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Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge‐rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web‐powered context‐aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.

The purpose of this paper is to review the state-of-the-art in adaptive user interface systems by studying their historical development over the past 20 years. Moreover, this paper contributes with a specific model combining three main entities (users, context and devices) that have been demonstrated to be always represented in these environments. Novel concepts that should be taken into account in these systems are also presented.

The authors first provide a review and a comparison of current user interface adaptive systems. Next, the authors detail the most significant models and the set of techniques used to, finally, propose a novel model based on the studied literature.

Literature solutions for adaptive user interface systems tend to be very domain dependant. This situation restricts the possibility of sharing and exporting the information between such systems. Furthermore, the studied approaches barely highlight the dynamism of these models.

The paper is a review of adaptive user interface systems and models. Although there are several reviews in this area, there is a lack of research for modelling users, context and devices simultaneously in this domain. The paper also presents several significant concepts that should be taken into account to bring an adaptive and dynamic perspective to the studied models.

The purpose of this paper is to present a new approach toward automatically visualizing Linked Open Data (LOD) through metadata analysis.

By focussing on the data within a LOD dataset, the authors can infer its structure in a much better way than current approaches, generating more intuitive models to progress toward visual representations.

With no technical knowledge required, focussing on metadata properties from a semantically annotated dataset could lead to automatically generated charts that allow to understand the dataset in an exploratory manner. Through interactive visualizations, users can navigate LOD sources using a natural approach, in order to save time and resources when dealing with an unknown resource for the first time.

This approach is suitable for available SPARQL endpoints and could be extended for resource description framework dumps loaded locally.

Most works dealing with LOD visualization are customized for a specific domain or dataset. This paper proposes a generic approach based on traditional data visualization and exploratory data analysis literature.

This paper aims to outline a map of all the research that exists on mobile technology and tourism archived in the two main databases worldwide (Web of Science and Scopus). Accordingly, with the identification of all the scientific articles that deal with both mobile technology and tourism, the authors seek to ascertain the evolution of mobile technology in the tourism sector through the years, countries, universities and authors and determine the various collaborations brought about between authors, universities, institutions and/or companies in various research projects. Finally, it also allows the authors to distinguish the main topics under study within the scope of ‘mobile tourism’.

A mixed methodology has been carried out. The search focused on the principal databases of bibliographic references and citations of periodical publications, such as articles from scientific journals, books and other types of printed material. Once the results were obtained in the respective databases, it was necessary to be able to work with them. In this respect, the authors had to extract the relevant data and dump it in a bibliographic reference manager, for which they chose Mendeley. After this, the tabulation of data was performed in Excel and tables and graphs were created from all the data collected.

The main results obtained and analyzed are the number of articles per year, countries and universities. In the same way, it is interesting to highlight the number of countries and universities that participate in each article under study. On the other hand, an analysis has been carried out regarding the number of articles per author, as well as the topics dealt with in the different articles.

This analysis reveals the role that has been played by mobile phones in tourism since the first scientific article was recorded in 2002. In this regard, in recent years there has been a significant increase in the number of articles, finally resulting in moderate figures in relation to countries (40) and universities (233) that have formed part of the subject matter under study. In contrast to other areas of research in tourism, the relevance of this subject is therefore evident, as is the need for greater background knowledge to establish research models adapted to the new reality of tourism in a world of ever-increasing mobility.

The event‐driven paradigm is appropriate for context aware, distributed applications, yet basic events may be too low level to be meaningful to users. The authors aim to argue that this bottom‐up approach is insufficient to handle very low‐level sensor data or to express all the queries users might wish to make.

The authors propose an alternative model for querying and subscribing transparently to distributed state in a real‐time, ubiquitous, sensor‐driven environment such as is found in Sentient Computing.

The framework consists of four components: a state‐based, temporal first‐order logic (TFOL) model that represents the concrete state of the world, as perceived by sensors; an expressive TFOL‐based language, the Abstract Event Specification Language (AESL) for creating abstract event definitions, subscriptions and queries; a higherorder service (Abstract Event Detection Service) that accepts a subscription containing an abstract event definition as an argument and in return publishes an interface to a further service, an abstract event detector; and a satisfiability service that applies classical, logical satisfiability in order to check the satisfiability of the AESL definitions against the world model, in a manner similar to a constraint‐satisfaction problem.

The paper develops a model‐based approach, appropriate for distributed, heterogeneous environments.

The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance.

Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain.

The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25.

This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.