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El presente trabajo de investigación tiene como objetivo principal verificar las bondades que aportan la metodología de precios hedónicos a la valoración de inmuebles, mediante la obtención de una muestra con precios de oferta del segmento medio y semilujo, siendo en nuestro estudio el precio como la variable dependiente y una serie de variables independientes que influyen en el precio, se pretende explicar de una manera más objetiva el valor de mercado. La estimación de valor de mercado de un activo fijo es de gran importancia para infinidad de trámites y toma de decisiones, tal es el caso de las instituciones financieras para tomar en garantía de préstamo, a las empresas para conocer cuánto valen sus activos fijos, gobierno para cobrar impuestos y una infinidad de usos que se le pueden dar, se trata de verificar que variables explican el precio que reflejen las condiciones de mercado.

Higher Education plays a principal role in the changing and complex world of today, and there has been rapid growth in the scientific literature dedicated to predicting students’ academic success or risk of dropout thanks to advances in Data Mining techniques. Degrees such as Computer Science in particular are in ever greater demand. Although the number of students has increased, the number graduating is still not enough to provide society with as many as it requires. This study contributes to reversing this situation by introducing an approach that not only predicts the dropout risk or students’ performance but takes action to help both students and educational institutions. The focus is on maximizing graduation rates by constructing a Recommender System to assist students with their selection of subjects. In particular, the challenge is addressed of constructing reliable Recommender Systems on the basis of data which are both sparse and few in quantity, imbalanced, and anonymized, and which might have been stored under imperfect conditions. This approach is successfully applied to create a Recommender System using a real-world dataset from a public Spanish university containing performance data of a Computer Science degree course, demonstrating its successful application in real environments. The construction of a support system based on that approach is described, its results are evaluated, and its implications for students’ academic achievement, and for institutions’ graduation rates are discussed. Through the construction of this decision support system for students, we intend to increase the graduation rates and lower the dropout rate.

Different types of sensors along the distribution pipelines are continuously measuring different parameters in Smart WAter Networks (SWAN). The huge amount of data generated contain measurements such as flow or pressure. Applying suitable algorithms to these data can warn about the possibility of leakage within the distribution network as soon as the data are gathered. Currently, the algorithms that deal with this problem are the result of numerous short-term water demand forecasting (WDF) approaches. However, in general, these WDF approaches share two shortcomings. The first one is that they provide low-frequency predictions. That is, most of them only provide predictions with 1-hour time steps, and only a few provide predictions with 15 min time steps. The second one is that most of them require estimating the annual seasonality or taking into account not only data about water demand but also about other factors, such as weather data, that make their use more complicated. To overcome these weaknesses, this work presents an approach to forecast the water demand based on pattern recognition and pattern-similarity techniques. The approach has a twofold contribution. Firstly, the predictions are provided with 1 min time steps within a time lead of 24 hours. Secondly, the laborious estimation of annual seasonality or the addition of other factors, such as weather data, is not needed. The paper also presents the promising results obtained after applying the approach for water demand forecasting to a real project for the detection and location of water leakages.

The use of workflows to manage business or administrative process has been increasing since the end of the past century. Administrative workflows proved useful in large companies and public institutions with strict hierarchical organizations. In order to use them adequately in different areas or departments of a hierarchical organization, they must be adapted to their particular conditions while complying with the general regulations of the process established at the top level. Any department of an organization can add activities, can remove them, and can choose concrete users to perform them and to set the data managed in the activities, as long as the adapted process does not fail to comply with the restrictions established at the generic level. This problem, called hierarchical adaptation problem, also implies establishing the appropriate measures to implement when the general regulation is changed. Such measures must maintain the consistency among the different levels by means of the propagation of the changes to all the adapted processes. To solve this problem, this paper presents the hierarchical adaptation method, which is an ontology-based method to define the rules to satisfy by a generic adaptable workflow. Additionally, it establishes the rules to satisfy the adaptations of the specification ontology. Moreover, it provides the operations facilitating both adaptations of administrative workflows and propagations of changes.

Data-intensive Web applications are usually developed by means of specific Web application frameworks, which provide, among other features, the proper tools to automate redundant tasks, e.g. the implementation of CRUD operations. However, although Model-Driven Web Engineering (MDWE) approaches have defined specific languages and tools to significantly reduce development efforts, the provision of such automation tools has been traditionally neglected. This paper presents AutoCRUD, a WebRatio plug-in automating the generation of CRUD operations in OMG Interacting Flow Modelling Language (IFML). It has been developed as a joint effort by academia and industry to assess its validity in a real scenario.

In this paper we present a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) during the period 2009–2016. This ranking has been carried out through the integration into a complete database of the detailed information of individuals finishing such studies together with their labor data. The multicriteria method used is TOPSIS together with a new decision support method for assessing the influence of each criterion and its dependence on the weights assigned to them. This new method is based on a worst-best case scenario analysis and it is compared to a well known global sensitivity analysis technique based on the Pearson's correlation ratio.

In the last years, the growing volumes and sources of data has made Big Data technologies to become mainstream. In that sense, techniques like Data Visualization are being used more and more to group large amounts of data in order to transform them into useful information. Nevertheless, these techniques are currently included in Business Intelligence approaches to provide companies and public organizations with helpful tools for making decisions based on evidences instead of intuition. The Sankey diagram is an example of those complex visualization tools allowing the user to graphically trace meaningful relationships in large volumes of data. However, this type of diagram is usually static so they must be continuously and manually rebuilt on top of massive multivariable environments whenever decision makers need to evaluate different options and they do not allow to establish conditions over the data shown. This paper presents LiveSankey, an approach to automatically generate dynamic Sankey Diagrams allowing users to filter the data shown. As a result, multiple conditions may be established over the data used and the corresponding diagram can be dynamically rebuilt.

There are a large number of machine learning algorithms as well as a wide range of libraries and services that allow one to create predictive models. With machine learning and artificial intelligence playing a major role in dealing with engineering problems, practising engineers often come to the machine learning field so overwhelmed with the multitude of possibilities that they find themselves needing to address difficulties before actually starting on carrying out any work. Datasets have intrinsic properties that make it hard to select the algorithm that is best suited to some specific objective, and the ever-increasing number of providers together make this selection even harder. These were the reasons underlying the design of CompareML, an approach to supporting the evaluation and comparison of machine learning libraries and services without deep machine learning knowledge. CompareML makes it easy to compare the performance of different models by using well-known classification and regression algorithms already made available by some of the most widely used providers. It facilitates the practical application of methods and techniques of artificial intelligence that let a practising engineer decide whether they might be used to resolve hitherto intractable problems. Thus, researchers and engineering practitioners can uncover the potential of their datasets for the inference of new knowledge by selecting the most appropriate machine learning algorithm and determining the provider best suited to their data.

A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper. publishersversion published

Rubio-Largo, A., Preciado, J. C., & Iribarne, L. (2019). Data-Driven Computational Intelligence for Scientific Programming. Scientific Programming,[5235706].[Editorial]. Doi: https://doi.org/10.1155/2019/5235706

Rodriguez-Echeverria, R., Preciado, J. C., Rubio-Largo, Á., Conejero, J. M., & Prieto, Á. E. (2019). A Pattern-Based Development Approach for Interaction Flow Modeling Language. Scientific Programming,[7904353]. https://doi.org/10.1155/2019/7904353 Development and deployment technologies for data-intensive web applications have considerably evolved in the last years. Domain-specific frameworks or model-driven web engineering approaches are examples of these technologies. They have made possible to face implicit problems of these systems such as quick evolving business rules or severe time-to-market requirements. Both approaches propose the automation of redundant development tasks as the key factor for their success. The implementation of CRUD operations is a clear example of repetitive and recurrent task that may be automated. However, although web application frameworks have provided mechanisms to automate the implementation of CRUD operations, model-driven web engineering approaches have generally ignored them, so automation has not been properly faced yet. This paper presents a pattern-based development approach for the Interaction Flow Modeling Language as a way to finally automate repetitive specification tasks. Our approach is illustrated by defining and applying IFML patterns for CRUD operations. Additionally, a supporting tool, which enables automation, is shown. The suitability of our approach and the utility of its tool have been evaluated by its application into several real projects developed by a software company specialized in model-driven web application development. The results obtained present evidence of a significant productivity improvement obtained by the automation of the IFML specification of CRUD operations. publishersversion published

Education and employment are key aspects of a country’s well-being. Governments expend valuable resources on designing education plans and employment programs. These two aspects are usually analysed separately, although, as they are closely related, considering them together might improve their efficacy. The problem lies, at least in part, in the fact that different public entities manage their own data with their own isolated systems, and do not develop joint educational and employment policies. In order to facilitate working towards this goal, in this manuscript, we make use of Data Engineering, Data Visualization, and Intelligent Data Analytics methods to create a decision support system for the Government of Extremadura. Extremadura is a European Union Objective 1 region in Spain with high rates of unemployment and secondary school drop-out. Data Engineering is used to create a Data Warehouse that unifies the different data sources into a central repository for quick access and control. This allows dealing with the challenge of transforming, processing, storing and accessing the data. Data Visualization techniques are applied to create an interactive dashboard that assists users in analysing and interpreting the data in the Data Warehouse repository. Thus, charts, diagrams, and maps are created specifically to help technical or political decision-makers. Finally, Intelligent Data Analytics techniques are used to incorporate Association Rules into the visualization dashboard. Its goal is to identify associations, relationships, and patterns in data that, at least in plain sight, are not readable or interpretable by humans. It does this by inferring knowledge that humans cannot pick out by themselves. As a result, a complete system was defined and implemented to support public administrations in their decision-making and definition of precise evidence-based policies in the areas of education and employment. In particular, it allows the definition of unified strategies to reduce the unemployment rate.

In the last fifteen years, services deployed via the Web have become a part of our everyday lives. Supporting Web technologies have also evolved very quickly to respond to the new services and features demanded by mobile and always connected users. Currently, the demand for services on the Web is still growing at a very fast pace as well as new techniques, methods and cutting-edge technologies that make theWeb an increasingly complex environment. In this scenario, the application of systematic, disciplined and quantifiable approaches to development, operation, and maintenance of Web-based applications continues to have a full validity like never before. So, at this point, is where the Web Engineering [1] discipline comes into the scene. Web Engineering deals with the process of developing, deploying and maintaining Web applications. The main issues ofWeb Engineering encompass how to successfully manage the diversity and complexity of Web development, and to avoid potential failures that may have serious implications [2]. During the last decades, the Web Engineering research community has handled innovative concepts such Model-Driven Web Engineering [3], Rich Internet Applications [4], Social Applications [5], Mobile Web Apps [6] or Quality Aspects [7], among other relevant goals. However, new challenges appear in Web engineering for now and the next few years with the incorporation of new trends that will be reflected in theWeb as Artificial Intelligence [8], Machine Learning [9], Web Big Data and Web Data Analytics [10], Web of Things applications [11], new Computing and Deployment approaches [12], among other significant challenges. As one of the references in this research field, the International Conference onWeb Engineering (ICWE), is the prime yearly international conference on the different aspects of designing, building, maintaining and using Web applications. With the ever-increasing importance of the Web in almost all walks of life, ICWE bring together practitioners and researchers to tackle the emerging challenges in the engineering of Web applications. This special issue in the Journal ofWeb Engineering is oriented for discussing on advanced techniques, new challenges, current state-of art and experiences for Enhancing the Web with Advanced Engineering dealing with a selection of the best papers of the 2018 edition of ICWE [13]. Gallidabino et al. extend the standard HTML5WebWorkerAPI with the concept of liquid WebWorkers (LWWs), so that developers can transparently offload parallel execution of stateless tasks by managing the necessary device selection and direct peer-to-peer data transfer. By introducing the liquid WebWorker API into our Liquid.js framework, they present how to create a pool of devices sharing their CPU processing capabilities according to different policies. They propose the design of liquid WebWorkers and their implementation within the Liquid.js framework. LWWs are designed for building liquid application featuring heavy computations, which are dynamically redeployed across multiple, partially idle heterogeneous devices. The goal is to avoid slowing down the overall performance of the application because some of its components are running on a slow, bottleneck device. The preliminary evaluation of the liquid WebWorkers concept and their prototype implementation shows that there is the opportunity for increasing the overall performance of liquid Web applications when LWWs are migrated from slow to more powerful devices. Overall, with the extension of Liquid.js presented in their paper, they can create complex device ensembles able to directly connect and transparently share storage and computation resources between them. This allows developers to control how their liquidWeb applications balance performance and energy consumption. On the other hand, Govind et al. present a novel concept called “Semantic Finger-printing” that allows Web content classification solely based on the information derived from the named entities contained in a Web document. It encodes the semantic nature of Web content into a concise vector, namely the semantic fingerprint. Thus, they expect that semantic fingerprints, when utilized in combination with machine learning, will enable a fine-grained classification ofWeb contents. In order to empirically validate the effectiveness of semantic fingerprinting, they also perform a case study on the classification of Wikipedia documents. Even further, they thoroughly examine the results obtained by analyzing the performance of Semantic Fingerprinting with respect to the characteristics of the data set used for the experiments. In addition, they also investigate performance aspects of the engineered approach by discussing the run-time in comparison with its competitor baselines. They observe that the semantic fingerprinting approach outperforms the state-of-the-art baselines as it raises Web contents to the entity-level and captures their core essence. Moreover, this approach achieves a superior run-time performance on the test data in comparison to competitors. Additionally, Bakaev et al. describe a visual analysis tool which takes a WUI screenshot and produces structured and machine-readable representation (JSON) of the interface elements’ spatial allocation. The implementation is based on OpenCV (image recognition functions), dlib (trained detector for the elements’ classification), and Tesseract (label and content text recognition). The JSON representation is used to automatically calculate several metrics related to visual complexity, which is known to have major effect on user experience with UIs. They further describe a WUI measurement platform that allows integration of the currently dispersed sets of metrics from different providers and demonstrate the platform’s use with several remote services. They perform statistical analysis of the collected metrics in relation to complexity-related subjective evaluations obtained from 63 human subjects of various nationalities. Finally, they build predictive models for visual complexity and show that their accuracy can be improved by integrating the metrics from different sets. Regressions with the single index of visual complexity metric that they proposed had R2 = 0.460, while the best joint model with 4 metrics had R2 = 0.647. Complementary, Toala et al. present a conceptual model (CM) for the study of the effect of personality and productivity on the perceptions and adoption intentions regarding MDWE. Also, it has presented an exploratory observational study to assess whether there are significant differences between groups of subjects scoring at the high vs low level of the personality and productivity IVs with respect to the perception and intention DVs. This purpose with these two contributions is fostering the discussion among the research community regarding individual differences and how they may be affecting the use ofMDWE methods and tools. They propose that, in order for the MDWE research community to come up with a CMthat truly reflects theMDWEmethod adoption influencers, substantial empirical research needs to be done. In this sense its paper is only another drop in the bucket. Only with much more extensive data set will it be possible to apply much more sophisticated statistical analysis that serve to validate alternative CMs in order to better understand which are the real influencers of MDWE adoption. Finally, Koren et al. conceptualize different methods to move video delivery from centralized cloud infrastructures to end user devices. They discuss their strengths and weaknesses and present design considerations. To exemplify a particular approach, the authors showcase the implementation and evaluation of their system called OakStreaming. It streams videos peer-to-peer viaWebTorrent in HTML5. Particularly, they offer Web video providers a library that has various parameters, for instance to limit the bandwidth available for peer-to-peer uploads.

