Rolando Quintero

Ontology-Driven description of spatial data for their semantic processing

We use ontologies in this paper to search for alternative representations of geographic objects thus providing a description of these objects in cartographic vector maps. We define ontologies based on two types of concepts (“terminal” and “non-terminal”) and two kinds of relations (“has” and “is-a”). These are the basic elements used to describe a map. We also present a case study in which an ontology for topographic maps is created. Our approach is oriented towards solving heterogeneity and interoperability issues in GIS.

An ontology-based approach for representing the interaction process between user profile and its context for collaborative learning environments

This work is focused on defining user profiles based on ontologies.The research proposes the evolution operation that interacts with ontologies.This work defines an approach to represent the interaction process between user profiles and their context.The application of this work is oriented toward establishing adequate user profiles for recommender systems. Recent researches about the personalized content generation have focused their efforts on two main topics: the first topic is the user model definition, i.e. the dimensions to be taken into account to represent the user, and the second topic is about the techniques used by recommender systems to provide recommendations according to the user requirements, such as adaptive approaches for context-aware systems, collaborative learning, and recommender systems for mobile environments. In this work, an approach based on ontologies to represent the interaction process between user profile and its context for collaborative learning is presented. We also analyzed the role assignments, permissions, restrictions and the definition of rules that are applied to the user, particularly in the collaborative learning context where the subject is involved. A case study related to the context of a school as well as the defined roles by the occupations in the context of locations is proposed.

GEONTO-MET: an approach to conceptualizing the geographic domain

To date, there are different ontologies for many domains and applications. Users can access them to share information, reuse knowledge, and integrate data sources for several purposes and applications such as semantic web, data warehousing, e-learning, e-commerce, knowledge representation, and so on. Ontology engineering is rapidly becoming a mature discipline, having produced tools and methodologies for building and managing ontologies. However, even with a clearly defined engineering methodology, building an ontology remains a challenging, time-consuming, and error-prone task, because it forces ontology builders to conceptualize their expert knowledge explicitly and to re-organize it in typical ontological categories such as concepts, properties, and axioms. In this article, an approach to conceptualizing the geographic domain is described. It is oriented toward formalizing a geographic domain conceptualization according to specifications from the Mexican Institute of Statistics, Geography and Informatics. The main goal is to provide semantic and ontological descriptions, which represent the properties and relationships that describe the behavior of geographic objects by means of concepts. GEONTO-MET is focused on developing geographic application ontologies for sharing and integrating geospatial information.

Hardware implementation of the elitist compact Genetic Algorithm using Cellular Automata pseudo-random number generator ☆

Abstract In this paper the design and implementation of two versions of the compact Genetic Algorithm (cGA), with and without mutation and elitism, and a Cellular Automata-based pseudo-random number generator on a Field Programmable Gate Arrays (FPGAs) are accomplished. The design is made using a Hardware Description Language, called VHDL. Accordingly, the obtained results show that it is viable to have this searching algorithm in hardware to be used in real time applications.

Geomorphometric Analysis of Raster Image Data to detect Terrain Ruggedness and Drainage Density

We present an approach to identify some geomorphometrical characteristics of raster geo-images. The identification involves the generation of raster layers, topographic ruggedness and drainage density. The topographic ruggedness is used to express the amount of elevation difference between adjacent cells of Digital Elevation Model (DEM). The topographic ruggedness is presented by means of Terrain Ruggedness Index (TRI). The densities layers are obtained by Spline Interpolation Method. These layers are used to represent the amount of geographic linear objects. The algorithm has been implemented into Geographical Information System (GIS) – ArcInfo, and applied for a GIS of Tamaulipas State, Mexico.

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.

Geospatial information integration based on the conceptualization of geographic domain

Geospatial information integration is not a trivial task. An integrated view must be able to describe various heterogeneous data sources and its interrelation to obtain shared conceptualizations. Up-to-date, there are different and public ontologies for many domains and applications. Ontology engineering is rapidly becoming a mature discipline, which has produced various tools and methodologies for building and managing ontologies. However, even with a clearly defined engineering methodology, building a large ontology remains a challenging, time-consuming and error-prone task, since it forces ontology builders to conceptualize their expert knowledge explicitly and to re-organize it in typical ontological categories such as concepts, properties and axioms. In this paper, an approach to conceptualize the geographic domain is described. As a result of this conceptualization, we propose a semantic method for geospatial information integration. This consists of providing semantic descriptions, which explicitly describe the properties and relations of geographic objects represented by concepts, while the behavior describes the objects semantics. Summing up, this work presents a methodology allowing integrate and share geospatial information. It provides feasible solutions towards these and other related issues such as compact data by alternative structures of knowledge representation and avoids the ambiguity of these terms, using a geographic domain conceptualization. The general vision of the paper is to establish the basis to implement semantic processing oriented to geospatial data. Future works are focused on designing intelligent geographic information systems (iGIS).

Landform Classification in Raster Geo-images

We present an approach to perform a landform classification of raster geo-images to obtain the semantics of DEMs. We consider the following raster layers: slope, profile curvature and plan curvature, which have been built to identify the intrinsic properties of the landscape. We use a multi-valued raster to integrate these layers. The attributes of the multi-valued raster are classified to identify the landform elements. The classification approach is used to find the terrain characteristics of the water movement. Moreover, we describe the mechanisms to compute the primary attributes of digital terrain model. The method has been implemented into Geographical Information System-ArcInfo, and applied for Tamaulipas State, Mexico.

Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images

We present an approach to color image segmentation by applying it to recognition and vectorization of geo-images (satellite, cartographic). This is a simultaneous segmentation-recognition system when segmented geographical objects of interest (alphanumeric, punctual, linear, and area) are labeled by the system in same, but are different for each type of objects, gray-level values. We exchange the source image by a number of simplified images. These images are called composites. Every composite image is associated with certain image feature. Some of the composite images that contain the objects of interest are used in the following object detection-recognition by means of association to the segmented objects corresponding “names” from the user-defined subject domain. The specification of features and object names associated with perspective composite representations is regarded as a type of knowledge domain, which allows automatic or interactive system’s learning. The results of gray-level and color image segmentation-recognition and vectorization are shown.

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%.