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MEMORIA DEL ENCUENTRO NACIONAL DE CIENCIAS DE LA COMPUTACIîN

___________________ ENC 2014 ___________________

3, 4 y 5 de Noviembre, 2014

NovaUniversitas, Ocotl�n, Oaxaca, M�xico

Instituciones Participantes:

Universidad Aut�noma de Baja California, UABC Instituto Tecnol�gico de Sonora, ITSON Instituto Tecnol�gico de Estudios Superiores de Monterrey, ITESM Sistema de Universidades Estatales de Oaxaca, SUNEO Centro Nacional de Investigaci�n y Desarrollo Tecnol�gico, CENIDET Instituto Nacional de Astrof�sica, îptica y Electr�nica, INAOE Centro de Investigaci�n Cient�fica y de Educaci�n Superior de Ensenada, CICESE Centro P�blico de Innovaci�n y Desarrollo Tecnol�gico, INFOTEC Universidad Tecnol�gica de la Mixteca, UTM

Asociaciones que apoyan:

Sociedad Mexicana de Ciencias de la Computaci�n Asociaci�n Mexicana de Interacci�n Humano Computadora AC

Editada por:

Dra. Marcela D. Rodr�guez, Dr. Ana I. Mart�nez Garc�a y Dr. Juan Pablo Garc�a V�zquez

Preface

Las diferentes ramas de la Computacion no dejan de ser disciplinas en evolucion que

congregan a investigadores y practicantes en todo el mundo. La Sociedad Mexicana de

Ciencias de la Computacion (SMCC) tiene el proposito de fomentar la colaboracion en-

tre la comunidad de investigadores mexicanos, promoviendo sus lıneas de investigacion

y dando a conocer sus contribuciones. Para lograr lo anterior, organiza el Encuentro

Nacional de Ciencias de la Computacion (ENC), tambien conocido en otras ediciones

como Congreso Internacional de Ciencias de la Computacion. Ası, el ENC se distingue

por ser el evento que anualmente organiza la SMCC, llevandose a cabo este ano, del

3-6 de Noviembre y teniendo como sede a Nova Universitas, Universidad del sistema

SUNEO, situada en Ocotlan de Morelos, Oaxaca.

Con el objetivo de identificar e integrar a las comunidades cientıficas de distintas

areas de la Computacion con presencia en Mexico, el ENC’2014 se organizo como un

conjunto de Talleres, Simposio de Posgrado y Sesion de Carteles, cuyo llamado se definio

por los intereses de las comunidades activas en el paıs. De esta forma, el ENC quedo

conformado principalmente por 8 talleres: Computacion Clınica e Informatica Medica

(CCIM), Investigacion y Aplicacion de la Ingenierıa de Software (IAIS), WorkShop on

Network Systems and Protocols (WNSP), Biocomputacion, Aspectos algorıtmicos de

Sistemas Computacionales, Tecnologıas Emergentes en la Educacion (TTEE), Aplica-

ciones de Computo Suave (TACS), 2nd Workshop on Semantic Web and Linked Open

Data (SW-LOD).

La publicacion de este libro digital, con ISBN 978-0-9908236-0-5, corre a cargo de

la SMCC, de la Universidad Autonoma de Baja California (UABC), del Centro de

Investigacion Cientıfica y de Educacion Superior de Ensenada (CICESE) y de Nova

Universitas. Contiene 95 trabajos seleccionados de un total de 148; los cuales fueron

previamente seleccionados por los respectivos comites de programas.

A nombre de la Sociedad Mexicana de Ciencias de la Computacion, enfatizamos

nuestro agradecimiento a todos los miembros de la SMCC pertenecientes al INAOE,

CICESE, CENIDET, INFOTEC, ITESM, ITSON, UABC, quienes sin el apoyo de sus

instituciones, no hubiera sido posible realizar este evento. Agradecemos en particular

a la Asociacion Mexicana de Interaccion Humano Computadora (AMEXIHC) por su

gran colaboracion para tomar decisiones de logıstica y conjuntar las audiencias de

los Congresos MexIHC y ENC. Queremos tambien agradecer a los organizadores de

Talleres, Consorcio de Posgrado y Sesion de Carteles, y a los Comites de Revision, por

su esfuerzo y tiempo invertido para asegurar la calidad de los trabajos publicados en

este libro.

