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Journal articles on the topic 'Web metrics'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Belanger, France, Weiguo Fan, L. Christian Schaupp, et al. "Web site success metrics." Communications of the ACM 49, no. 12 (2006): 114–16. http://dx.doi.org/10.1145/1183236.1183256.

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2

Calero, Coral, Julián Ruiz, and Mario Piattini. "Classifying web metrics using the web quality model." Online Information Review 29, no. 3 (2005): 227–48. http://dx.doi.org/10.1108/14684520510607560.

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3

Dhyani, Devanshu, Wee Keong Ng, and Sourav S. Bhowmick. "A survey of Web metrics." ACM Computing Surveys 34, no. 4 (2002): 469–503. http://dx.doi.org/10.1145/592642.592645.

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4

Mendes, Emilia, Nile Mosley, and Steve Counsell. "Investigating Web size metrics for early Web cost estimation." Journal of Systems and Software 77, no. 2 (2005): 157–72. http://dx.doi.org/10.1016/j.jss.2004.08.034.

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5

Singh, Jaipuneet, and Simarpreet Singh. "Review of the Web Metrics Model." International Journal of Applied Research on Information Technology and Computing 4, no. 1 (2013): 55. http://dx.doi.org/10.5958/j.0975-8089.4.1.005.

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6

Chiang, I‐Ping, Chun‐Yao Huang, and Chien‐Wen Huang. "Traffic metrics and Web 2.0‐ness." Online Information Review 34, no. 1 (2010): 115–26. http://dx.doi.org/10.1108/14684521011024155.

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7

Domènech, Josep, José A. Gil, Julio Sahuquillo, and Ana Pont. "Web prefetching performance metrics: A survey." Performance Evaluation 63, no. 9-10 (2006): 988–1004. http://dx.doi.org/10.1016/j.peva.2005.11.001.

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8

Hölttä, Vesa, and Heikki Koivo. "Performance metrics for web-forming processes." Journal of Process Control 21, no. 6 (2011): 885–92. http://dx.doi.org/10.1016/j.jprocont.2011.03.008.

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9

Marchetto, Alessandro, Roberto Tiella, Paolo Tonella, Nadia Alshahwan, and Mark Harman. "Crawlability metrics for automated web testing." International Journal on Software Tools for Technology Transfer 13, no. 2 (2010): 131–49. http://dx.doi.org/10.1007/s10009-010-0177-3.

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10

Sathya, M., P. Dhavachelv, and G. Sureshkuma. "Web Service Categorization Using Structural Metrics." International Journal of Soft Computing 5, no. 4 (2010): 164–70. http://dx.doi.org/10.3923/ijscomp.2010.164.170.

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11

Thelwall, Mike. "Web use and peer interconnectivity metrics for academic web sites." Journal of Information Science 29, no. 1 (2003): 1–10. http://dx.doi.org/10.1177/016555150302900101.

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12

Riza, Ahmad, Mohamad Alif Irfan Anshori, Farrah Arrazy, and M. Ainul Yaqin. "Pengukuran Metrik Kompleksitas Web Service Sekolah." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 5, no. 1 (2020): 147. http://dx.doi.org/10.30645/jurasik.v5i1.179.

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The Complexity Metric Diagram is a method of measuring software complexity. This method uses Class Diagrams as test parameters. This study uses test data from previous research regarding the design of Information Systems accompanied by Class Diagrams with case studies of School Academic Information Systems. The facts found in the field that school software developers do not conduct application audits so it is not ripe to face problems that will arise in the future. The results of the calculation of the complexity metrics in the School Academic Information System produce an output of 993.32 uni

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13

Zahedi Nooghabi, Mahdi, and Akram Fathian Dastgerdi. "Proposed metrics for data accessibility in the context of linked open data." Program 50, no. 2 (2016): 184–94. http://dx.doi.org/10.1108/prog-01-2015-0007.

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Purpose – One of the most important categories in linked open data (LOD) quality models is “data accessibility.” The purpose of this paper is to propose some metrics and indicators for assessing data accessibility in LOD and the semantic web context. Design/methodology/approach – In this paper, at first the authors consider some data quality and LOD quality models to review proposed subcategories for data accessibility dimension in related texts. Then, based on goal question metric (GQM) approach, the authors specify the project goals, main issues and some questions. Finally, the authors propo

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14

Jayanthi, J., and M. Ezhilmathi. "Evaluating the Effectiveness of Web Search Metrics." Asian Journal of Computer Science and Technology 1, no. 2 (2012): 16–19. http://dx.doi.org/10.51983/ajcst-2012.1.2.1707.

