This paper presents the definition, use, and evaluation of intelligent management frameworks for global cooperation. The research work brings new concepts and ideas to design new management models and artificial intelligence solutions in sustainable environments. An intelligent management framework is a flexible and efficient vertical association of models, architectures, and processes. It is a mixed (architectural and methodological) association of services and procedures across IT departments of global organizations. The paper presents a general top‐down approach to design these frameworks for global, cooperative models of intelligence. The approach includes five levels of abstraction and three refinement techniques. These elements are used to design an evaluation case study with global services and process‐oriented cooperation for current sustainable targets in education. In our future work, we will implement these management solutions for government organizations currently involved with digital transformations.

Survival analysis is an area of statistics that deals with the study of the time it takes for an event of interest to occur, usually applying mainstream statistical techniques. The appearance of the Big Data phenomenon and the digitization of medical information lead to the generation of huge amounts of information that require optimal processing. For this, it is necessary to apply new algorithms, such as machine learning techniques. This article introduces a REST API based on machine learning to predict survival using categorical features.

One of the main problems that affect patients in dialysis therapy who are on the waiting list to receive a kidney transplant is predicting their survival time if they do not receive a transplant. This paper proposes a new approach to survival prediction based on artificial intelligence techniques combined with statistical methods to study the association between sociodemographic factors and patient survival on the waiting list if they do not receive a kidney transplant. This new approach consists of a first stage that uses the clustering techniques that are best suited to the data structure (K-Means, Mini Batch K-Means, Agglomerative Clustering and K-Modes) used to identify the risk profile of dialysis patients. Later, a new method called False Clustering Discovery Reduction is performed to determine the minimum number of populations to be studied, and whose mortality risk is statistically differentiable. This approach was applied to the OPTN medical dataset (n = 44,663). The procedure started from 11 initial clusters obtained with the Agglomerative technique, and was reduced to eight final risk populations, for which their Kaplan-Meier survival curves were provided. With this result, it is possible to make predictions regarding the survival time of a new patient who enters the waiting list if the sociodemographic profile of the patient is known. To do so, the predictive algorithm XGBoost is used, which allows the cluster to which it belongs to be predicted and the corresponding Kaplan-Meier curve to be associated with it. This prediction process is achieved with an overall Multi-class AUC of 99.08 %.

A general need for tourists is the availability of guides that tell them what to see in the cities they visit. Normally, paper guides are used, mobile applications that digitize physical guides or official tourism services that inform tourists in person. However, in the last decade, huge amounts of data released by institutions and companies have become available in many cities that can be used to create value-added services. This article describes the development of a mobile application that aims to facilitate tourist visits to the city of Madrid, so that the functionality it offers is implemented as a value-added service that uses data from various sources of information in order to create a system that offers multiple functions aimed at planning visits to monuments and other places of interest. The value of this application lies in the exploitation it carries out on the data it collects, from which it creates services such as recommending plans, generating itineraries, planning visits or searching for plans using geolocation.

The aggregation model is the foundation of some of the main NoSQL databases. This model is characterized because the basic element of management is the concept of aggregate. Furthermore, another characteristic is that it is not necessary to create a definition schema in the database to store information as in the case of relational databases. It is for this reason that in general there are no sophisticated design tools for databases oriented towards aggregates since the schema is implicit in the stored information. However, a design tool allows better management of the information to be stored and especially the maintenance of the database. This article presents a visual tool that allows you to design a NoSQL database oriented towards aggregates, as well as manage different designs. On the other hand, the tool allows generating instances of the design containing data for a MongoDB-type document database.