Agradecemos encarecidamente al Dr. Modesto Seara Vasquez, Rector del SUNEO, a

M.E.C. Josue Neftalı Garcıa Matıas, Vice-Rector Academico. Y a todos los miembros

del Comite Local, presididos por Dr. Raul Cruz Barbosa, Director del Instituto de

Computacion de la Universidad Tecnologica de la Mixteca y por M.C. Mario A Moreno

Rocha, Director Academico del UsaLab Laboratorio de Usabilidad de la Universidad

Tecnologica de la Mixteca. Especialmente, al Comite Local agradecemos su disposicion

incondicional en la organizacion del ENC y la hospitalidad brindada. Finalmente,

agradecemos a todos los participantes del ENC’2014 por el interes en participar y

contribuir a que este evento sea el que caracterice a la comunidad cientıficaen Ciencias

v

de la Computacion de Mexico. Hacemos una mencion especial a los administradores

del sistema easychair, por permitirnos accederlo.

Octubre 24, 2014

Mexicali, B.C. Mexico

Marcela D. Rodrıguez

Ana. I. Martınez

Juan Pablo Garcıa-Vazquez

vi

Marco arquitectonico para la integracion de recursos educativos

residentes en sistemas heterogeneos mediante el uso de dispositivos

moviles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper58

Jesus Moreno and Victor Menendez

El rol de la interoperabilidad en repositorios de objetos de aprendizaje . . .paper76

Araceli Justo, Gabriel Lopez-Morteo and Emmanuelle Ruelas

Sistema de Aprendizaje Movil Consciente de Contexto . . . . . . . . . . . . . . . . .paper10

German Gomez Castro, Eduardo Lopez Domınguez, Yesenia Hernan-

dez Velazquez and Magdalena Yahaira Rodrıguez Matla

A contextual study and usability testing of videogames to inform the

design of a serious game to improve reading comprehension . . . . . . . . . . . . .paper53

Laura Sanely Gaytan Lugo, Sara C. Hernandez, Pedro Cesar Santana

Mancilla and Miguel A. Garcia-Ruiz

Diseno de actividades de aprendizaje usando mesas multitouch:

Consideraciones pedagogicas y tecnologicas . . . . . . . . . . . . . . . . . . . . . . . . . . .paper77

Joaquın Morales Alfaro, Rene Cruz Flores and Magally Martınez Reyes

Incorporacion de aplicaciones moviles dentro de las tecnicas didacticas

de aprendizaje para los nuevos modelos educativos. . . . . . . . . . . . . . . . . . . . .paper38

Luis Jose Gonzalez Gomez and Gilberto Huesca Juarez

Learning Models and Information Dissemination in the Subject of

Organ and Tissue Donation Using web 2.0 Tools: Facebook, SlideShare,

and

YouTube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper71

Ximena Cabezas, Santos Lazzeri, Luis Ojeda and Francisca Leiva

Mundo Virtual de una cocina para ninos con capacidades diferentes . . . . .paper88

Lorena Perez Sanchez, Norma Sanchez Sanchez and Marva Angelica

Mora Lumbreras

Modelo para consultas semanticas sensibles al contexto sobre recursos

educativos estructurados con OAI-PMH . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper49

Arianna Becerril Garcıa, Rafael Lozano Espinosa and Jose Martın

Molina Espinosa

Experiencia de diseno y uso de una herramienta de aprendizaje

colaborativa para la educacion superior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper93

Danice Deyanira Cano Barron, Humberto Jose Centurion Cardena,

Mario Renan Moreno Sabido and Didier R. Moreno Vazquez

Workshop on Semantic Web and Linked Open Data (SW-LOD)

Semantic Sensors:A Proposal From Smart Building to Smart City

Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper50

xii

Oscar Hernandez, Domingo Guinea and Matilde Santos

Ontology-Driven Financial Regulatory Change Management: An

Iterative Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper95

Angelina Espinoza, Elie Abi-Lahoud and Tom Butler

Integrating Heterogeneous Databases Based on Semantic . . . . . . . . . . . . . . .paper4

Zhenzhen Gu and Songmao Zhang

Multidimensional Ontology Network for Organizational Environments:

An Application to Context-Aware Recommender Systems . . . . . . . . . . . . . .paper56