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Software metrics are the key performance indicators, using which the performance of a system can be assessed quantitatively. Metrics can also be applied for personalized web search which can be used to retrieve relevant results for each individual user depending on their unique profile. Although personalized search based on user profile has been under research for many years and various metrics have been proposed, it is still uncertain whether personalization is unswervingly effective on different queries for different user profiles. We present a framework for personalized search which retriev

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15

Singh, Sukhjinder, Amit Makkar, and Navneet Singh. "A METRICS BASED APPROACH TO ANALYSE WEB USAGE PATTERN." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 1, no. 1 (2011): 1–6. http://dx.doi.org/10.24297/ijct.v1i1.6089.

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Web metrics are established goals and standards for measuring website performance. Web Analytics can be used to analyze and statistically process user and customer behavior. Web Analytics especially refers to the use of data collected from a Website to determine which aspects of the Website work towards the business objectives. This paper provides a web metrics based approach that can be used to analyze and improve web usage pattern. We define a set of 15 web metrics that can play an important role in understanding web visitors behavior and provide suggestion how these metrics can help in maki

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16

BASCI, D., and S. MISRA. "Data Complexity Metrics for XML Web Services." Advances in Electrical and Computer Engineering 9, no. 2 (2009): 9–15. http://dx.doi.org/10.4316/aece.2009.02002.

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17

B.Senousy, M., and Anthony Nosshi Shaker. "Experimental & Analytical Evaluation of Web Metrics." مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات 12, no. 12 (2012): 4–13. http://dx.doi.org/10.21608/jstc.2012.120070.

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18

Jayanthi, J., M. Ezhilmathi, and S. Rathi. "Evaluating the Effectiveness of Web Search Metrics." International Journal of Applied Information Systems 4, no. 10 (2012): 28–35. http://dx.doi.org/10.5120/ijais12-450818.

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19

Brogan, Mark. "Web Metrics for Library and Information Professionals." Archives and Manuscripts 43, no. 1 (2015): 74–76. http://dx.doi.org/10.1080/01576895.2014.1000808.

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20

Palmer, Jonathan W. "Web Site Usability, Design, and Performance Metrics." Information Systems Research 13, no. 2 (2002): 151–67. http://dx.doi.org/10.1287/isre.13.2.151.88.

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21

Kruesi, Lisa. "Web Metrics for Library and Information Professionals." Australian Academic & Research Libraries 46, no. 2 (2015): 143–44. http://dx.doi.org/10.1080/00048623.2015.1031627.

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22

Pooley, R., D. Senior, and D. Christie. "Collecting and analyzing Web-based project metrics." IEEE Software 19, no. 1 (2002): 52–58. http://dx.doi.org/10.1109/52.976941.

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23

Liao, Lili, Guohua Shen, Zhiqiu Huang, and Fei Wang. "Cohesion Metrics for Evaluating Semantic Web Ontologies." International Journal of Hybrid Information Technology 9, no. 11 (2016): 369–80. http://dx.doi.org/10.14257/ijhit.2016.9.11.31.

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24

Mendes, E., N. Mosley, and S. Counsell. "Web metrics - estimating design and authoring effort." IEEE Multimedia 8, no. 1 (2001): 50–57. http://dx.doi.org/10.1109/93.923953.

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25

Jaillet, Hélène F. "Web metrics: measuring patterns in online shopping." Journal of Consumer Behaviour 2, no. 4 (2003): 369–81. http://dx.doi.org/10.1002/cb.114.

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26

Lamot, Kenza, Steve Paulussen, and Peter Van Aelst. "Do Metrics Drive News Decisions? Political News Journalists’ Exposure and Attitudes Toward Web Analytics." Electronic News 15, no. 1-2 (2021): 3–20. http://dx.doi.org/10.1177/19312431211018141.

