Energy labelling of electric appliances, among other goods and services, matters to both consumers and producers. It aids the consumers in gathering an expectation of how much electricity the good they are acquiring will consume, and it establishes guidelines that producers can follow to create more efficient products and services. Energy labelling is, nevertheless, nearly absent for software due to the heterogeneous hardware execution environments and unequal circumstances. Throughout this article, we provide an equal ground for software energy labelling by providing an intelligent prospective behavioural, hardware and energy-consumption patterns rating algorithm meant for the design phase of the Software-Development Life Cycle, as opposed to the existing approaches whose labelling procedure applies to the implementation phase onward. We expect our categorization method to guide software developers, helping them to decrease their energy consumption by raising their awareness of the energy footprint that their design choices produce; culminating in aware consumers who know what energy usage to expect from the software they consume.

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.

Regardless of the improvements in the efficiency of energy consumption of information and communication technology, energy consumption will forever be a requisite for software execution. Consequently, researchers have promoted the development of green and sustainable software with new development methods and tools. These, however, have been adopted with limited success due to technicalities and specific language/platform requirements. In this paper, we present RADIANCE: a web app for designing greener software with a model-driven approach based on the Behavior-Based Consumption Profiles (BBCP) external Domain-Specific Language (DSL). RADIANCE, in contrast to other tools, embraces users with different levels of knowledge about green software and software architecture. Moreover, RADIANCE assesses and rates, reports, and provides advice on the energy-consuming patterns of the software models created by the user, assisting them in identifying possible design changes that result in greener software designs from the initial stages of software development.

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.

Best Student Paper Award

Content-Based Recommendation Systems suggest items (e.g., articles, products, objects, services, or places) that are relevant to the user based on the features describing the items. In many content-based recommendation systems we can find, along with discrete attributes, textual features (e.g., text summaries or comments) obtained from web pages, news articles, etc. Traditionally, to enable its exploitation, the textual information of items is represented by using basic information retrieval models (such as the vector space model), which do not take into account natural language challenges involving the semantics of the words (synonymy, polysemy and hiperonymy, etc.) or language understanding. Other solutions try to exploit those semantics. In this paper, we present an experimental evaluation where we compare several recommendation approaches, including a content-based recommender based on vector space models, a deep learning and content-based recommendation approach, and a semantic-aware content-based recommendation model. This last approach exploits textual features of items obtained from the Linked Open Data (LOD) and BERT (Bidirectional Encoder Representations from Transformers) for language modelling. Deep Learning transformers are achieving good results in different NLP (Natural Language Processing) problems, but using them to build content-based recommendation systems has not been explored in depth so far. Our experimental results, focused on the domain of movie recommendations, show that a approach based on the use of BERT can provide good results if enough training data are available.

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.

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.

In the legal field, it is a fact that a large number of documents are processed every day by management companies with the purpose of extracting data that they consider most relevant in order to be stored in their own databases. Despite technological advances, in many organizations, the task of examining these usually-extensive documents for extracting just a few essential data is still performed manually by people, which is expensive, time-consuming, and subject to human errors. Moreover, legal documents usually follow several conventions in both structure and use of language, which, while not completely formal, can be exploited to boost information extraction. In this work, we present an approach to obtain relevant information out from these legal documents based on the use of ontologies to capture and take advantage of such structure and language conventions. We have implemented our approach in a framework that allows to address different types of documents with minimal effort. Within this framework, we have also regarded one frequent problem that is found in this kind of documentation: the presence of overlapping elements, such as stamps or signatures, which greatly hinders the extraction work over scanned documents. Experimental results show promising results, showing the feasibility of our approach.

In this work, we describe the design, development, and deployment of NEREA (Named Entity Recognizer for spEcific Areas), an automatic Named Entity Recognizer and Disambiguation system, developed in collaboration with professional documentalists. The aim of NEREA is to keep accurate and current information about the entities mentioned in a local repository, and then support building appropriate infoboxes, setting out the main data of these entities. It achieves a high performance thanks to the use of classification resources belonging to the local database. With this aim, the system performs tasks of named entity recognition and disambiguation by using three types of knowledge bases: local classification resources, global databases like DBpedia, and its own catalog created by NEREA. The proposed method has been validated with two different datasets and its operation has been tested in English and Spanish. The working methodology is being applied in a real environment of a media with promising results.

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.

