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The purpose of this paper is to classify the most important metrics proposed for web information systems, with the aim of offering the user a global vision of the state of the research within this area.

WQM distinguishes three dimensions related to web features, lifecycle processes and quality characteristics. A range of recently published (1992‐2004) works that include web metrics definitions have been studied and classified within this model.

In this work, a global vision of web metrics is provided. Concretely, it was found that about 44 percent of metrics are related to “presentation” and that most metrics (48 percent) are usability metrics. Regarding the life cycle, the majority of metrics are related to operation and maintenance processes. Nevertheless, focusing on metrics validation, it was found that there is not too much work done, with only 3 percent of metrics validated theoretically and 37 percent of metrics validated empirically.

The classification presented tries to facilitate the use and application of web metrics for different kinds of stakeholders (developers, maintainers, etc.) as well as to clarify where web metric definition efforts are centred, and thus where it is necessary to focus future works.

This work tries to cover a deficiency in the web metrics field, where many proposals have been stated but without any kind of rigour and order. Consequently, the application of the proposed metrics is difficult and risky, and it is dangerous to base decisions on their values.

The purpose of this paper is to present a thorough analysis of three complementary features of real-scale really simple syndication (RSS)/Atom feeds, namely, publication activity, items characteristics and their textual vocabulary, that the authors believe are crucial for emerging Web 2.0 applications. Previous works on RSS/Atom statistical characteristics do not provide a precise and updated characterization of feeds’ behavior and content, characterization that can be used to successfully benchmark the effectiveness and efficiency of various Web syndication processing/analysis techniques.

The authors empirical study relies on a large-scale testbed acquired over an eight-month campaign from 2010. They collected a total number of 10,794,285 items originating from 8,155 productive feeds. The authors deeply analyze feeds productivity (types and bandwidth), content (XML, text and duplicates) and textual content (vocabulary and buzz-words).

The findings of the study are as follows: 17 per cent of feeds produce 97 per cent of the items; a formal characterization of feeds publication rate conducted by using a modified power law; most popular textual elements are the title and description, with the average size of 52 terms; cumulative item size follows a lognormal distribution, varying greatly with feeds type; 47 per cent of the feed-published items share the same description; the vocabulary does not belong to Wordnet terms (4 per cent); characterization of vocabulary growth using Heaps’ laws and the number of occurrences by a stretched exponential distribution conducted; and ranking of terms does not significantly vary for frequent terms.

Modeling dedicated Web applications capacities, Defining benchmarks, optimizing Publish/Subscribe index structures.

It especially opens many possibilities for tuning Web applications, like an RSS crawler designed with a resource allocator and a refreshing strategy based on the Gini values and evolution to predict bursts for each feed, according to their category and class for targeted feeds; an indexing structure which matches textual items’ content, which takes into account item size according to targeted feeds, size of the vocabulary and term occurrences, updates of the vocabulary and evolution of term ranks, typos and misspelling correction; filtering by pruning items for content duplicates of different feeds and correlation of terms to easily detect replicates.

A content-oriented analysis of dynamic Web information.

Effective and efficient software security inspection is crucial as the existence of vulnerabilities represents severe risks to software users. The purpose of this paper is to empirically evaluate the potential application of Stochastic Gradient Boosting Trees (SGBT) as a novel model for enhanced prediction of vulnerable Web components compared to common, popular and recent machine learning models.

An empirical study was conducted where the SGBT and 16 other prediction models have been trained, optimized and cross validated using vulnerability data sets from multiple versions of two open-source Web applications written in PHP. The prediction performance of these models have been evaluated and compared based on accuracy, precision, recall and F-measure.

The results indicate that the SGBT models offer improved prediction over the other 16 models and thus are more effective and reliable in predicting vulnerable Web components.

This paper proposed a novel application of SGBT for enhanced prediction of vulnerable Web components and showed its effectiveness.

The performance of the World Wide Web is evaluated as an information resource in a specific information domain. The theoretical framework underpinning this approach recognises the contribution of information‐seeking behaviour from the discipline of information science and models of information systems success from the discipline of information systems in explaining World Wide Web usage as an information resource. A model integrating the construct of uncertainty and the task‐technology fit model is presented. A questionnaire‐based empirical study is used to test this integrated approach. Our results confirm that richer models representing the broad context of World Wide Web usage are required to evaluate usage of the World Wide Web as an information resource. Usage for travel tasks, uncertainty reduction, the Web as an information resource, and mediation all have a significant impact on users' perception of performance, explaining 46 per cent of the variance.

The purpose of this paper is to consider the method engineering perspective for service-oriented system engineering (SOSE). A number of SOSE methods have been proposed in both academia and industry. Given this, many intuitive, common questions arise. To answer these questions, many comparison frameworks have been developed. Each of which has considered certain methodological perspectives. However, less attention has been given to the method engineering (ME) perspective. The authors argue that this perspective would answer the question “what ME to apply in order to produce SOSE methods that themselves produce quality services at different levels of abstraction and SBAs”. This research question is further decomposed into other questions; the main one is “whether the existing ME do apply to service orientation”. Answering such a question would lead to either developing SOSE methods by using the existing ME or developing a new ME or framework for the specifics of SOSE.

This work first provides a literature review on ME approaches and techniques; then, it compares a sample of existing SOSE methods with respect to their ME within a comparison framework that comprises a set of relevant properties of a solution that would be provided by an SOSE method, namely, service-oriented architecture (SOA) adoption, quality of services and ME; and finally, it discusses the applicability of the existing ME to SOSE.

Strengths and weaknesses of the existing methods with respect to the aforementioned criteria, in addition to SOSE methodology open issues, were identified. The comparison has shown that while the existing SOSE methods have proved their success in a specific task, they still present some weaknesses. Therefore, it is better to benefit from the advantages of the existing ME techniques, notable method fragments, even if they need some alteration.

While this work has many open issues related to SOSE methods with respect to ME, it could be further developed in many directions by exploring the open issues. For instance, the generation of a new ME technique and application of this new ME technique to the existing SOSE methods to see to what extent the existing methods may be situational.

This work has practical implications, as it provides a better understanding of different views of SOSE methods, and assists the method engineers in deciding which ME technique is most suitable to their situation.

The produced artifact provides a research roadmap toward SOSE ME.

None of the existing comparison frameworks for SOSE methods has considered the criteria such as SOA adoption and ME techniques. Indeed, ME techniques and approaches would allow better reuse of the existing proven fragments of methods.

Interest is currently growing in open social learner modeling (OSLM), which means making peer models and a learner's own model visible to encourage users in e-learning. The purpose of this study is to examine students' views about the OSLM in an e-learning system.

This case study was conducted with 40 undergraduate students enrolled in advanced programming and database management system courses. A Likert-type questionnaire and open-ended questions were used to obtain the students' views. System usage data were also analyzed to ensure the richness and diversity of the overall data set.

The quantitative data of the students' views were analyzed with descriptive statistics; the results are presented as graphics. The qualitative data of the students' views were examined by content analysis to derive themes. These themes are organized into four subtopics: the students' positive views, their negative views, their improvement suggestions and their preferences about using similar OSLM visualizations in other e-learning systems. The students' subjective views are discussed in the context of their recorded interactions with the system.

Competition due to seeing peer models was considered by participants both as positive and negative features of the learning system. So, this study revealed that, the ways to combine peer learner models to e-learning systems that promote positive competition without resulting social pressure, still need to be explored.

By combining open learner models with open peer models, OSLM enhances the learning process in three different ways: it supports self-regulation, encourages competition and empowers self-evaluation. To take advantage of these positive contributions, practitioners should consider enhancing e-learning systems with both own learner and peer model features.

Despite increasing interest in OSLM studies, several limitations and problems must be addressed such as sparsity of data and lack of study of different contexts and cultures. To date, no published study in this area exists in Turkey. The purpose of this study is to fill this gap by examining OSLM features in an e-learning system from the perspectives of Turkish students by using both their system interaction data and their subjective views.

This paper aims to present a detailed description of Tukuchiy, a framework to dynamically generate adapted user interfaces. Tukuchiy is based on Runa-Kamachiy, a conceptual integration model that combines human–computer interaction (HCI) standards to create user interfaces with user-centered concepts usually addressed by adaptation.

The first step was the definition of three profiles: user, context and interface. These profiles contain information, such as user disabilities, location characteristics (e.g. illumination) and preferences (e.g. interface color or type of system help). The next step is to define the rules that ensure usability for different users. All of this information is used to create the Tukuchiy framework, which generates dynamic user interfaces, based on the specified rules. The last step is the validation through a prototype called Idukay. This prototype uses Tukuchiy to provide e-learning services. The functionality and usability of the system was evaluated by five experts.

To validate the approach, a prototype of Tukuchiy, called Idukay, was created. Idukay was evaluated by experts in education, computing and HCI, who based their evaluation in the system usability scale (SUS), a standard usability test. According to them, the prototype complies with the usability criteria addressed by Tukuchiy.

This work was tested in an academic environment and was validated by different experts. Further tests in a production environment are required to fully validate the approach.

Tukuchiy generates adapted user interfaces based on user and context profiles. Tukuchiy uses HCI standards to ensure usability of interfaces that dynamically change during execution time. The interfaces generated by Tukuchiy adapt to context, functionality, disabilities (e.g. color blindness) and preferences (usage and presentation) of the user. Tukuchiy enforces specific HCI standards for color utilization, button size and grouping, etc., during execution.

The purpose of this study is to examine the predictors of high‐quality answers in a community‐driven question answering service (Yahoo! Answers).

The identified predictors were organised into two categories: social and content features. Social features refer to the community aspects of the users and are extracted from explicit user interaction and feedback. Content features refer to the intrinsic and extrinsic content quality of answers that could be used to select the high‐quality answers. In total the framework built in this study comprises 17 features from two categories. Based on a randomly selected dataset of 1,600 question‐answer pairs from Yahoo! Answers, high‐quality answer predictors were identified.

The results of the analysis showed the importance of content appraisal features over social and textual content features. The features identified as strongly associated with high‐quality answers include positive votes, completeness, presentation, reliability and accuracy. Features weakly associated with high‐quality answers were high frequency words, answer length, and best answers answered. Features related to the asker's user history were found not to be associated with high‐quality answers.

This work could help in the reuse of answers for new questions. The study identified features that most influence the selection of high‐quality answers. Hence they could be used to select high‐quality answers for answering similar questions posed by users in the future. When a new question is posed, similar questions are first identified, and the answers for these questions are extracted and routed to the proposed quality framework for identifying high‐quality answers. Based on the overall quality index computed, the high‐quality answer could be returned to the asker.

Previous studies in identifying high‐quality answers were conducted using either of two approaches. First using social and textual content features found in community‐driven question answering services and second using content appraisal features by thorough assessment of answer quality provided by experts. However no study had integrated both approaches. Hence this study addresses this gap by developing an integrated generalisable framework to identify features that influence high‐quality answers.

The purpose of this paper is to test the hypothesis of whether more complex and emergent hashtags can be sufficient pointers to climate change events. Human-induced climate change is one of this century’s greatest unbalancing forces to have affected our planet. Capturing the public awareness of climate change on Twitter has proven to be significant. In a previous research, it was demonstrated by the authors that public awareness is prominently expressed in the form of hashtags that uses more than one bigram (i.e. a climate change term). The research finding showed that this awareness is expressed by more complex terms (e.g. “climate change”). It was learned that the awareness was dominantly expressed using the hashtag: #ClimateChange.

The methods demonstrated here use objective computational approaches [i.e. Google’s ranking algorithm and Information Retrieval measures (e.g. TFIDF)] to detect and rank the emerging events.

The results shows a clear significant evidence for the events signaled using emergent hashtags and how globally influential they are. The research detected the Earth Day, 2015, which was signaled using the hashtag #EarthDay. Clearly, this is a day that is globally observed by the worldwide population.

It was proven that these computational methods eliminate the subjectivity errors associated with humans and provide inexpensive solution for event detection on Twitter. Indeed, the approach used here can also be applicable to other types of event detections, beyond climate change, and surely applicable to other social media platforms that support the use of hashtags (e.g. Facebook). The paper explains, in great detail, the methods and all the numerous events detected.

Companies operating on the internet need appropriate metrics to make strategic marketing decisions. This paper applies established qualitative research methods to the online environment to evaluate how web managers generate and incorporate web metrics to inform strategic marketing decisions.

Initial theories were developed using a comprehensive literature review as well as exploratory interviews with New Zealand companies. Applying a mixed methodology, the exploratory research used interviews to assess current practice within the industry, refine the research questions and set up the research design. An in‐depth case study in the USA evaluated best practices and highlighted issues that affect the use of web metrics. The main data collection utilized case studies to generate the in‐depth information necessary for theory building.

The exploratory results showed that companies currently measure web site performance and consumer behaviour online but are still uncertain how best to use those metrics to inform strategic marketing decisions. The in‐depth case study showed how web metrics can be used when sufficient resources are available and measuring performance is a priority. Owing to the initially recognized low level of web metrics use, the main research was expanded purposively to selected participants who make greater use of web metrics.

This paper applies traditional qualitative research methods to the online environment. Analysis of the case studies and continued research will address the research gap and provide recommendations to web managers as well as attempt to illustrate best practices, solutions to issues and industry benchmarks.