Miguel Torres-Ruiz

Innovative Mobile Information Systems: Insights from Gulf Cooperation Countries and All Over the World

Mobile Information Systems and applications have received growing attention in recent years from various perspectives. The thriving numbers behind mobile systems adoption and contribution have captured the attention of computer engineering and business researchers that, in the past years, have been trying to decipher the phenomenon of mobile information systems, its relation to already-conducted research, and its implications for new research opportunities that effect innovation, entrepreneurship, and world economy dynamics. The current mobile information systems and applications show cases in the Gulf Cooperation Council countries landscape and worldwide presenting a very interesting picture. Several big scale information systems provide a variety of services to local citizens and foreigners promoting top quality research towards the enhancement of the local economies and their transformation to knowledge based high effective sustainable economies. These widely accepted mobile information systems endeavors demonstrate that a wide range of mobile applications are available and present a viable and robust alternative to traditional desktop stand-alone solutions. The objective of the special issue is to communicate and disseminate recent computer engineering and business research and success stories that demonstrate the power of mobile computing to improve traditional information technologies and their exploitation approaches. The purpose of the special issue is to demonstrate state-of-the-art approaches of mobile information systems that have had successful application and to show how new and advanced business models and adoption strategies can expand the sustainability frontiers in advanced applied computer engineering and update the global agenda of innovations in mobile information systems and applications.

A collaborative learning approach for geographic information retrieval based on social networks

Method for geographic information retrieval based on matching-query layers is defined.The approach builds an ontology that defines the time, space and social features.The work defines particular cases in GIR for GIScience collaborative learning.Application of the work establishes adequate user profiles for information retrieval. Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases.

A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning

Abstract Nowadays, people are practicing physical exercise in order to maintain good health conditions. Such physical workouts are required by a plan, which should be designed and supervised by sport specialists and medical assistants. Thus, the exercise sessions shall start with consultation of a coach, doctor and dietician; however, many times this scenario is not presented. In typical activities such as running, cycling and fitness, people use health mobile apps with their smartphones, which offer support for these activities. Nevertheless, the functionality and operation of these applications are isolated, because many and long questionnaires are performed. Additionally, the physical and health state of a user is not considered. These issues would be taken into account for determining recommendations about the time for doing exercise and the kind of activity for each person. In this work, a social semantic mobile framework to generate recommendations where a mobile application allows sensing the physical performance, taking into consideration medical criteria with smartphones is proposed. The approach includes a semantic cross-information that comes from social network and official data as well as sport activities and medical knowledge. This knowledge is translated into application ontologies related directly to health, nutrition and training domains. The methodology also covers physical fitness tests and a monitoring tool for evaluating the nutrition plan and the correct execution of the training. As case study, the mobile application offers to evaluate the physical and health conditions of a runner, automatically generate a nutrition plan and training, monitor plans and recomputed them if users make changes in their routines. The data provided from the social network are used as feedback in the application, in order to make the training and nutrition plans more flexible by applying spatio-temporal analysis based on machine learning. Finally, the generated training and nutrition plans were validated by specialists, they have demonstrated 82% of effectiveness rate in exercise training routines and 86% in nutrition plans. In addition, the results were compared with isolated approaches and manual recommendations made by specialists, the obtained overall performance was 81%.

Geocoding Tweets Approach Based on Conceptual Representations in the Context of the Knowledge Society

In this paper, an approach to geocode tweets published in Spanish is proposed. The tweets are related to traffic events within an urban context of the Mexico City. They are generated by a particular phenomenon for knowing the behavior of the involved geographic entities. In order to disambiguate and verify the consistency of information, an application ontology was defined. Thus, the core goal is to identify location as well as spatial relationships between entities presented in the events, using semantic and spatial analysis of the collected dataset. In consequence, a visualization method for presenting the results was also proposed. The paper describes the methodology for enabling the discovery of spatial patterns within traffic tweets and provides useful information to make timely decisions and contribute in the context of Knowledge Society.

A Mobile Information System Based on Crowd-Sensed and Official Crime Data for Finding Safe Routes: A Case Study of Mexico City

Mobile information systems agendas are increasingly becoming an essential part of human life and they play an important role in several daily activities. These have been developed for different contexts such as public facilities in smart cities, health care, traffic congestions, e-commerce, financial security, user-generated content, and crowdsourcing. In GIScience, problems related to routing systems have been deeply explored by using several techniques, but they are not focused on security or crime rates. In this paper, an approach to provide estimations defined by crime rates for generating safe routes in mobile devices is proposed. It consists of integrating crowd-sensed and official crime data with a mobile application. Thus, data are semantically processed by an ontology and classified by the Bayes algorithm. A geospatial repository was used to store tweets related to crime events of Mexico City and official reports that were geocoded for obtaining safe routes. A forecast related to crime events that can occur in a certain place with the collected information was performed. The novelty is a hybrid approach based on semantic processing to retrieve relevant data from unstructured data sources and a classifier algorithm to collect relevant crime data from official government reports with a mobile application.