Recently, the US Department of Transportation’s Federal Aviation Administration and other international organizations have proposed a set of requirements for small unmanned aerial vehicles (UAVs) to operate for nonrecreational purposes. However, existing UAV architectures fulfill only some of the established requirements, and not all in one solution. This article presents an event-driven service-oriented architecture that allows autonomous UAVs to satisfy all these requirements and to detect critical situations, performing real-time decision making.

In the last years, the Smart City term has appeared in roadmaps and digital agendas for many public administrations in both regional and national contexts. Following this trend, many cities have made important efforts to deploy a network of sensors with the aim of gathering a huge amount of networking related data. Most of these cities are publishing their data through Open Data portals in order to facilitate access and re-use of these data by third parties. Unfortunately, just 5% of the gathered data are currently processed; therefore, the actual contributions extracted from the usage of these data are far from the potential benefits that they may offer. This work presents a first prototype based on the Geo-Temporal Dynamic Activity Flows concept that ease processing and consuming these data. These Dynamic Activity Flows are based on the usage of a resource commonly used to represent cities, their maps.

Due to their nature, Eolic parks are situated in zones with difficult access. As a result, management of Eolic parks using remote sensing techniques is of great importance. In addition, the huge amount of data managed by Eolic parks, together with their nature (distributed, heterogeneous, produced, consumed at different times, etc.) makes them ideal to apply big data techniques. In this paper, we present a multilayer hardware/software architecture that applies cloud computing techniques for managing big data from Eolic parks. This architecture allows tackling the processing of large, distributed, and heterogeneous data sets in a remote sensing context. An innovative contribution of this work is the combination of different techniques at three different layers of the proposed hardware/software architecture for Eolic park big data management and processing.

