Collective Spatial Keyword Querying (CoSKQ) was proposed over a decade ago as a model to retrieve sets of objects in spatial databases given a specific query. The rationale behind is that the retrieved solution sets must cover query keywords as well as minimise the geographic distances between the query and the solution elements. However, in most real scenarios, the exact matching of query keywords and object descriptions is rare or not possible. In this paper, we extend the notion of CoSKQ for recommendation problems and present SURGE (Spatial User Recommendations using Geographical metrics and sEmantics), an approach that puts forward a recommender system able to return solution sets even when query keywords and object descriptions do not match exactly. In order to do that, semantic techniques are used. A tourism domain has been used throughout the paper to explain the model. Furthermore, an exhaustive set of experiments has been carried out to validate the approach.

In the Big Data era, context-aware mobile recommender systems are crucial in assisting citizens and tourists in making informed decisions, providing a suitable way for users to find the relevant data. These systems should be proactive, able to detect the ideal time and location to provide recommendations for a specific item or activity. To accomplish this, push-based recommender systems can be employed, utilizing context rules to determine when a recommendation should be initiated. However, there is very limited reported experience in defining and implementing such systems and a complete generic solution that adapts flexibly to the preferences of users and protects their privacy is still missing. In this paper, we present a novel approach where appropriate types of recommendations are provided automatically, without the need for user input. Our proposal allows users to easily activate, deactivate, customize, and create rules for improved personalization. Additionally, the module that, based on the context, decides the types of recommendations required is executed on the user’s mobile device, reducing wireless communication and safeguarding the user’s privacy, as context data are evaluated locally. To illustrate the approach, we have developed R-Rules, a prototype for Android devices focused on the triggering of recommendation rules, which provides a friendly user interface that facilitates user personalization. We have evaluated various technological options and demonstrated the feasibility, performance, and scalability of the proposal, as well as its suitability to users’ needs.

In the current Big Data era, mobile context-aware recommender systems can play a key role to help citizens and tourists to make good decisions. Ideally, these systems should be proactive, able to detect the right moment and place to offer suggestions of a specific type of item or activity to the user. For this purpose, push-based recommender systems can be used, exploiting context rules to decide when a specific type of recommendation should be triggered.

The COVID-19 crisis has turned the world upside-down and many aspects of our social and daily lives need to be adapted. In this position paper, we argue that data management techniques for mobile computing scenarios also have to be extended for them to be useful and suited to the new situation. In particular, the concept of social distancing, which has been shown to be one of the most effective measures to cope with the virus expansion, should be incorporated as a natural component of many information services and applications for mobile users. We illustrate this idea with two example use cases: mobile recommender systems and information services for drivers. We outline current challenges and some ideas for future work.

This work presents integral actions towards women in engineering recognition organized according to educational stages they are directed: Stage 1, from early childhood education, primary education and secondary education; Stage 2, during university and Stage 3 after university. At stage 1 actions are devised to increase girl's interest on Science, Technology, Engineering and Mathematics (STEM). Emphasis is put on showing women in engineering as role models, illustrating engineers work and stressing the importance of diversity in working groups. Stage 2 is focused on making male and female students aware of the gender gap in engineering and the importance of diversity for innovation and training female students on known female narrow circumstances. Finally, at stage 3, the objective is to retain and promote women in the engineering profession. The specific actions developed at the three stages are presented. Their impact is discussed in order to accomplish effective actions for achieving gender balance towards excellence in Engineering.

In recent years, there has been an increasing research attention towards Context-Aware Recommender Systems (CARS) for mobile users. The main motivation is that, by considering the current context of the mobile user, more relevant suggestions can be provided. However, further research is required to enable the effective deployment of mobile CARS.

Nowadays, due to the high availability of heterogeneous data sources that can provide interesting information, users usually suffer from information overload. Therefore, the development of adaptive information systems that can offer personalized information and filter out irrelevant data for a user is required. Significant work has been developed to solve this problem in the area of the so-called recommendation systems. However, context information has only started to be considered recently to build recommendation systems, despite being key to obtain more accurate recommendations. Moreover, even with some context information, there is still a significant gap between the fields of mobile computing and recommendation systems.