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As newsrooms are increasingly using web analytics to monitor news behavior, journalism is likely to become increasingly “metrics-driven.” Research suggests that analytics are commonly used by web editors to decide on the distribution and promotion of news stories, but how does this affect the news practices of journalists? To what extent are audience metrics taken into account by individual journalists and reporters who work in a specific news beat? This paper explores this question through a survey of political journalists in Belgium. The study examines the level of access that political news

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27

Bora, Abhijit, and Tulshi Bezboruah. "Some Aspects of Implementation of Web Services in Load Balancing Cluster-Based Web Server." International Journal of Information Retrieval Research 10, no. 1 (2020): 48–72. http://dx.doi.org/10.4018/ijirr.2020010104.

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We have designed, developed, and implemented SOAP-based web services in load balancing cluster-based web server and carried out load testing over the system. The roles of web services, such as client, broker, and service provider are segregated among different services. The system is monitored through a load testing tool, Mercury LoadRunner. The recorded system metrics are evaluated to study the overall performance and reliability aspects against different massive level of users. This article presents in detail the system architecture, testing methodology, and recorded system metrics. The stat

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28

Daugherty, Alan, Robert A. Hegele, Hong S. Lu, Nigel Mackman, Daniel J. Rader, and Christian Weber. "Web of Science’s Citation Median Metrics Overcome the Major Constraints of the Journal Impact Factor." Arteriosclerosis, Thrombosis, and Vascular Biology 42, no. 4 (2022): 367–71. http://dx.doi.org/10.1161/atvbaha.122.317426.

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There are many metrics to evaluate the performance and status of journals. Among these, the journal impact factor (JIF) has become the dominant metric. The influence of JIF is illustrated by its widespread use to evaluate academic status, compensation, and funding decisions. However, as noted by Clarivate Analytics, the parent company of the Web of Science (WoS), the JIF should not be used without careful attention to the many phenomena that influence citation rates. To facilitate transparency, Clarivate Analytics provides all data used to determine the JIF. In addition, WoS provides other met

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29

Bekavac, Ivan, and Daniela Garbin Praničević. "Web analytics tools and web metrics tools: An overview and comparative analysis." Croatian Operational Research Review 6, no. 2 (2015): 373–86. http://dx.doi.org/10.17535/crorr.2015.0029.

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30

Malhotra, Ruchika, and Anjali Sharma. "Quantitative evaluation of web metrics for automatic genre classification of web pages." International Journal of System Assurance Engineering and Management 8, S2 (2017): 1567–79. http://dx.doi.org/10.1007/s13198-017-0629-1.

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31

Wang, Juan, Xinyue Qi, Bo Cui, and Maozu Guo. "A Survey of Metrics Measuring Difference for Rooted Phylogenetic Trees." Current Bioinformatics 15, no. 7 (2020): 697–702. http://dx.doi.org/10.2174/1574893614666191017130217.

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Background: The evolutionary history of organisms can be described by phylogenetic trees. We need to compare the topologies of rooted phylogenetic trees when researching the evolution of a given set of species. Objective: Up to now, there are several metrics measuring the dissimilarity between rooted phylogenetic trees, and those metrics are defined by different ways. Methods: This paper analyzes those metrics from their definitions and the distance values computed by those metrics by terms of experiments. Results: The results of experiments show that the distances calculated by the cluster me

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32

Delgado-López-Cózar, Emilio, and Rafael Repiso-Caballero. "The impact of scientific journals of communication: Comparing Google Scholar Metrics, Web of Science and Scopus." Comunicar 21, no. 41 (2013): 45–52. http://dx.doi.org/10.3916/c41-2013-04.

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Google Scholar Metrics' launch in April 2012, a new bibliometric tool for the evaluation of scientific journals by means of citation counting, has ended with the duopoly exerted by the Web of Science and Scopus databases. This paper aims at comparing the coverage of these three databases and the similarity their journal rankings may have. We selected a sample of journals from the field of Communication Studies indexed in the three databases. Data was recollected on 1720 November, 2012. 277 journals were identified to which we calculated their hindex and ranked them according to such indicator.

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33

Vu, Hong Tien. "The online audience as gatekeeper: The influence of reader metrics on news editorial selection." Journalism 15, no. 8 (2013): 1094–110. http://dx.doi.org/10.1177/1464884913504259.

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This study provides a snapshot of the hierarchy-of-influences model in the new media environment through examining the effects of audience web metrics on editors. Surveying 318 gatekeepers, the study found that audience metrics influence editors in gatekeeping. Editors’ likelihood to monitor web metrics is affected by their journalism training. Gatekeepers who attach the importance of high readership to economic benefits are more likely to have different news decisions based on web metrics. The study suggests a revision of the hierarchy-of-influences model with more emphasis being placed on th

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34

Bezboruah, Tulshi, and Abhijit Bora. "Performance Evaluation of Hierarchical SOAP Based Web Service in Load Balancing Cluster-Based and Non-Cluster-Based Web Server." International Journal of Information Retrieval Research 5, no. 4 (2015): 19–30. http://dx.doi.org/10.4018/ijirr.2015100102.

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Evaluating the implementation techniques of web service and analysing their performance in load balancing cluster-based and non-cluster-based web server is necessary from the perspective of web service developer, researchers as well as users. As such the authors propose to develop and implement a SOAP based hierarchical web service using load balancing cluster-based and non-cluster based Apache Tomcat web server to study the web service performance metrics. The performance of overall system is tested using the load testing tool Mercury Load runner and a comparative investigation is carried out

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35

ZENG, Yi, Xiao-wei HU, and Juan LI. "Web software complexity metrics based on projection pursuit." Journal of Computer Applications 32, no. 3 (2013): 827–30. http://dx.doi.org/10.3724/sp.j.1087.2012.00827.

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36

Tripathi, Sandesh, Abbas, and Rizwan Beg. "Availability metrics: under controlled environments for web services." International Journal on Web Service Computing 2, no. 3 (2011): 1–9. http://dx.doi.org/10.5121/ijwsc.2011.2301.

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37

Davlatov, Sh R., and P. V. Kuchinsky,. "Web resource security analysis based on CVSS metrics." Informatics 17, no. 3 (2020): 72–77. http://dx.doi.org/10.37661/1816-0301-2020-17-3-72-77.

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Based on the analysis of vulnerability data for web resources and the CVSS metric, the distribution of the average CVSS (Common Vulnerability Scoring System standard for calculating a numerical vulnerability score on a ten-point scale) score for the websites of theRepublicofBelaruswas studied. The hypothesis on the distribution of the CVSS vulnerability assessment according to Poisson's law was tested by chi-square criteria. It was found that about 10% of web resources from the original general of samples of 19000 size have a critical averaged assessment level of vulnerability. As part of this

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38

Layton, Astrid, Bert Bras, and Marc Weissburg. "Designing Industrial Networks Using Ecological Food Web Metrics." Environmental Science & Technology 50, no. 20 (2016): 11243–52. http://dx.doi.org/10.1021/acs.est.6b03066.

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39

Koutlis, Christos, Manos Schinas, Symeon Papadopoulos, and Ioannis Kompatsiaris. "GAP: Geometric Aggregation of Popularity Metrics." Information 11, no. 6 (2020): 323. http://dx.doi.org/10.3390/info11060323.

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Estimating and analyzing the popularity of an entity is an important task for professionals in several areas, e.g., music, social media, and cinema. Furthermore, the ample availability of online data should enhance our insights into the collective consumer behavior. However, effectively modeling popularity and integrating diverse data sources are very challenging problems with no consensus on the optimal approach to tackle them. To this end, we propose a non-linear method for popularity metric aggregation based on geometrical shapes derived from the individual metrics’ values, termed Geometric

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40

Liang, Qianhui, Anandhi Bharadwaj, and Bu Sung Lee. "Interactive and Iterative Service-Composition-Based Approach to Flexible Information System Development." International Journal of Web Services Research 8, no. 4 (2011): 81–107. http://dx.doi.org/10.4018/jwsr.2011100104.

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An emerging class of technologies defined as Service-Oriented Architecture (SOA) has been heralded as the answer for inflexible IT architecture and promises to reduce operational barriers of current IT infrastructures. In SOA, loosely coupled Web services are integrated to provide dynamic digital capabilities within and across enterprise boundaries. Little research exists on development processes of information systems using Web services and against certain development metrics. One way to perform such research is to propose a development approach, identify the metrics, and embed the metrics in

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41

Cho, Jae Han, Jae Min Park, Tae Hyeop Kim, Seung Wook Lee, and Jiyeon Kim. "Machine Learning-based Detection of HTTP DoS Attacks for Cloud Web Applications." Korean Institute of Smart Media 12, no. 2 (2023): 66–75. http://dx.doi.org/10.30693/smj.2023.12.2.66.

