This paper was submitted as part of the requirements and statement of interest for participation in the NSF funded NSDL Webmetrics Workshop in Aug. 2004. It documents GROW's experience with regards to development of webmetrics software and intention to include webmetrics strategies as a part of evaluation. GROWâ s evaluation strategy was articulated in conjunction with the library design and development framework (Budhu & Coleman, 2002). A digital library is a complex thing to evaluate and the â interactivesâ evaluation framework we proposed uses hybrid methods to study distinct layers and objects in the digital library (resource itself, the interface, the search engine, etc.) and understand users and evaluate educational impact. Our Interactives Evaluation strategy has been shared with users and stakeholders at various venues such as the Harvill conference and the NSDL Participant Interaction Digital Workshop, February 2004

A review of the literature reveals that web metrics is a complex topic that can be found under many different terms and phrases: e-metrics, web site traffic measurement, web usage, web mining, online consumer/usage behavior, are just a few. Data mining, web analytics, knowledge data discovery, informetrics (bibliometrics and web-o-metrics) and business analytics are also relevant. â Metricsâ are measures and â analyticsâ are measurements of information, processes and data analysis from processes but web analytics is also becoming synonymous for e-metrics and web metrics. â Verticalizationâ is one of the newest trends in business web analytics/metrics; this means not just web traffic logging and analysis but has been broadened to include understanding and predicting customer behavior and customer relationship management. â Personalizationâ is considered fundamental to improving customer motivation, satisfaction and retention. What is the potential of web metrics for understanding educational use of the NSDL and measuring educational impact? As Einstein said, â Not everything that can be counted counts, and not everything that counts can be counted.â What do we want to count for the NSDL how, and why? These are the questions that have motivated the creation of this bibliography. We hope it will be a useful starting point and reference document as we begin framing a plan of action for the NSDL in this important area. Status: This bibliography is a work in progress. When it is completed (target date: 08/30/04) it will be a selective, annotated bibliography on web metrics. Currently, the abstracts in this bibliography are often taken directly from the source articles or websites and are not annotations. Books and journals dealing with this topic are not yet included (with one exception); we plan to include at least other texts and journals in the final version. Acknowledgments: Chris Neuhaus jumpstarted this bibliography and Anita conducted a literature search in databases such as the ACM Digital Library besides editing it. In addition, we found the statements of the Webmetrics Workshop participants most helpful in preparing this bibliography and some of the references in the statements have been included here. We also acknowledge the labor of Shawn Nelson, SIRLS, University of Arizona, in locating the abstracts and articles/items listed in this bibliography. Your feedback and comments (especially critical comments and reader annotations about any of the items) will help to improve this selective, annotative bibliography and are greatly encouraged. Version: This is version 2 of the bibliography prepared by volunteers of the Educational Impact and Evaluation Standing Committee for the NSDL Webmetrics Workshop ( a joint workshop of the Technology Standing Committee and the EIESC), Aug. 2-3, Costa Mesa, CA. This version adds two tools mentioned at the workshop and includes citations to two papers that were distributed at the workshop as well. Version 1 of the bibliography, also a volunteer effort, was distributed as a paper copy to the 26 participants of the workshop. For details about the workshop, visit http://webmetrics.comm.nsdl.org/. This bibliography is being made available through DLIST, http://dlist.sir.arizona.edu/.

The explosive and continuous growth in the size and use of the World Wide Web is at the basis of the great interest into web usage mining techniques in both research and commercial areas. In particular, the need for predicting the user’s needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalization. In this thesis, we introduce a new framework that takes advantage of the sophisticated association rule finding web mining technology on both dynamic user activities over a web site, such as navigational behavior, and static information, such as user profiles and web content. We also provide a novel personalization selection system which allows users to choose the most suitable profile for them in any given period of time. In order to examine the viability of our framework, we incorporate and implement it over a well designed simulation environment. Moreover, our experiment proves that our framework provides an overall better web personalization service in terms of both recommendation accuracy and user satisfaction.

This thesis proposes a hybrid approach in using Google Distance and WordNet together in a new method, called the Domain Independent Quality of Service (DIQOS) method for QoS-enabled web services discovery. Comparisons, using delay, recall, and precision metrics, between this hybrid approach and an earlier lightweight Google Distance-only based approach for web services discovery are provided. Further, our performance evaluation demonstrates as of yet undocumented trade-offs between Google distance, Google-WordNet distance, and WordNet distance approaches for similarity matching in the web services discovery phase. The impact of all approaches on QoS-enabled web service composition is described for representative web transactions in the travel domain. Findings include that the recall of signature matching increases by 5 to 15% for WordNet-assisted Google Distance DIQOS approach over the pure Google-distance DIQOS variant. WordNet-assisted Google Distance also shows 20-40 % increases in recall for signature matching compared to the WordNet only approach. Also, bigram-based, short sentence, and WordNet-based vector optimizations in specification similarity matching show an average of 25% increase in recall over a previous competitive method called the Flexible Ontology-Independent QOS-enabled method (FOIQOS). Our WordNet-assisted Google Distance method shows 34% increase in recall compared to FOIQOS. Our approaches produce 11% lower precision than FOIQOS, but FOIQOS is speedier as delays are 5 to 15 % lower.

Zugl.: Diplomarbeit

Stuttgart, Univ., Diplomarbeit, 2008.

Stuttgart, Univ., Studienarbeit, 2007.

Diss. (sammanfattning) Stockholm : Kungliga Tekniska högskolan, 2008. / Härtill 7 uppsatser.

Thesis (M.A. in Security Studies (Homeland Security and Defense))--Naval Postgraduate School, September 2008. / Thesis Advisor(s): Bergin, Richard. "September 2008." Description based on title screen as viewed on November 4, 2008. Includes bibliographical references (p. 85-87). Also available in print.

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on May 7, 2009. "April 2009." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "Presented to the faculty of California Polytechnic State University, San Luis Obispo." Major professor: Aaron Keen, Ph. D. Includes bibliographical references (p. 102-103). Also available on microfiche.