We showcase QUEST (QUEry generator for STructured sources), a search engine for relational databases that combines semantic and machine learning techniques for transforming keyword queries into meaningful SQL queries. The search engine relies on two approaches: the forward, providing mappings of keywords into database terms (names of tables and attributes, and domains of attributes), and the backward, computing the paths joining the data structures identified in the forward step. The results provided by the two approaches are combined within a probabilistic framework based on the Dempster-Shafer Theory. We demonstrate QUEST capabilities, and we show how, thanks to the flexibility obtained by the probabilistic combination of different techniques, QUEST is able to compute high quality results even with few training data and/or with hidden data sources such as those found in the Deep Web.

Nowadays, a great amount of the contents of the WWW are still mainly only human-oriented. To progressively move into the Semantic Web, the adoption of the use of ontologies is a milestone. However, their elaboration from scratch is quite expensive, which could prevent non-experts from using them in their applications. Due to this reason, different approaches for ontology reusing and engineering have been proposed, but they require domain experts and knowledge engineers. Besides, finding an ontology (if it exists) that fits a specific domain can be a tedious and frustrating experience.

Keyword queries offer a convenient alternative to traditional SQL in querying relational databases with large, often unknown, schemas and instances. The challenge in answering such queries is to discover their intended semantics, construct the SQL queries that describe them and used them to retrieve the respective tuples. Existing approaches typically rely on indices built a-priori on the database content. This seriously limits their applicability if a-priori access to the database content is not possible. Examples include the on-line databases accessed through web interface, or the sources in information integration systems that operate behind wrappers with specific query capabilities. Furthermore, existing literature has not studied to its full extend the inter-dependencies across the ways the different keywords are mapped into the database values and schema elements. In this work, we describe a novel technique for translating keyword queries into SQL based on the Munkres (a.k.a. Hungarian) algorithm. Our approach not only tackles the above two limitations, but it offers significant improvements in the identification of the semantically meaningful SQL queries that describe the intended keyword query semantics. We provide details of the technique implementation and an extensive experimental evaluation.

We propose the demonstration of Keymantic , a system for keyword-based searching in relational databases that does not require a-priori knowledge of instances held in a database. It finds numerous applications in situations where traditional keyword-based searching techniques are inapplicable due to the unavailability of the database contents for the construction of the required indexes.

The syntactic approach of most of Web search engines still has the drawback of not considering the semantics of the keywords entered by the user. So, users usually have to browse many hits looking for the information they want. In this paper, we present a system that, given a set of keywords with well defined semantics, automatically generates a set of formal queries, in the query language of the user's choice, which attempt to capture what the user had in mind when she or he wrote those keywords. The system uses ontologies and a description logics reasoner to perform a semantic enrichment of user keywords to improve the discovering of possible user queries and to reject semantically inconsistent queries.

The lack of explicit semantics in the current Web can lead to ambiguity problems: for example, current search engines return unwanted information since they do not take into account the exact meaning given by user to the keywords used. Though disambiguation is a very well-known problem in Natural Language Processing and other domains, traditional methods are not flexible enough to work in a Web-based context.In this paper we have identified some desirable properties that a Web-oriented disambiguation method should fulfill, and make a proposal according to them. The proposed method processes a set of related keywords in order to discover and extract their implicit semantics, obtaining their most suitable senses according to their context. The possible senses are extracted from the knowledge represented by a pool of ontologies available in the Web. This method applies an iterative disambiguation algorithm that uses a semantic relatedness measure based on Google frequencies. Our proposal makes explicit the semantics of keywords by means of ontology terms; this information can be used for different purposes, such as improving the search and retrieval of underlying relevant information.

In the last decade, mobile agents have arisen as a promising paradigm to build distributed and mobile computing applications. However, mobile agents have not been massively adopted. One of the reasons could be that some issues have yet to be solved to increase the confidence of developers. Thus, scalability problems sometimes arise in applications with a high number of mobile agents when calls and trips happen very frequently. In this paper we present SPRINGS, a novel Java-based mobile agent system which is scalable, flexible, and easy to use. Our work has been motivated by our experience with mobile agents in several research projects. We focus on scalability issues and efficient maintenance of location-independent agent references in dynamic scenarios where agents communicate and travel frequently among computers. We have obtained encouraging performance results through an extensive set of experiments. Moreover, our tests show that SPRINGS achieves a degree of concurrency that other well-known platforms cannot reach