Exploring scholarly data with Rexplore.

Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves

Leadership and decision-making practices in public versus private universities in Pakistan

The goal of this study is to examine differences in leadership and decision-making practices in public and private universities in Pakistan, with a focus on transformational leadership (TL) and participative decision-making (PDM). We conducted semi-structured interviews with 46 deans and heads of department from two public and two private universities in Pakistan. Our findings indicate that leadership and decision-making practices are different in public and private universities. While differences were observed in all six types of TL-behaviour, the following three approaches emerged to be crucial in both public and private universities: (1) articulating a vision, (2) fostering the acceptance of group goals, and (3) high-performance expectations. In terms of PDM, deans and heads of department in public and private universities adopt a collaborative approach. However, on a practical level this approach is limited to teacher- and student-related matters. Overall, our findings suggest that the leadership and decision-making practices in Pakistani public and private universities are transformational and participative in nature

Exploiting Context-Dependent Quality Metadata for Linked Data Source Selection

The traditional Web is evolving into the Web of Data which consists of huge collections of structured data over poorly controlled distributed data sources. Live queries are needed to get current information out of this global data space. In live query processing, source selection deserves attention since it allows us to identify the sources which might likely contain the relevant data. The thesis proposes a source selection technique in the context of live query processing on Linked Open Data, which takes into account the context of the request and the quality of data contained in the sources to enhance the relevance (since the context enables a better interpretation of the request) and the quality of the answers (which will be obtained by processing the request on the selected sources). Specifically, the thesis proposes an extension of the QTree indexing structure that had been proposed as a data summary to support source selection based on source content, to take into account quality and contextual information. With reference to a specific case study, the thesis also contributes an approach, relying on the Luzzu framework, to assess the quality of a source with respect to for a given context (according to different quality dimensions). An experimental evaluation of the proposed techniques is also provide

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Start up ecosystem: Features, processes, and actors.

Successful start-ups can positively contribute to the well-being of countries' economies by creating jobs and new investment opportunities. The success of start-ups strongly depends on the ecosystem in which they are inserted. In this regard, it is important to understand the concept of the start-up ecosystem, in particular from the point of view of researchers and professionals. The desire to deepen the dimensions and components of the ecosystem and to observe more closely the best start-up-friendly ecosystems, then propose a comparison with the Italian context, is derived from evidence indicating that the most successful start-ups are concentrated mainly in certain areas of the world, and this concentration is by no means accidental. In fact, the presence of cities and districts recognized worldwide as real technological hubs appears to be directly connected to the presence of a series of conditions that are extremely favorable to their development. From this reasoning, the concept of "ecosystem," which we defined in the course of the work as a "set of conditions, actors and infrastructures capable of supporting the birth and development of innovative business projects; an absolutely heterogeneous system of elements, which embraces culture, regulatory and fiscal measures, public administration, financiers, businesses, universities and research centers." To better describe the phenomenon of start-up ecosystems and analyze the main components that characterize the latter, especially in relation to the geographical contexts in which they develop, we have chosen to start from a model that presents five essential components of start-up ecosystems: entrepreneurship with a particular focus on the diffusion of start-up companies; business incubators and accelerators; institutions (and in particular universities); and the possibility of accessing technologies as a lever for achieving the main objectives of start-ups. The work presents a qualitative research methodology on different levels of analysis. The process research is aimed at multiple case studies in which we first present a comparison between the start-up ecosystems of Rome and Naples and then conciliate with a first benchmarking with a context considered to be of excellence (despite the limitations it presents in recent times), i.e., that of Silicon Valley. The case studies were enriched by the results of narrative interviews of the main actors of the start-up ecosystem: start-uppers, directors of incubators and start-up accelerators and university professors engaged in the issues of new entrepreneurship

The Level of Promotion of Entrepreneurship in Technical Colleges in Palestine

The study aimed to identify the level of promotion of entrepreneurship in the technical colleges in Palestine. The analytical descriptive method was used in the study. A questionnaire of 41 items was randomly distributed to the technical colleges in the Gaza Strip. The random sample consisted of (275) employees from the mentioned colleges, and the response rate were (74.5%). The results of the study showed that the technical colleges achieved a high level of promotion of entrepreneurship with a relative weight of 73.45%. The results of the study showed that there is a high level of promotion of entrepreneurship (risk, preparedness, proactive competition, innovation orientation) in the technical colleges in Gaza Strip. The field of competition came in first place with a relative weight of 76.65%. In the second place came the field (the trend towards innovation) and relative weight (74.96%). In the third place came the field of pre-emptive preparedness with a relative weight of 74.07%. In the fourth and last place came the field of risk and a relative weight of 68.39%. The results confirmed that there are statistically significant differences in the promotion of entrepreneurship in the technical colleges in Gaza Strip due to the college variable in favor of UCAS. The results confirmed that there is no statistically significant relationship in the promotion of entrepreneurship in technical colleges in Gaza Strip due to the variable level of employment. The researchers suggest a set of recommendations, the most important of which is to draw the attention of the technical colleges to the importance of promoting entrepreneurship, because of their role in reducing the problem of unemployment, the importance of linking technical education and promoting entrepreneurship to the Palestinian society in general and Gaza Strip in particular. The importance of urging decision-makers in technical colleges to promote interest in leadership and to put their own courses in all technical education programs in these colleges, as well as enhancing the technical, technological and technical capabilities of technical education and keeping pace with the latest international standards by providing the necessary material resources. There is a need to urge researchers to conduct further studies of the future which deal with the same variables of the current study in the field of entrepreneurship and applied to other sectors

Content Recommendation Through Linked Data

Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia