Correlation of Impact Measures of Institutional Repositories and PBRF Ranking

<p>This study examines the correlation of website impact factor of institutional repositories (IR) of all eight universities in New Zealand and the Performance Based Research Fund (PBRF) quality score. The purpose of the research is to find out whether there is correlation between these two figures. The research problems are: the correlation between the IR homepage ranking and the PBRF quality score, the correlation between the IR website inlinks and the PBRF quality score, and the correlation between the IR website impact factor and the PBRF quality score. The research also studied the different web ranking tools and tried to find out whether these tools can be used to measure the quality of IR documents. The research used Yahoo Site Explorer to collect information of inlinks and also use other tools to collect the webpage ranking. The finding of this research are that there is small correlation between the IR website impact factor and PBRF quality score, and the page ranking is not a good tool to exam the quality of IR document as a whole.</p>

Correlation of Impact Measures of Institutional Repositories and PBRF Ranking

<p>This study examines the correlation of website impact factor of institutional repositories (IR) of all eight universities in New Zealand and the Performance Based Research Fund (PBRF) quality score. The purpose of the research is to find out whether there is correlation between these two figures. The research problems are: the correlation between the IR homepage ranking and the PBRF quality score, the correlation between the IR website inlinks and the PBRF quality score, and the correlation between the IR website impact factor and the PBRF quality score. The research also studied the different web ranking tools and tried to find out whether these tools can be used to measure the quality of IR documents. The research used Yahoo Site Explorer to collect information of inlinks and also use other tools to collect the webpage ranking. The finding of this research are that there is small correlation between the IR website impact factor and PBRF quality score, and the page ranking is not a good tool to exam the quality of IR document as a whole.</p>

Web Page Ranking using Web Mining Techniques: A comprehensive survey

<div>Purpose: Due to the exponential growth of internet users and internet traffic, information seekers are highly dependent upon search engines to extract relevant information. Due to the accessibility of a large amount of textual, audio, video etc. contents, the responsibility of search engines has increased.</div><div>Design/methodology/approach: The search engine provides relevant information to internet users concerning to their query; based on content, link structure etc. However, it does not provide the guarantee of the correctness of the information. The performance of a search engine is highly dependent upon the ranking module. The performance of the ranking module is dependent upon the link structure of web pages, which analyse through Web structure mining (WSM) and their content, which analyses through Web content mining (WCM). Web mining plays an important role in computing the rank of web pages.</div><div>Findings: In this article, web mining types, techniques, tools, algorithms and their challenges are presented. Further, it provides a critical comprehensive survey for the researchers by presenting different features of web pages, which are important to check the quality of web pages.</div><div>Originality: In this work, authors presented different approaches/techniques, algorithms and evaluation approaches in previous researches and identified some critical issues in page ranking & web mining, which provide future directions for the researchers, working in the area.</div>

Web Page Ranking using Web Mining Techniques: A comprehensive survey

<div>Purpose: Due to the exponential growth of internet users and internet traffic, information seekers are highly dependent upon search engines to extract relevant information. Due to the accessibility of a large amount of textual, audio, video etc. contents, the responsibility of search engines has increased.</div><div>Design/methodology/approach: The search engine provides relevant information to internet users concerning to their query; based on content, link structure etc. However, it does not provide the guarantee of the correctness of the information. The performance of a search engine is highly dependent upon the ranking module. The performance of the ranking module is dependent upon the link structure of web pages, which analyse through Web structure mining (WSM) and their content, which analyses through Web content mining (WCM). Web mining plays an important role in computing the rank of web pages.</div><div>Findings: In this article, web mining types, techniques, tools, algorithms and their challenges are presented. Further, it provides a critical comprehensive survey for the researchers by presenting different features of web pages, which are important to check the quality of web pages.</div><div>Originality: In this work, authors presented different approaches/techniques, algorithms and evaluation approaches in previous researches and identified some critical issues in page ranking & web mining, which provide future directions for the researchers, working in the area.</div>

