Semantic client‐side approach for web personalization of SaaS‐based cloud services

Fan, Haolong; Hussain, Farookh Khadeer; Hussain, Omar Khadeer

doi:10.1002/cpe.3418

Cited by 7 publications

(14 citation statements)

References 28 publications

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“…These client-side designs were built successfully, but most heavy tasks are still handled on the server side, neglecting the capability of the current browser client, new front-end technologies and the utilization of semantics. In our previous work [14], we created an innovative approach to implement semantic client-side personalization for SaaS. While other personalization services rely extensively on the server side, this solution leverages Rich Client technologies and Semantic Web to ameliorate performance and e ciency.…”

Section: Related Workmentioning

confidence: 99%

“…We extend a semantic client-side personalization approach [14] by integrating Rich Client and Semantic Web for SaaS-based personalization. To improve the e ciency in gathering user profile data and reducing the overhead of server-side computing, parts of the functions and recommendation tasks are migrated from the back end (server-side) to the front end (client-side).…”

Section: Extended Semantic Client-side Personalizationmentioning

confidence: 99%

“…It comprises four fundamental layers as shown in Figure 2. In the Client Layer, it deploys a semantic client-side approach [14] with local web storage and semantic web content for SaaS. A Business Model Layer on the main tier is responsible for receiving user data, storing data, processing data, generating patterns, building personalization configurations and recommending services.…”

Section: Saas Personalization Frameworkmentioning

confidence: 99%

“…An Observer Module gathers user preferences and environment information and generates concept usage data. By applying data mining techniques, the task of a Recommendation Engine is to discover recommendation patterns [14] by investigating user data to predict user personalization. According to the recommendation patterns o↵ered, a Personalization Engine contributes to the creation of personalization configurations and the delivery of service content.…”

Section: Saas Personalization Frameworkmentioning

confidence: 99%

“…Figure 1 represents a conceptual diagram of a personalization process. It comprises di↵erent phases, including: Data collection, Data preparation and transformation, Data preservation, Recommendation, Personalization and service delivery [14]. These phases require di↵erent information sources so as to prepare personalization of services.…”

Section: Saas Personalization Frameworkmentioning

confidence: 99%

See 4 more Smart Citations

An integrated personalization framework for SaaS-based cloud services

Fan

Hussain

Younas

et al. 2015

Future Generation Computer Systems

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Section: Related Workmentioning

confidence: 99%

Section: Extended Semantic Client-side Personalizationmentioning

confidence: 99%

Section: Saas Personalization Frameworkmentioning

confidence: 99%

Section: Saas Personalization Frameworkmentioning

confidence: 99%

Section: Saas Personalization Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

An integrated personalization framework for SaaS-based cloud services

Fan

Hussain

Younas

et al. 2015

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

Advances on cloud services and cloud computing

Barolli

Chen

Xhafa

2014

Concurrency and Computation

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EDITORIALAdvances on cloud services and cloud computing As cloud computing becomes prevalent, many organizations outsource the expensive computing and storage into the cloud servers. This brings many benefits including relief of the burden for storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, and personnel maintenances. Despite the tremendous benefits, the outsource storage inevitably suffers from the security, privacy, trustworthiness, and the access of outsourced data. To address these issues, there has been increased interest to propose and design new algorithms and methodologies for cloud computing and cloud services.This special issue brings together high quality contributions in these research areas. The papers of the special issue have also identified and discussed the challenges in the field that will lead to new solutions and applications for cloud computing and cloud services.The special issue consists of 12 papers organized as follows. In the first paper [1], the authors propose the guidelines of a holistic security model in online collaborative learning through an effective trustworthiness. They consider a parallel processing approach, which can considerably decrease the time of data processing, thus allowing for building relevant trustworthiness models to support learning activities even in real time. The authors used a distributed infrastructure, such as Hadoop and cluster computing, to implement and evaluate the proposed approach for massive processing of log data. The experimental results showed the feasibility of coping with the problem of structuring and processing ill-formatted, heterogeneous, large log files to extract information on trustworthiness indicators and levels from learning groups and ultimately fill a global framework devoted to improve information security in e-learning in real time.In the second paper [2], the authors present a ciphertext-policy attribute-based proxy reencryption scheme for data sharing in public clouds. They proposed a new single-hop unidirectional Ciphertext-Policy Attribute-Based Proxy Re-Encryptio (CP-ABPRE) scheme, which supports attribute-based re-encryption with any monotonic access structure, to tackle the open problem left by the existing CP-ABPRE schemes. The authors showed that the proposed scheme can be proved secure in the random oracle model under the decisional q-parallel bilinear Diffie-Hellman exponentiation assumption.In the third paper [3], the authors propose Ontology as a Service (OaaS), where the ontology tailoring process serves as a service in the cloud. To illustrate OaaS, in this paper, they propose sub-ontology extraction and extension, whereby multiple sub-ontologies are extracted from various source ontologies and then these extracted sub-ontologies are extended to form a complete ontology to be used by the user. The authors use a maximum extraction method to facilitate this. Unified Medical Language System (UMLS) metathesaurus ontology is us...

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