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Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are diff

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42

Balaji, B., and M. Dhanamjaya. "Preprints in Scholarly Communication: Re-Imagining Metrics and Infrastructures." Publications 7, no. 1 (2019): 6. http://dx.doi.org/10.3390/publications7010006.

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Digital scholarship and electronic publishing within scholarly communities change when metrics and open infrastructures take center stage for measuring research impact. In scholarly communication, the growth of preprint repositories as a new model of scholarly publishing over the last three decades has been one of the major developments. As it unfolds, the landscape of scholarly communication is transitioning—with much being privatized as it is made open—and turning towards alternative metrics, such as social media attention, author-level, and article-level metrics. Moreover, the granularity o

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43

Burghardt, Kyle J., Bradley H. Howlett, Audrey S. Khoury, Stephanie M. Fern, and Paul R. Burghardt. "Three Commonly Utilized Scholarly Databases and a Social Network Site Provide Different, But Related, Metrics of Pharmacy Faculty Publication." Publications 8, no. 2 (2020): 18. http://dx.doi.org/10.3390/publications8020018.

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Scholarly productivity is a critical component of pharmacy faculty effort and is used for promotion and tenure decisions. Several databases are available to measure scholarly productivity; however, comparisons amongst these databases are lacking for pharmacy faculty. The objective of this work was to compare scholarly metrics from three commonly utilized databases and a social networking site focused on data from research-intensive colleges of pharmacy and to identify factors associated with database differences. Scholarly metrics were obtained from Scopus, Web of Science, Google Scholar, and

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44

Scott, S. D., and Y. H. Koh. "Design Metrics and the Adaptation of Web-Page Content Chunks for PDAs." Journal of IT in Asia 1, no. 1 (2017): 35–51. http://dx.doi.org/10.33736/jita.404.2005.

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The majority of web-pages are unsuitable for viewing on PDAs, WAP phones and similar devices without first being adapted. However, little empirical work has been done on what actually constitutes a good PDA or WAP web-page. This paper ranks a number of PDA web-pages from different categories empirically and correlates the result against the design metrics present. The findings are then compared against a similar set of experiments for PC web-pages. The results of this comparison suggest that, as well as omitting, summarizing and converting individual multimedia objects in the web-page to a les

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45

Udartseva, O. M., and A. E. Rykhtorova. "Using web analytics tools to assess the effectiveness of means for promoting library resources." Bibliosphere, no. 2 (June 30, 2018): 93–99. http://dx.doi.org/10.20913/1815-3186-2018-2-93-99.

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This article is devoted to analysis of the technologies effectiveness to promote library resources. The first part explores using web analytics and its tools in the library. The study of existing approaches to choose web metrics allows identifying the following key metrics to assess the effectiveness of the library web site operation: (1) attendance metrics; (2) the metrics of involvement and loyalty; (3) social metrics. The techniques effectiveness direct evaluation is proposed to promote library resources based on the analysis of: (1) measurable targeted actions by users on the site; (2) tra

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46

Dooleya, Jennifer Allyson, Don Iversona, and Sandra C. Jonesa. "Are There Good Metrics for Evaluating Web 2.0 Campaigns?" Journal of Social Media Studies 1, no. 2 (2015): 83–90. http://dx.doi.org/10.15340/2147336612783.

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47

Selvi, R. Thirumalai, Adham Sheriff A, George T. Manohar, and Balasubramanian N.V. "Problem and Solution Oriented Metrics for Web Based Applications." i-manager's Journal on Software Engineering 2, no. 1 (2007): 1–5. http://dx.doi.org/10.26634/jse.2.1.556.

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48

John, Biju, Souheil Khaddaj, and Andreas Hoppe. "Software Metrics for the Evaluation of Semantic Web Applications." Journal of Algorithms & Computational Technology 4, no. 4 (2010): 511–22. http://dx.doi.org/10.1260/1748-3018.4.4.511.

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49

Dikaiakos, Marios D., Athena Stassopoulou, and Loizos Papageorgiou. "An investigation of web crawler behavior: characterization and metrics." Computer Communications 28, no. 8 (2005): 880–97. http://dx.doi.org/10.1016/j.comcom.2005.01.003.

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50

Malhotra, Ruchika, and Anjali Sharma. "Estimating the threshold of software metrics for web applications." International Journal of System Assurance Engineering and Management 10, no. 1 (2019): 110–25. http://dx.doi.org/10.1007/s13198-019-00773-1.

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