One of the characteristics of the Big Data phenomenon is the possibility of accessing huge amounts of data from a wide variety of topics. One type of data source is called open data repositories. These are digital repositories in which public and private institutions provide information about their activity and which can usually be accessed for free. A specific case is the open data repository of the Madrid City Council where you can find data on its activity as well as information about the city. From these repositories it is possible to implement applications that use this data to create value-added services. Another source of data that has emerged in the context of Big Data has been the sensors used in different devices such as smartwatches, mobiles, fridges, etc. This phenomenon is called the Internet of Things, and refers to the possibility of using data from sensors in order to provide intelligent services to users. This article presents an Android application that implements an added value service that allows to retrieve information about tourist monuments in the city of Madrid that are close to where the user is. For this, it is used the geolocation information provided by the sensors from the smartphone in order to retrieve information from the open data repository of the Madrid city council about tourist monuments near the current location.

Currently there are numerous repositories of linked data and open data that store information from very varied domains. These repositories can be consulted to retrieve the data they store using query languages. Normally, the result of the queries generates a file containing the requested data represented in some information representation format. This article presents a process model to create learning resources in a simple and fast way using these repositories can be used As example of this model, it has been created a web application in Python that suggests paintings and other artistic objects from the museums of the city of Madrid and allows to build, in a semi-automatic way, a set of proposals for learning activities oriented to their students. In order to it, the application exploits several repositories of open data portals have been exploited: Wikidata and the Madrid City Council open data portal.

Interactive fiction is a part of digital literature that promotes the active role of the reader in the reading process of an electronic book (e-book). For this purpose, multimedia resources are included within the content of the e-book, with which the reader has to interact. As a consequence, a richer reading experience is promoted, according to which the reader must decide how to continue the story he/she is reading, and, in turn, these decisions condition the future course of the story. However, the main problem faced by writers of interactive fiction is the lack of editors that make it easier for them, who are not necessarily experts in interactive content design, to use these strategies in the contents of their e-books, and which in turn offer them enough narrative flexibility. For this purpose, we have developed IFDBMaker, an interactive fiction authoring tool oriented to writers with no prior knowledge in digital content production. This tool is equipped with a web-based interface and lets non-technical writers use sophisticated narrative resources while authoring interactive fiction e-books with the sole support of a simple web browser.

Interactive fiction is an area of work within digital literature that aims to insert elements within the content of a digital book that force the reader to participate actively in reading the book. For this, strategies are used such as for the reader to make decisions about which is the next chapter to read or how the story told at a certain point in the book should continue. In this article we present a software tool oriented to the edition of digital books that implements diverse functionalities to insert elements of interactive fiction following the stretchtext hypertext paradigm.

Many e-learning tools are based on domain-specific languages (DSLs) targeted to the educational domain. Thus, methods and techniques from the programming language community can help in developing these tools. In this paper, we show how attribute grammars, a well-known declarative specification method for the syntax and semantics of programming languages, can facilitate the production and subsequent evolution of e-learning tools. We also describe how we produced and extended, a courseware system supporting an XML-based DSL, by using XLOP (XML Language-Oriented Processing), a meta-tool supporting attribute grammars for the development of XML processing applications.

The services provided by learning object repositories are usually enabled by the processing of the metadata documents associated with these learning objects. This paper proposes a way to process these metadata documents, which are usually encoded in XML, through a framework called XLOP. XLOP is based on attribute grammars, a well-known technique used in the development of language processors. XLOP makes the automatic generation of efficient XML processing components from high-level specifications possible, enhancing the maintainability of the aforementioned services. The technique is illustrated in the context of Chasqui, a system for building repositories of learning objects in specialized domains.

Agrega is a federation of Scorm 2004 learning object repositories, with nodes situated in each of the Autonomous Regions of Spain. Each repository offers a group of services for managing and using the learning objects that it stores. The operations performed on the objects are based on the metadata described in an XML document called imsmanifest which every object has. Together with Agrega, a set of tools has been developed aimed at the end user, for use outside the repositories. One of these is the Agrega offline tool. It consists of a group of utilities integrated under a single tool which enables you to perform editing and management operations similar to those which can be performed on the learning objects from this node. This article describes the functionalities offered by Agrega Offline and how they are applied in different use scenarios.

This paper presents the scene and the elements that take part in the definition of a quality model in the learning objects arena and describes the difficulties that appear during its definition, summarizing the current open research issues.