Reading is a fundamental skill that each person needs to develop during early childhood and continue to enhance into adulthood. While children/teenagers depend on this skill to advance academically and become educated individuals, adults are expected to acquire a certain level of proficiency in reading so that they can engage in social/civic activities and successfully participate in the workforce. A step towards assisting individuals to become lifelong readers is to provide them adequate reading selections which can cultivate their intellectual and emotional growth. With that in mind, we have developed SOLE-R, a topic map-based tool that yields book recommendations. SOLE-R takes advantage of lexical and semantic resources to infer the likes/dislikes of a reader and thus is not restricted by the syntactic constraints imposed on existing recommenders. Furthermore, SOLE-R relies on publicly-accessible data on books to perform an in-depth analysis of the preferences of a reader that goes beyond book content or reading patterns explored by existing recommenders. We have verified the correctness of SOLE-R using a popular benchmark dataset. In addition, we have compared its performance with (state-of-the-art) recommendation strategies to further demonstrate the effectiveness of SOLE-R.

Automatic text categorisation systems is a type of software that every day it is receiving more interest, due not only to its use in documentaries environments but also to its possible application to tag properly documents on the Web. Many options have been proposed to face this subject using statistical approaches, natural language processing tools, ontologies and lexical databases. Nevertheless, there have been no too many empirical evaluations comparing the influence of the different tools used to solve these problems, particularly in a multilingual environment. In this paper we propose a multi-language rule-based pipeline system for automatic document categorisation and we compare empirically the results of applying techniques that rely on statistics and supervised learning with the results of applying the same techniques but with the support of smarter tools based on language semantics and ontologies, using for this purpose several corpora of documents. GENIE is being applied to real environments, which shows the potential of the proposal.

During the last years, mobile computing has been the focus of many research efforts, due mainly to the ever-growing use of mobile devices. In this context, there is a need to manage dynamic data, such as location data or other data provided by sensors. As an example, the continuous processing of location-dependent queries has been the subject of thorough research. However, there is still a need of highly expressive ways of formulating queries, augmenting in this way the systems' answer capabilities. Regarding this issue, the modeling power of Description Logics (DLs) and the inferring capabilities of their attached reasoners could fulfill this new requirement. The main problem is that DLs are inherently oriented to model static knowledge, that is, to capture the nature of the modeled objects, but not to handle changes in the property values (which requires a full ontology reclassification), as it is common in mobile computing environments (e.g., the location is expected to vary continually). In this paper, the authors present a novel approach to process continuous queries that combines 1) the DL reasoning capabilities to deal with static knowledge, with 2) the efficient data access provided by a relational database to deal with volatile knowledge. By marking at modeling time the properties that are expected to change during the lifetime of the queries, the authors'system is able to exploit both the results of the classification process provided by a DL reasoner, and the low computational costs of a database when accessing changing data (mobile environments, semantic sensors, etc.), following a two-step continuous query processing that enables us to handle continuous DL queries efficiently. Experimental results show the feasibility of the authors' approach.

According to a wide range of studies, IT should become a key facilitator in establishing primary education, reducing mortality and supporting commercial initiatives in Least Developed Countries (LDCs). The main barrier to the development of IT services in these regions is not only the lack of communication facilities, but also the lack of consistent information systems, security procedures, economic and legal support, as well as political commitment. In this paper, we propose the vision of an infrastructureless data platform well suited for the development of innovative IT services in LDCs. We propose a participatory approach, where each individual implements a small subset of a complete information system thanks to highly secure, portable and low-cost personal devices as well as opportunistic networking, without the need of any form of infrastructure. We review the technical challenges that are specific to this approach.

The vast amount of text documents stored in digital format is growing at a frantic rhythm each day. Therefore, tools able to find accurate information searching in natural language information repositories are gaining great interest in recent years. In this context, there are especially interesting tools capable of dealing with large amounts of text information and deriving human-readable summaries. However, one step further is to be able not only to summarize, but to extract the knowledge stored in those texts, and even represent it graphically. In this paper we present an architecture to generate automatically a conceptual representation of knowledge stored in a set of text-based documents. For this purpose we have used the topic maps standard and we have developed a method that combines text mining, statistics, linguistic tools, and semantics to obtain a graphical representation of the information contained therein, which can be coded using a knowledge representation language such as RDF or OWL. The procedure is language-independent, fully automatic, self-adjusting, and it does not need manual configuration by the user. Although the validation of a graphic knowledge representation system is very subjective, we have been able to take advantage of an intermediate product of the process to make a experimental validation of our proposals.