Web Pages Ranking Algorithms: A Survey

Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. In order to explore the internet, users depend on various search engines. Search engines face a significant challenge in returning the most relevant results for a user's query. The search engine's performance is determined by the algorithm used to rank web pages, which prioritizes the pages with the most relevancy to appear at the top of the result page. In this paper, various web page ranking algorithms such as Page Rank, Time Rank, EigenRumor, Distance Rank, SimRank, etc. are analyzed and compared based on some parameters, including the mining technique to which the algorithm belongs (for instance, Web Content Mining, Web Structure Mining, and Web Usage Mining), the methodology used for ranking web pages, time complexity (amount of time to run an algorithm), input parameters (parameters utilized in the ranking process such as InLink, OutLink, Tag name, Keyword, etc.), and the result relevancy to the user query.

Rank algorithm of web English educational resources based on fuzzy sets and RSS

In order to improve the acquisition and recommendation effect of English education resources, this paper proposes a Rank algorithm of web English educational resources based on fuzzy sets and RSS, and deeply studies the basic principles of the algorithm and introduces several keyword extraction techniques. The user’s browsing behavior and user interest acquisition methods are classified. Researchers can plan to further explore the page ranking algorithm to improve the performance of the scheme based on the damping factor. In addition, this paper uses Web technology to acquire English education resources and build a recommendation model, and uses crawler technology to build an overall system model. Finally, this paper designs experiments to verify the performance of the algorithm model constructed in this paper, and analyses the experimental results by mathematical statistics. The research results show that the algorithm model proposed in this paper has significant effects and is of great significance to the acquisition and recommendation of English education resources.

Host-Based Virtual Machine Workload Characterization Using Hypervisor Trace Mining

Cloud computing is a fast-growing technology that provides on-demand access to a pool of shared resources. This type of distributed and complex environment requires advanced resource management solutions that could model virtual machine (VM) behavior. Different workload measurements, such as CPU, memory, disk, and network usage, are usually derived from each VM to model resource utilization and group similar VMs. However, these course workload metrics require internal access to each VM with the available performance analysis toolkit, which is not feasible with many cloud environments privacy policies. In this article, we propose a non-intrusive host-based virtual machine workload characterization using hypervisor tracing. VM blockings duration, along with virtual interrupt injection rates, are derived as features to reveal multiple levels of resource intensiveness. In addition, the VM exit reason is considered, as well as the resource contention rate due to the host and other VMs. Moreover, the processes and threads preemption rates in each VM are extracted using the collected tracing logs. Our proposed approach further improves the selected features by exploiting a page ranking based algorithm to filter non-important processes running on each VM. Once the metric features are defined, a two-stage VM clustering technique is employed to perform both coarse- and fine-grain workload characterization. The inter-cluster and intra-cluster similarity metrics of the silhouette score is used to reveal distinct VM workload groups, as well as the ones with significant overlap. The proposed framework can provide a detailed vision of the underlying behavior of the running VMs. This can assist infrastructure administrators in efficient resource management, as well as root cause analysis.

BeeRank

There is an incredible change in the world wide web, and the users face difficulty in accessing the needed information as per their need. Different algorithms are devised at each step of the information retrieval process, and it is observed that ranking is one of the core ingredients of any search engine that plays a major role in arranging the information. In this regard, different measures are adopted for ranking the web pages by using content, structure, or log data. The BeeRank algorithm is proposed that provides quality results, which is inspired by the artificial bee colony algorithm for web page ranking and uses both the structural and content approach for calculating the rank value and provides better results. It also helps the users in finding the relevant web pages by minimizing the computational complexity of the process and achieves the result in minimum time duration. The working is illustrated and is compared with the traditional PageRank algorithm that incorporates only structural links, and the result shows an improvement in ranking and provides user-specific results.