Environmental Noise Sensing Approach Based on Volunteered Geographic Information and Spatio-Temporal Analysis with Machine Learning

In this paper a methodology for analyzing the behavior of the environmental noise pollution is proposed. It consists of a mobile application called ‘NoiseMonitor’, which senses the environmental noise with the microphone sensor available in the mobile device. The georeferenced noise data constitute Volunteered Geographic Information that compose a large geospatial database of urban information of the Mexico City. In addition, a Web-GIS is proposed in order to make spatio-temporal analysis based on a prediction model, applying Machine Learning techniques to generate acoustic noise mapping with contextual information.According to the obtained results, a comparison between support vector machines and artificial neural networks were performed in order to evaluate the model and the behavior of the sensed data.

Semantic Recommender System for Touristic Context Based on Linked Data

The lack of personalization presented in touristic itineraries that are offered by travel agencies involve a little flexibility. Basically, they are designed with the points of interest (POIs) that have more relevance in the area. On the other hand, there are POIs that have agreements with the agencies, which originate a excluding POIs that could be interesting for the tourist. In this work, a method capable to use the user preferences, like POIs and activities that user wants to realize during their vacations is proposed. Moreover, some weighted features such as the max distance that user wants to walk between POIs, and opinions of other users, coming from the web 2.0 by means of social media are taken into account. As result, a personalized route, which is composed of recommended POIs for the user and satisfied the user profile is provided.

A recommender system to generate museum itineraries applying augmented reality and social-sensor mining techniques

Nowadays, museums offer technological and digital options to enrich the user experience in a visit. However, questions arise like which exhibition/museum could I visit? How to tour it and get the best experience? These questions are not easy to answer, because they do not represent tasks straightforward. Considering that the experiences of visiting a museum are now available in social networks, in which users describe, rate, and disseminate a work of art/exhibition of a museum, this information can be mined to generate tour recommendations in museums. Such recommendations could be improved by combining and applying data mining obtained from Internet of Things sensors installed in museums. In this paper, a hybrid approach to make recommendations for museum visits is proposed. It includes an Internet of Things architecture of beacons, incorporating some technologies based on semantic analysis, data mining, and machine learning. This approach integrates and combines data sources for generating and recommending indoor and outdoor itineraries for museums, which are visualized with augmented reality. The itinerary is built, taking into consideration opinions and assessments from social networks, the semantic classification of museums, and cultural activities, as well as data measured by beacon sensors in museum exhibitions. The result is a customized tour with augmented reality that contains a set of recommendations of how to visit a set of museums and obtain a better experience of the visit. A prototype of mobile application is available in the Google Play, called the “Historic Center,” with almost 500 downloads and an acceptable evaluation.

DIS-C: conceptual distance in ontologies, a graph-based approach

This paper presents the DIS-C approach, which is a novel method to assess the conceptual distance between concepts within an ontology. DIS-C is graph based in the sense that the whole topology of the ontology is considered when computing the weight of the relationships between concepts. The methodology is composed of two main steps. First, in order to take advantage of previous knowledge, an expert of the ontology domain assigns initial weight values to each of the relations in the ontology. Then, an automatic method for computing the conceptual relations refines the weights assigned to each relation until reaching a stable state. We introduce a metric called generality that is defined in order to evaluate the accessibility of each concept, considering the ontology like a strongly connected graph. Unlike most previous approaches, the DIS-C algorithm computes similarity between concepts in ontologies that are not necessarily represented in a hierarchical or taxonomic structure. So, DIS-C is capable of incorporating a wide variety of relationships between concepts such as meronymy, antonymy, functionality and causality.

On the usage of sorting networks to control greenhouse climatic factors

The goal of this article is the application of a non-adaptive classification algorithm to support the variable management process for internal climate control. The protected agriculture has given many advantages for the care and improvement in the production of almost any food. This work is focused on improving a control system for climate variables. The decision for activating an actuator for the correct care of the crop is very important. A sorting network technique with predefined compare–interchange operations and designed to order data very efficiently is proposed. This approach has been applied to the process of handling actuators within a control system. An advantage of using the sorting networks is that it has an inflexibility when processing a data list; it always takes the same units of time and is executed in the same number of operations for an input size of n. Sorting network–based applications are scarce in the state-of-the-art because there are not many techniques that are effective for sorting very small data sizes. The use of sorting networks in the process of assessing the determination of actuators is proposed. This operation is scheduled in the design of control and performed at each reading back data.