Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users' web usage data. An overall accuracy of 87.73% is achieved by the proposed approach.
Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users' web usage data. An overall accuracy of 87.73% is achieved by the proposed approach.
Video-on-demand (VoD) applications have become extensively used nowadays. YouTube is one of the most extensively used VoD application. These applications are used for various purposes like entertainment, education, media, etc., of all age groups. Earlier, these applications were supported by private data centers and application servers. Sufficient infrastructure had to be bought and maintained, to support the demand even during unexpected peak times. This approach caused huge loss of resources when the demand is normal as a large portion of the resources remained idle. To overcome this, VoD application providers moved to the cloud, to host their video content's. This approach reduced the wastage of resources and the maintenance cost of the VoD application provider. The problem is to determine the number of resources to handle the demand while maintaining QoS for every instance. We have designed two algorithms in this paper, namely the multiple cloud resource allocation (MCRA) algorithm and the hybrid MCRA algorithm. Most of the cloud service providers (CSPs) basically provide two types of resource allocation schemes: (i) the reservation scheme and (ii) the on-demand scheme. The reservation scheme provides time-based tariff prices, where the discount is provided for the resources depending on their quantity and reservation time. This scheme is used in the MCRA algorithm to reduce the cost of the VoD application provider. In Hybrid MCRA algorithm both the reservation scheme and on-demand scheme are implemented, to overcome the drawbacks of the MCRA algorithm which are under-subscription and over-subscription. We have analyzed both the algorithms in terms of cost and allocation of resources. These algorithms can help allocate resources in of cloud for VoD applications in a cost-effective way and at the same time not compromise on the QoS of the video content.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.