The number of video-surveillance applications has increased in the last years due to the increasing security requirements, especially in public buildings. In that sense, new types of applications have appeared with new services. As an example, digital applications allow the possibility of remotely controlling the video through internet. However, most of these applications are implemented ad-hoc. They are very tied to the programming level reducing thus their reusability, configurability and maintainability. In this setting, we propose a Model-Driven approach to develop video-surveillance applications. This approach is based on the utilization of a Domain Specific Language (DSL) that allows the design of these applications at the modeling level. The utilization of models enhance the reusability of the services provided by the systems but also aims at managing their variability making them highly configurable. The models built based on this DSL are then used by model transformations that are able to automatically generate the configuration of the applications so that they may be dynamically reconfigured. Moreover, the approach provides different versions of the system, namely a standalone application, a web application or an application for mobile devices. All these applications allow the remote control of the video installation and the integration of external services such as SMS alerts, vehicles' plate recognition or smart home control.

Assistive Software Recommender (ASR) systems in the context of interoperability architectures.Requirements and design of ASR systems.Implementation and deployment of an ASR system.We report the non-functional assessment of our ASR system: performance and scalability evaluation. Assistive Software offers a solution for people with disabilities to manage specialized hardware, devices or services. However, these users may have difficulties in selecting and installing Assistive Software in their devices for managing smart environments. This paper addresses the requirements of these kinds of systems and their design in the context of interoperability architectures. Our solution follows a semantic approach, for which ontologies are a key. The paper also presents an implementation of our design proposal, i.e., a real and usable system which is evaluated according to a set of functional and non-functional requirements here proposed.

Rich Internet Applications (RIAs) have become a common platform for Web developments. Its adoption has been accelerated thanks to different factors, among others, the appearance of patterns for typical RIA behaviors and the extension of different Model Driven Web Engineering methodologies to introduce RIA concepts. The real fact is that more and more developers are switching to RIA technologies and, thus, the modernization of legacy Web applications into RIAs has become a trend topic. However, this modernization process lacks of a systematic approach. Currently, it is done in an ad hoc manner, being expensive and error-prone. This work presents a systematic process to modernize legacy Web applications into RIAs. The process is based on the use of traceability matrices that relate modernization requirements, RIA features and patterns. Performing some operations on these matrices, they provide the analyst with the necessary information about the suitability of a pattern or set of patterns to address a given requirement. This work also introduces two measures, the degree of requirement realization and the degree of pattern realization, which are used to discuss the pattern selection. Finally, the applicability of the approach is evaluated by using it in several Web systems.

In recent years, the increasing use of social networks and applications—especially those used in an asymmetric way—has significantly changed the business processes in many organizations. These applications provide new cooperative ways of performing these processes by taking advantage of the interactions among users. However, the high number of these applications has led to a lack of automation in their interactions and, thus, the need of manually connecting to these networks to perform recurrent and repetitive tasks. In order to automate these operations, this paper presents SCPL, a Domain Specific Language (DSL) that enables connectivity among different social networks and applications and provides a way to automate their management. The main contribution of this paper is showing how SCPL can be used to specify collaborative tasks using symmetric and asymmetric social networks in a transparent way.

Rich Internet Applications (RIAs) technologies are challenging the way in which the Web is being developed. However, from the UI accessibility point of view, these technologies pose new challenges that the Web Accessibility Initiative of the W3C is trying to solve through the use of a standard specification for Accessible Rich Internet Applications (WAI-ARIA). Currently, the introduction of properties defined in WAI-ARIA is being done in an ad-hoc manner due to the lack of models, methodologies and tools to support the design of accessible RIA UIs. In this paper we propose a semantic approach to deal with this modeling issue by extending the RUX-Method, a model-based method to build RIA UIs. The approach includes the validation process of the accessibility issues at two different levels: the UI structure and the interactions behavior.

A steadily growing trend in Web applications is the development of user interfaces through rich Internet applications. Among other capabilities, RIAs offer high interactivity and native multimedia support, giving them a major advantage over standard HTML To update existing HTML Web applications, the authors propose the RUX-Model, which facilitates the user interface adaptation of existing Web 1.0 applications to Web 2.0. Their proposal focuses on new RIA capacities that exploit the data and business logic already provided in legacy Web models.

In the last years, the increasing use of social networks and applications has significantly changed the business processes in many organizations. These applications provide new cooperative ways of performing these processes by taking advantages of the interactions among users. However, the high number of these applications has lead to a lack of automation in their interactions and, thus, the need of manually connecting to these networks to perform recurrent and repetitive tasks. In order to automate these operations, this paper presents SCPL, a Domain Specific Language (DSL) that enables the connectivity among different social networks and applications and provides a way to automate their management. The main contribution of this paper is showing how SCPL can be used to specify collaborative tasks using social networks in a transparent way.