Due to recent advances in communications and computing devices, vehicular networks are attracting a lot of research attention. In a vehicular ad hoc network the nodes are vehicles which communicate among themselves by using shortrange wireless communications. These vehicles can store and share certain data (e.g., regarding the surrounding traffic and environment) which can be used, for example, to develop a wide range of applications to provide the driver with different services related to safety, traffic information, entertainment, etc. The data needed by these applications can be obtained by processing suitable queries in the vehicular network. However, a number of difficulties arise from the point of view of data management. An important challenge is how to communicate a query to the relevant vehicles, retrieve the relevant data from such a distributed and highly dynamic network, and bring back those data to the vehicle that originated the query. In this proposal, we will study the suitability of mobile agent technology to accomplish these tasks, as mobile agents could help thanks to their adaptability to mobile environments and other features, such as their mobility, autonomy or intelligence.

Location-based services have become an increasingly interesting research area in the last two decades. However, in many scenarios, dealing with the most precise location coordinates is not the best solution since people structure the world in geographic areas instead of coordinates. Since humans work with abstractions, and names are the way we refer to those abstractions, introducing semantics in geographic definitions becomes natural. For example, users can be interested in states with vacation resorts and may want to retrieve the state names, instead of the exact geographic limits of such states. Moreover, semantics introduces new challenges, such as how to exploit the location semantics to infer new information from known definitions. For instance, we may want a system to automatically obtain the value added tax (VAT) that should be applied by a shop in Madrid, inferring the applicable tax by considering the economic area where Madrid is included (in this case, Spain); notice that the VAT should not be inferred from a bigger economic area, like Europe, although it also includes Madrid geographically. Thus, the expression of locations at different granularities extends the traditional location-based query processing to consider the most appropriate semantics for each user. In this article, adopting description logics (DLs) as a base formalism, we provide a formalization of the notion of semantic location granule and semantic granule map. We benefit from the underlying semantics of the different granularities to extend the expressivity of location-based queries and automatically discover and infer new knowledge. The model we propose uses a DL reasoner to infer new granules relationships. In particular, a DL reasoner can infer containment and intersection relationships between location granules (and help to obtain several more relationships), which provides the way to introduce semantics in location-based queries. This is done within the logical frame of DLs, thus ensuring that our approach can be supported by existing regular DL reasoners (such as Pellet, Racer Pro, and HermiT) without the need to extend their reasoning capabilities.

Continuous location-dependent queries can be considered as key elements for the development of different categories of location-based and context-aware services. However, most work on location-dependent query processing has been mainly oriented towards outdoor environments. This paper studies location-dependent and context-aware queries over moving objects in indoor environments (e.g., houses, commercial malls, etc.), with a special focus on navigation-related queries (i.e., mainly path search and range queries). A hierarchical and context-dependent spatial data model is firstly presented, which leads to the consideration of other contextual dimensions besides the location of the involved entities, such as time and user profiles. Two algorithms for continuous processing of path and range queries on top of this modelling approach are introduced. The former performs a hierarchical and incremental path search for the continuous processing of path queries, and applied to both static and moving objects. The latter presents an incremental approach for continuous range queries, which performs a hierarchical network expansion around the initial query point and implements a mechanism to update the initial search tree based on user's movements.

Today in media companies there is a serious problem for cataloging news due to the large number of articles received by the documentation departments. That manual labor is subject to many errors and omissions because of the different points of view and expertise level of each staff member. There is also an additional difficulty due to the large size of the list of words in a thesaurus. In this paper, we present a new method for solving the problem of text categorization over a corpus of newspaper articles where the annotation must be composed of thesaurus elements. The method consists of applying lemmatization, obtaining keywords and named entities, and finally using a combination of Support Vector Machines (SVM), ontologies and heuristics to infer appropriate tags for the annotation. We carried out a detailed evaluation of our method with real newspaper articles, and we compared out tagging with the annotation performed by a real documentation department, obtaining really promising results.

The interest of intelligent transportation systems and vehicular networks is on the rise. In this paper, we present an overview of the development of VESPA, a system that we are developing for vehicles to share information (e.g., information about available parking spaces, accidents or obstacles in the road, emergency brakings, emergency vehicles asking the right of way, etc.) in inter-vehicle ad hoc networks. We summarize the research challenges that we have considered so far and the difficulties that still lie ahead. This includes the basic push-based approach used in VESPA, the resource allocation policy proposed to avoid competition in the access to scarce resources (such as parking spaces), and the development of data aggregation techniques for knowledge extraction. Besides, we also analyze our current and future work, which focuses on the exploitation of different data sources and pull-based query processing, the management of multimedia information, and the routing of query results.

A vehicular ad hoc network (VANET) is a type of mobile network whose nodes are traveling cars and which communicate with one another using short-range wireless communications. These cars can exchange and share different information among them, which can lead to the development of interesting applications that require the cooperation of vehicles or using the vehicular network as a distributed computing platform. Along with the opportunities offered by vehicular networks, a number of challenges also arise. In this paper, we focus on information gathering in vehicular ad hoc networks by using a pull model. Such model would allow users to send queries to a set of cars in order to find the desired information. In order to propose such a query processing scheme, the main challenge to address is how to route the different results towards their recipient in a highly dynamic network where the nodes move very quickly. To solve this, we propose GeoVanet, a DHT-based geographic routing protocol which ensures that the sender of a query can get a consistent answer. Our goal is not to compute the query result "instantaneously" but to ensure that the user will be able to retrieve it within a bounded time. To prove the effectiveness of GeoVanet, an experimental evaluation is provided, which shows that up to 80% of the available query results are delivered to the user.

Broadcasting sport events in live is a challenging task because obtaining the best views requires taking into account many dynamic factors, such as: the location and movement of interesting objects, all the views provided by cameras in the scenario (some of them wireless, mobile, or attached to moving objects), possible occlusions, etc. Therefore, a technical director needs to manage a great amount of continuously changing information to quickly select the camera whose view should be broadcasted.

This paper focuses on intelligent transportation systems and, more precisely, on intervehicle ad hoc networks. A vehicular ad hoc network (VANET) is a highly dynamic network as the vehicles communicate using short-range wireless communications and can move very quickly. Thus, for example, we can only rely on short interactions between vehicles to exchange information about relevant events. In this paper, we describe in detail a dissemination protocol that vehicles can use to share information by using vehicle-to-vehicle communications. The dissemination approach considers the relevance of the data, represented by what we call encounter probability, to decide when a rediffusion is needed. The protocol is able to disseminate data about any type of event in the network (e.g., available parking spaces, accidents or obstacles in the road, information about moving objects such as emergency vehicles that should get the right of way, etc.) by setting appropriate weights for the different factors that affect the computation of the encounter probability. An extensive experimental evaluation with different types of events shows the interest of the proposal: The vehicles receive the relevant messages in time, and the network overload is limited.

Nowadays it is widely recognized the need of developing a new generation of professional equipment for broadcasting that allows a considerable reduction of productions costs in different scenarios. We consider mobile multimedia scenarios where the incorporation of last generation hardware and the development of new software should allow: 1) to decrease the needs of human resources to produce (create, edit, and distribute) audiovisual content; and 2) to increase the richness of the broadcasted product.

Networks of sensors are used in many different fields, from industrial applications to surveillance applications. A common feature of these applications is the necessity of a monitoring infrastructure that analyzes a large number of data streams and outputs values that satisfy certain constraints.

By exchanging events in a vehicular ad hoc network, drivers can receive interesting information while driving. For example, they can be informed of available parking spaces in their vicinity. Obviously, a suitable protocol is needed to disseminate the events efficiently within the area where they ar

VESPA (Vehicular Event Sharing with a mobile P2P Architecture) 1 is a system for enabling vehicles to share information in vehicular ad-hoc networks (VANETs). The originality of VESPA is to process and disseminate any type of event (e.g., available parking spaces, accidents, emergency braking, infor

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

With the current advances of mobile computing technology, we are witnessing an explosion in the development of applications that provide mobile users with a wide range of services. In this paper, we present a system that supports distributed processing of continuous location-dependent queries in mobile environments. The system that we propose presents the following main advantages: 1) it is a general solution for the processing of location-dependent queries in scenarios where not only the users issuing queries, but also other interesting objects can move; 2) it performs an efficient processing of these queries in a continuous way; 3) it is especially well adapted to environments where location data are distributed in a network and processing tasks can be performed in parallel, allowing a high scalability; and 4) it optimizes wireless communications. We use mobile agents to carry the processing tasks wherever they are needed. Thus, agents are in charge of tracking the location of interesting moving objects and refreshing the answer to a query efficiently. We evaluate the usefulness of the presented proposal showing that the system achieves a good precision and scales up well

Among mobile computing applications, location-based services are expected to be a big business for wireless operators. These services provide value added by considering the location of the user in order to give him/her more customized information. However, the processing of continuous location-dependent queries is still a subject of research. One of the key issues is how to update the answer presented to the user efficiently as it becomes obsolete very quickly. The goal of this paper is to analyze how to deal with situations where the answer cannot be updated with the desired frequency. We present several approaches to this problem and evaluate their performance.