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This paper aims to propose a service-oriented framework for performing content annotation and search, adapted to the task context. Media production workflows are becoming increasingly distributed and heterogeneous.The tasks of professionals in media production can be supported by automatic content analysis and search and retrieval services.

The processes of the framework are derived top-down, starting from business goals and scenarios in audiovisual media production. Formal models of tasks in the production workflow are defined, and business processes are derived from the task models. A software framework enabling the orchestrated execution of these models is developed.

This paper presents a framework that implements the proposed approach called Metadata Production Management Framework (MPMF). The authors show how a media production workflow for a real-world scenario is implemented using the MPMF.

The authors have demonstrated the feasibility of a model-based approach for media processing. In the reification step, there is still information that needs to be provided in addition to the task models to obtain executable processes. Future research should target the further automation of this process.

By means of this approach, the implementation of the business process defines the workflow, whereas the services that are actually used are defined by the configuration. Thus, the processes are stable and, at the same time, the services can be managed very flexibly. If necessary, service implementations can also be completely replaced by others without changing the business process implementation.

The authors introduce a model-based approach to media processing and define a reification process from business-driven task models to executable workflows. This enables a more task-oriented design of media processing workflows and adaptive use of automatic information extraction tools.

The current research presents a 14-week experience of developing socially orientated narratives in a digital mode, which involved 60 female Saudi English as a foreign language (EFL) learners. Initially working together, they were later divided into groups of approximately eight members each. The purpose of this research is to determine the benefits and challenges facing EFL leaners who are engaged with socially orientated digital-storytelling tasks. It also explores the nature of creating language-related socially orientated digital-storytelling projects and the main features associated with such projects. This practice targets the use of a new form of educational technologies that promotes educators’ pedagogical strategies, as well as their social needs, by sharing learners’ personal thoughts with others and cooperating and coordinating with other team members.

The current research has been designed in line with qualitative analysis. A qualitative analysis approach was chosen as the study seeks to gain further understanding about the issue of socially orientated digital storytelling among EFL learners in Saudi Arabia. The two main research methods used for conducting this research were semi-structured interviews and analysis of the content produced by the participants. Both methods were selected to enable the participants to express their personal thoughts and feedback directly to the researcher.

The findings have shown several benefits of this method, as well as highlighted the challenges regarding the application of such a practice in English language classes at university. The findings have indicated that such a novel educational atmosphere would result in the role of social orientation as a culture for enhancing learners’ competence and willingness to share a co-learning experience being regarded more highly. In addition, the results have revealed how such group work can be constructed and the main aspects of content that exist in the digital stories produced.

One limitation of the current research is that it only included a group of female EFL students. Therefore, it is recommended that the same research be conducted on male EFL students in Saudi Arabia so that a comparative analysis can be made regarding the effects of socially orientated digital storytelling on both genders. In addition, it is recommended that the research be carried out among more classes containing female and male EFL students to be able to analyse the data quantitatively. Lastly, there remains the issue of convincing administrators, parents and instructors opposed to these ideas to implement this kind of project in formal language education, which is often independently orientated.

The effect of such a practice is the improvements made to learners’ academic and digital literacies. Specifically, students’ academic-writing skills and abilities to tell stories are essential parts of this process that can be improved by learners during the online storytelling process.

The research presents an application of a promising pedagogy that integrates digital technologies into different learning settings, including the context of learning English as a foreign language.

Using Internet Archive's Wayback Machine, higher education web sites were retrospectively analyzed to study the effects that technological advances in web design have had on accessibility for persons with disabilities.

A convenience sample of higher education web sites was studied for years 1997‐2002. The homepage and pages 1‐level down were evaluated. Web accessibility barrier (WAB) and complexity scores were calculated. Repeated measures analysis of variance (ANOVA) was used to determine trends in the data and Pearson's correlation (r) was computed to evaluate the relationship between accessibility and complexity.

Higher education web sites become progressively inaccessible as complexity increases.

The WAB score is a proxy of web accessibility. While the WAB score can give an indication of the accessibility of a web site, it cannot differentiate between barriers posing minimal limitations and those posing absolute inaccessibility. A future study is planned to have users with disabilities examine web sites with differing WAB scores to correlate how well the WAB score is gauging accessibility of web sites from the perspective of the user.

Findings from studies such as this can lead to improved guidelines, policies, and overall awareness of web accessibility for persons with disabilities.

There are limited studies that have taken a longitudinal look at the accessibility of web sites and explored the reasons for the trend of decreasing accessibility.

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

The purpose of this paper is to review the use of adaptive systems in education. It is intended to be a useful introduction for the non-specialist reader.

A distinction is made between intelligent tutoring systems (ITSs) and adaptive hypermedia systems (AHSs). The two kinds of system are defined, compared and contrasted. Examples of the implementation of the two kinds of system are included.

Similarities and differences between the two kinds of system are highlighted. A conceptual unification is proposed based on the architecture of Course Assembly System and Tutorial Environment, a seminal prototype learning environment developed by Pask and Scott in the 1970s as an application of Pask’s conversation theory.

The architecture shows how the key aspects of ITSs and AHSs can be combined to complement each other. It is intended to be an original contribution that is of particular interest for the specialist reader.

This paper aims to present Long4Cloud (long-running workflows execution environment for cloud), a distributed and adaptive LRW execution environment delivered “as a service” solution.

LRWs last for hours, days or even months and their duration open the possibility of changes in business rules, service interruptions or even alterations of formal regulations of the business before the workflow completion. These events can lead to problems such as loss of intermediary results or exhaustion of computational resources used to manage the workflow execution. Existing solutions face those problems by merely allowing the replacement (at runtime) of services associated with activities of the LRW.

LONG4Cloud extends the previous works in two main aspects, namely, the inclusion of dynamic reconfiguration capabilities and the adoption of an “as a service” delivery mode. The reconfiguration mechanism uses quiescence principles, data and state management and provides multiple adaptive strategies. Long4Cloud also adopts a scenario-based analysis to decide the adaptation to be performed. Events such as changes in business rules or service failures trigger reconfigurations supported by the environment. These features have been put together in a solution delivered “as a service” that takes advantage of cloud elasticity and allows to better allocate cloud resources to fit into the demands of LRWs.

The original contribution of Long4Cloud is to incorporate adaptive capabilities into the LRW execution environment as an effective way to handle the specificities of this kind of workflow. Experiments using current data of a Brazilian health insurance company were carried out to evaluate Long4Cloud and show performance gains in the execution of LRWs submitted to the proposed environment.

There is a growing demand worldwide for the adoption of Learning management systems (LMS) by academic institutions to support e-Learning platform. Yet limited research has been conducted to investigate the factors affecting its usage, particularly by university instructors. To fill this research void, the expectation-confirmation model (ECM) was used as the core framework for analysis, while additional critical independent factors related to organizational, technological and individual characteristics were added to find a better model to understand university instructors’ continuance intention to use Blackboard system as a popular LMS.

Sample data were gathered from 158 university instructors at a university in the United Arab Emirates (UAE) who volunteered to participate in this study. Structural equation modeling technique was used to verify the causal relationships between the constructs.

Perceived usefulness (PU) affected satisfaction of Blackboard system. Both PU and satisfaction affected instructors’ continuance intentions to use Blackboard system. User-interface design affected both PU and satisfaction. Technical support influenced perceived usefulness. Training influenced perceived usefulness, but it had no influence on satisfaction. Computer self-efficacy had no influence on perceived usefulness.

Based on the ECM, this study contributes significantly to the limited body of research on capturing the influence of organizational, technological and individual motivators to explain university instructors’ continuance intention to use LMS.

Identifying important users from social media has recently attracted much attention in the information and knowledge management community. Although researchers have focused on users’ knowledge levels on certain topics or influence degrees on other users in social networks, previous works have not studied users’ prediction ability on future popularity. This paper aims to propose a novel approach to find prophetic bloggers based on their buzzword prediction ability.

The main approach is to conduct a time-series analysis in the blogosphere considering four factors: post earliness, content similarity, entry frequency and buzzword coverage. Our method has four steps: categorizing a blogger into knowledgeable categories, identifying past buzzwords, analyzing a buzzword’s peak time content and growth period and, finally, evaluating a blogger’s prediction ability on a buzzword and on a category.

Experimental results on real-world blog data consisting of 150 million entries from 11 million bloggers demonstrate that the proposed approach can find prophetic bloggers and outperforms others that do not take temporal features into account.

To the best of the authors’ knowledge, our approach is the first successful attempt to identify prophetic bloggers. Finding prophetic bloggers can bring great values for two reasons. First, as prophetic bloggers tend to post creative and insightful information, analysis on their blog entries may help find future buzzword candidates. Second, communication with prophetic bloggers can help understand future trends, gain insight into early adopters’ thoughts on new technology or even foresee things that will become popular.

Recently, people usually use the internet to obtain travel information, when they plan their travel. They especially want to obtain sightseeing spot information from reviews, but there are huge amounts of reviews of sightseeing spots. Users therefore cannot obtain important information from the reviews easily. As described herein, this paper aims to propose a system that automatically extracts and presents welcome news for sightseeing spots from reviews. This proposed Welcome-news is a “useful information” and “unexpected information” related to travel.

The flow for extracting Welcome-news from reviews is simple: A user inputs a sightseeing spot about which to get information; the system obtains reviews of the sightseeing spot and divides each sentence into reviews; the system extracts sentences including Welcome-news keyword(s), and the sentences become useful information; the system extracts unexpected information from useful information based on clustering, and it becomes Welcome-news; and the system presents all Welcome-news to the user.

This paper reports three findings: extraction of useful information for sightseeing spots based on Welcome-news keywords extracted by our experiment and using support vector machine (SVM); extraction of unexpected information for sightseeing spots by clustering; and automatic presentation of Welcome-news.

Numerous studies have extracted information from reviews based on some keywords. This proposed extraction of Welcome-news for travel not only uses keywords but also clusters based on topics. Furthermore, the proposed keywords include general keywords and unique keywords. The former appears for all kinds of sightseeing spots. The latter appears only for sightseeing spot. The authors extracted general keywords manually, and unique keywords using SVM.

Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method.

In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor.

The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors.

The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution.

This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms.