proposed system is tested with human effort and is generating valid questionnaire. Keywords: community question answering; review generation; authenticity of reviews; tagging; question extraction. DOI: 10.1504/IJWBC.2019.10022390
Identification of regression function and distribution model for denial of service attack in Second Life online community using simple network management protocol by Rajakumaran Gayathri, Venkataraman Neelanarayanan Abstract: The evolution of internet results in the emergence of online communities. Numerous communities exist today with millions of users. Security, privacy and availability are the top constraints to be focussed in online communities. Among the other security violations, denial of service (DoS) ranks first as it disrupts the availability of services. DoS attack is reported in a popular virtual world, Second Life which made the complete portal inaccessible to legitimate users. As TCP-SYN is the more prevalent attack strategy of DoS, our solution is aimed to provide efficient detection of DoS in the Second Life community. Detection and differentiation of attack traffic from the legitimate is achieved through simple network management protocol (SNMP) and machine learning algorithms. In spite of the 'n' number of solutions, an efficient outperforming strategy with accurate attack detection is the critical requirement. This paper focuses on the DoS attack detection, classification using SNMP MIB variables and linear regression model. Experimental observation proves the detection accuracy of the method under DoS. Keywords: Second Life; DoS detection; SNMP. DOI: 10.1504/IJWBC.2019.10023010
Energy efficient resource management techniques in cloud environment for web-based community by machine learning: a survey by Pratibha Pandey, Abhishek Singh Abstract: Cloud computing is a platform where the services of information technology are delivered by retrieval of resource from the internet through web applications and web tools instead of using direct server. In order to offer better quality of service to the larger number of web-based community users, many companies are adopting cloud computing. A dynamic number of web-based community user are interacting with each other via cloud computing. The number of online users is not just limited to manual counting figure so it is extremely important to manage each and every resource efficiently by minimising the energy consumption. To increase the performance of the system, the concepts of machine learning can be used to solve the challenges occur in cloud computing while managing resources for online communities. In this paper, we discuss the application of machine learning in cloud environment and its effect on web-based community. Keywords: machine learning; web-based community; cloud computing; energy efficient; resource allocation; supervised learning; unsupervised learning; data centre. DOI: 10.1504/IJWBC.2019.10023011
Dynamic ranking of cloud services for web-based cloud communities: efficient algorithm for rating-based discovery and multi-level ranking of cloud services by Abdul Quadir Md, V. Vijayakumar Abstract: Trust assessment in cloud depends on QoS attributes. While evaluating the trustworthiness of the CSPs, traditional methods of trust assessment do not take into account QoS attributes that keep changing dynamically. To address the issue of determining the trustworthiness of CSPs in web, rating based dynamic discovery (RBDD) of QoS attributes that keeps changing periodically and multi-layer ranking (MLR) algorithm that ranks the discovered CSPs in an efficient manner have been proposed. This approach allows us to evaluate CSPs trustworthiness from cloud auditors perception. evaluation results indicates that the RBDD is capable of sensing behavioural changes in CSPs and discovers the dynamic trustworthy service providers and MLR algorithm ranks them based on CC requirements with high accuracy and minimal time complexity compared to other approaches. The proposed system has been validated with synthetic dataset owing to absence of standardisation. Keywords: cloud consumers; cloud services; cloud service providers; CSPs; cloud service registry; CSR; cloud service discovery; quality of services; QoS; service level agreement; SLA. DOI: 10.1504/IJWBC.2019.10023009
An intelligent fuzzy-induced recommender system for cloud-based cultural communities by Logesh Ravi, Malathi Devarajan, Gwanggil Jeon, Oguz Bayat, V. Subramaniyaswamy Abstract: The rapid development of communication technologies and web-based services generate a large amount of information. In recent years, recommender systems (RS) emerge as an effective mechanism to tackle the information overloading problems. By exploiting the cloud computing paradigm, RS discovers interesting new cultural items based on user preferences and interests. Recent investigations on RS reveal that employing social network data can yield enhanced personalised recommendations with better prediction accuracy. Since users tend to visit only conventional monuments, and many charming cultural items are hidden from them due to lack of awareness about the cultural sites. This article proposes a personalised recommendation model in the field of cultural heritage (CH) with the help of the cloud computing environment. The experimental results obtained demonstrate the improved performance of developed RS in the area of cultural heritage tourism services. Keywords: cloud computing; personalised recommender system; fuzzy-KNN; location-based social network; LBSN; cultural heritage. DOI: 10.1504/IJWBC.2019.10023012