Nimrod Gonzalez-Franco, Hugo Omar Alejandres-Sanchez, Juan Gabriel

Gonzalez-Serna, Julia Yazmın Arana-Llanes and Azucena Montes-Rendon

Designing a thematic social network with research methodology

content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper122

Marıa Auxilio Medina Nieto, Karina Avelino Camacho, Jorge De La

Calleja and Antonio Benitez

Design Issues for the Development of Linked Data Content Management

Systems with Reasoning Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper131

Ernesto Giralt

GeoHealthOntoMex: Extendiendo una Ontologıa Geografica usando

Minerıa de Datos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper32

Marıa J Somodevilla, Concepcion Perez de Celis and Dr Ivo H. Pineda

Torres

Busqueda automatica de datasets de dominio en la nube de Linked

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper90

Jose Alfredo Temiquelt Ortiz, Alicia Martınez Rebollar and Hugo Estrada

Esquivel

A Distributed Model based on Ontologies to query Relational

Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper119

Francisco-Edgar Castillo-Barrera, Reyna-Carolina Medina-Ramırez, Sandra-

Edith Nava-Munoz and Claudia-Alicia Mendez-Hernandez

A service based on Linked Data to classify Web resources using a

Knowledge Organisation System A proof of concept in the Open

Educational Resources domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper134

Janneth Alexandra Chicaiza Espinosa, Nelson Piedra, Jorge Lopez-

Vargas and Edmundo Tovar

Getting Textual Description for a Corporate Memory Resources with

Text Mining Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .paper75

Cristal Karina Galindo Duran, R. Carolina Medina-Ramırez and Mi-

haela Juganaru-Mathieu

xiii

Semantic Sensors:A Proposal From Smart Building to

Smart City Model.

Oscar Hernández U.

TIEC,

CIATEQ AC.

Santiago de Querétaro, México.

oscar.hernandez@ciateq.mx

Domingo Guinea,

LERH,

CAR, CSIC-UPM.

Madrid, Spain.

domingo.guinea@ csic.es

Matilde Santos, Facultad de Informática,

UCM.

Madrid, Spain.

msantos@ucm.es

Abstract— Advances in hardware, software and

communications have been pointed out as promising tools for

building a Smart City. These advances have made possible to face

an increasingly complex functionality in different processes that

can be found in any city. Applications called "smart" (smart

building, smart water, smart energy, smart grid, smart city, smart

transport, etc.), start to emerge around the planet. These smart

applications are supported by ICT technologies and they connect

the physical world and its digital representation. Where novel

algorithms and knowledge representation provide with

information which can be used to support decision making for

users, local authorities, and so on. This paper presents a smart

building approach with multiple sensors and the use of a low

enthalpy geothermal system to maintain indoor comfort. We

decided to go a step ahead and go from smart building to a smart

city model of a real location. A smart city model is build and

environment is simulated where semantic web technologies and

linked open data play a key role.

Keywords—smart city; smart building; semantic sensor; KPI’s;

Internet of Things; nZEB;

I. INTRODUCTION

Some concerns from now on to next decades, that are unavoidable to attend are: demographic change –sooner 80% of world population will live in cities-; longevity -life average expectancy is greater-; climate change, and global business. All of them, in one way or another, affect directly economic growth and social live in a country. Events such as migration from town to cities will involve not only to add infrastructures to support population needs, but also to get to know and to explain how well and efficiently population needs are cover to reduce the gap between rich and poor people [1]. On the other hand, recent years have witnessed a rapid advance in hardware and software technologies, where sensors are more sophisticated and adding new functionalities is easier. Smart applications start to emerge around the planet and seem an option to face these kinds of problems.

Following Internet of Things (IoT), it is necessary to link data beyond a local system (for example, a smart building), to other entities (cities). That is, it is necessary to pull and push data down and up from WWW to share and reuse knowledge. Embedded sensor developed at the Renewable Energy Fuel Cells and Hydrogen Laboratory (LERH), CAR_CSIC_UPM, (http://www.car.upm-csic.es/lerh/index.php/en/) not only

generates huge amount of data but they can also enable contextual intelligence. Even more they have all the communications ports necessary to access the web, to account for great amount of data, to link data and interfaces, and to interact with users [2]. This increases the complexity because is needed to consider different entities (people, buildings, weather, heterogeneous devices, etc). This generates new challenges for governments and citizens. But these systems make cities more “intelligent” by integrating smart water systems with smart transport and smart buildings and so on. It is clearly that a large amount of data is out there, gaining access and being able to process heterogeneous data is a key factor to make possible the generation of smart actions. Here is where ontologies, semantic web technologies and linked data tools, methods and standards are useful. Santiago de Querétaro (México) is going in for the smart city concept to be self-sustainable and to improve citizen’s quality of life [3]. The main idea is that users should start to play an active role and city key performance indicators (KPI’s) such as CO2 emissions must be updated in real time. This paper is organized as follows. In next section, basic concepts and related works on semantic sensors are presented. In section III, the steps from a smart building to a smart city model is proposed. Some experimental tests have been carried out and results are shown. Finally conclusions and further work are formulated.

II. RELATED WORK: ONTOLOGIES + SEMANTIC WEB +

EMBEDDED DEVICES = SEMANTIC SENSORS.

By definition “ontology is a formal, explicit specification of a shared conceptualization” [4]. From a very general perspective, it means to build technical specifications of the desired features and requirements by using a formal language. In fact, the deepeners the encoding of these features and requirements is, and how explicitly all of them are formally specified, the ontologies are classified as formal or domain ontologies. The first one is fully described by a set of terms and some specification of their meaning through relationships, axioms and properties, using a representation language to infer knowledge (for instance, DOLCE). The domain ontology is related to relationships, axioms, and properties focus on some specific domain (for example SSNO).

The diversity of computer applications in which the use of ontologies is involved and the need to establish interoperability among multiple representations have favored the emergence of ontological patterns, where FOAF is one example [5]. The use

of ontologies has been widely incorporated to the sensor field where also patterns have started to emerge. For example, W3C Semantic Sensor Networks Incubator Group (SSN-XG) developed an ontology sensor called Semantic Sensor Network Ontology (SSNO). SSNO provides a common vocabulary to support modelling sensor networks. There is also a well-known pattern for semantic sensor observations that is part of OGC’s standards and has been incorporated in SSNO. Stimulus-Sensor-Observation (SSO) designed a pattern as a building block for work on the Semantic Sensor Web and Linked Sensor Data [6]. Also, smart applications using ontologies have been carried out where sensors are used [7-11]. This lets know that the web of documents where primary objects are documents with implicit semantic of content for human use is changing. Semantic Web Vision is the web of linked data where primary objects are things with explicit semantic of content and links, not only for human consumption but also for machine. It makes resources accessible to automated machines and allows the use of logic reasoning to find useful information.

The development of languages oriented to the creation of ontologies has been a key priority. One of the initial proposals was RDF/RDFS to interchange data and create hierarchies of classes and properties (taxonomies). Later on, Web Ontology Language OWL-DL, extends RDFS by adding more advanced constructs to describe things on the Semantic Web. It is based on description logic. SPARQL standard is used to query data that are built according to semantic web vision. There has been a lot of work to create standards such as the mentioned on the famous semantic web layer cake [12-14] where every piece of information is represented by a tuple: Subject, Object and Predicate (S-P-O), called triple (Figure 1). Semantic Web Vision has been broadly applied to BBC, dbpedia, geonames, and governments such as EU, US and UK [15-17].

ssn:Platform

«wsn-MRF24W»

ssn:onPlatformssn:SensingDevice

«Sensor de Temp.»

ssn

:ob

serv

ed

By

ssn:Observation

«Observación Temperatura»

ssn:o

bserv

ationResu

lt

ssn:SensorOutput

«Salida Sensor Temperatura» ssn:Property

«http://dbpedia.org/page/

Temperature»

ssn:observedProperty

ssn:ObservationValue

26.8 °C

ssn:o

bserv

ationSam

pTime

ssn:hasValue

ee

Time:DateTimeInterval

3 min

Time:Instant

15/01/2013 18:56:15

ssn:observationResultTime

DUL:PhysicalPlace

«Habitación 101»D

UL:h

asLo

catio

n

Fig. 1. A sensor network ontology for a smart building showing some triples

(S-P-O). Platform is a physical sensor node (S), it is located in (P) a physical place (O).

Energy consumption in buildings is a global concern. Buildings consume near 40% of energy worldwide and usually the 60% of total energy is spent for heating and cooling to maintain comfort temperature [18-19]. U.S.A. and EU goals by 2020 are to certify almost all buildings as Zero Energy Building (ZEB) [20-21]. Many papers have been published where

ontology for a semantic representation is mentioned [22-25]. Because of that, it is worthwhile to mention that most of the times it is not necessary to start from the scratch to build an ontology but to make use of some of the existing ontologies or use some of the called upper ontologies as skeleton to develop a new one. For example, to build an ontology of sensors, the first step is to search books, journals, and magazines and attend some workshops to enrich the vocabulary about sensors domain. Second step is to get data and to understand the standards related the variable the sensors are going to measure (measurement science). Third step would be to search for related ontologies and vocabularies –both SSNO and FOAF could be a good choice-. Finally, to build your own ontology or to extend the ontology chosen, trying to distinguish and formalize atomic concepts, relations and roles found in the sensor domain by making use of tools such as Protégé, Neon Toolkit or TopBraid [26-27]. Figure 1 illustrates a sensor network ontology for a smart building where the variable is “temperature” showing metadata information around it. In this building it is required to monitor and control many subsystems (Electrical, Thermal, Geothermal, Photovoltaic)

Fig. 2. OIKOS: A first approach to smart building

The first approach for a smart building was “OIKOS

(figure 2) [28-29]. Several challenges where solved using XML

files to exchange information between subsystems such as

photovoltaic panels (PVP) and Proton Exchange Membrane

Fuel Cells (PEMFC), to switch among different strategies of

energy flow. This system was presented in Expo2008, Zaragoza

and nowadays is at CAR-CSIC-UPM in Arganda del Rey,

Madrid.

III. FROM SMART BUILDING TO SMART CITY MODEL.

A. Smart Building.

A project of LERH Laboratory is about a building with a low enthalpy geothermal system. Here, the smart building concept was embraced. Sensors were implemented and enhanced with more complex functionalities. For example, smartphones were included to enable users to be more active (figure 3, upper part). Many electro-valves, pumps and blowers were controlled. Machine learning algorithms were used to predict rain events (logistic regression algorithm) and faulty sensors (Support Vector Machine). Simulations models were used to understand heat flow across walls when hot or cold fluid passes through

them (figure 3, bottom). Metadata was used to introduce information in a specific domain and context. That is, energy efficient management goes with minimal human intervention. For example, instead of just reading the temperature of the wall metadata that define its location (site, building, and floor), parameters of the wall (thermal conductivity, diameter, and layers), are added. A virtual sensor was built to provide user context (figure 4).

Fig. 3. Smart building. Top, the architecture with the sensor network. Solar

energy is collected and stored undeground. Bottom, thermal barrier is used to change thermal temperature gradient dynamically through the wall in order to

adapt it to weather conditions and save energy for heating and cooling.

Fig. 4. Virtual sensor was used to fuse several sensor values. This virtual

sensor is controled by a fuzzy engine. Everything is based on XML files, so a parser was needed.

B. Smart City Model.

Santiago de Queretaro (México), also called the “Pearl of the Bajío” has received the distinction of World Heritage City. Like many other cities, it has a lot of events where natural phenomena along a year are included. Same as a lack of information is a major cause of waste energy building during its lifetime, lack of information about annoying events in city is also a waste of energy for everybody. Quite recently a strong storm generated a chaos among citizens which increased the travel time by a

factor of almost four what at the same time meant more CO2 emission because of the increase in fuel consumption [30].

In Santiago de Querétaro city water utilities and power companies such as State Water Commission (CEA) and Federal Electricity Commission (CFE), have a supervisory control and data acquisition systems (SCADA). CEA collects information from 60 remote stations around the state to better understand water supply in urban areas as well as in rural areas. CFE has started to install smart meters where data is automatically captured, collected and sent to central servers. They also have weather stations spread around the city to know or predict weather conditions. Public transport at Santiago de Querétaro is connected to Internet to extract useful patterns not only about driving but also about GPS position and time of public transport. This information can be used to find delays between buses stop and to infer events or make predictions about traffic in roads. This city has also a lot of Wi-Fi APs managed by the municipality which can be used to discover mobility patterns, most frequently visited areas with respect to time, cross results with Public transport information to find more information. Smart building can also contribute to this global information system with some data such as water and gas consumption at user level, CO2 generation per day. The use of smartphones with several sensors is continuously growing, and they can be also used to collect or access data from sensors through Wi-Fi connection. One of the main goals of building a smart city is to extract relevant information from a large amount of data which are mainly generated by the sensors at a different sampling rates. In order to extract relevant parameters and knowledge from this huge amount of information, it is convenient to generate a model and to work with standards. Each smart application is a macroscopic model that shows relevant parameters (water utilities gives electromechanical efficiency). At a microscope level we have granular information, such as personal data (user profile or sensor information, see figure 1). With all of this instrumentation, it was aimed to design a smart city model.

Fig. 5. Layers of the Smart City Model.

Figure 5 shows the proposed model for a smart city. Four layers are highlighted: perception, network, database, and application. Perception layer will be in charge of acquiring data from sensors or files, where mobile devices redefine the possibilities to connect people, processes and related objects. For example, in our case smart building data is compose of low-enthalpy geothermal system, weather influences, wall properties, etc. Mobile devices not only can access this information and change user behavior but also enable or disable a thermal barrier. Network and database layer are in charge of the transmission and processing of the information obtained in the first layer. They pass through this information to the application layer and vice versa. Finally, the application layer

will allow the users to interact between them and let them make queries to understand the city status. Visualization and data mining tasks are executed. That means to use complex queries and new technologies (i.e., business intelligence, open data, geo-location, web services, contextual applications and augmented reality, etc.).

C. Experimental Tests.

We have collected sensor information from a smart building for 2 years such as indoor and outdoor temperature and humidity, solar irradiation, presence, etc. Some data from SCADA is simulated but useful to grasp main goal, such as water electromechanical efficiency. Each sensor node simulates a smart application. Data can come from buildings, transport, energy and water utilities. A small ontology is designed by coupling key concepts from existing ontologies in other smart applications. A large number of weather stations can be used to collect data and then apply some machine learning algorithms to predict locations with a high probability of raining. Because of that, smart water application can close some valves to stop sending drinking water -it has been showed that raining events implies less water consumption-. Similarly, Smart transport can reorganize buses to avoid traffic jams because of flooding located at specific paths throughout the city. Figure 6 shows the main idea of this proposal, how concepts and relations are used to connect smart applications and give rise to a Smart City.

Fig. 6. Some concepts and relations used to link smart applications in a Smart City model.

Topbraid composer trial have been used to extract information and visualize a Santiago de Queretaro map with some KPIs. First we use a template model which enable to handle quite icons to put on a google map. Several resources were defined as airport, coffee shops, university, restaurant, and so on. Here follows a snipped code:

:airport

rdf:type icons:IconTypes ;

icons:hasIconType "airport"^^xsd:string ;

icons:iconURL "http://www.smartcity.com/.../

/airport.png"^^xsd:anyURI;

Next we import RDF files with some instances places, triples with name of buildings, web page, address, phone number, latitude and longitude coordinates, and so on. This will carry data acquire or compute by sensors such as CO2 emissions. After that we open a web browser and point to next web page:

http://localhost:8083/tbl/sparqlmotion?id=gcm:MapAddresses

This address refers to the map application that will display instances loaded previously. Finally figure 7, shows CIATEQ AC building information.

Fig. 7. Setup topbraid composer to show in a google map main kpi’s from

smart building. A sparql endpoint is also available.

Nevertheless, some issues still need to be solved. CO2 is an important KPI for a smart building but is necessary to know how much energy was spent to compute this value each day. Once CO2 is calculated, this data is transformed in a RDF value. Similarly electromechanical efficiency is a relevant KPI for Smart Water and its dynamic and static levels should be displayed by each drinking water well.

IV. CONCLUSIONS.

The growth of embedded devices where mobiles and smart applications are included provides promising opportunities for real-time and distributed intelligent data analysis. There is no reason to wait longer for a Smart City when all the components are ready to be wired. Benefits on the short and long term are clear. First, open data of the city performance derived from sensors and computer systems such as water availability, energy, transport utilities, etc, allow citizens to be more active and to interact with these instruments. Second, public authorities will have a better knowledge about the impact of the new infrastructures, and where it would be more necessary to invest. Three main goals have been accomplished:

1. Experience gained through smart building and SCADA was used to build concepts, relations and roles through a new ontology.

2. Tests have been show the feasibility and benefits of smart cities designed using ontologies and semantic technologies.

3. Small smart city ontology can be re-used for semantic web applications.

ACKNOWLEDGMENT

Current work was partially funded by Research Grants: MICINN-INNOVA-INNPACTO IPT_2011_1164_920000 and MICINN-INNOVA-INNPACTO_IPT_2011_1584_920000 by the Spanish Government. First author thanks to the Alternative Energies Research and Development Foundation (FIDEAS) for a pre-doctoral grant and also CONACYT-CIATEQ for additional grant and economic support.

REFERENCES

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