Smart Data and Semantics in a Sensor World

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 24895

Special Issue Editors


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Guest Editor
Department of Computer Science and Systems Engineering, School of Engineering and Architecture, University of Zaragoza, E-50018 Zaragoza, Spain
Interests: data management; mobile computing; recommendation systems; vehicular networks
“Enzo Ferrari” Engineering Department, University of Modena and Reggio Emilia, Modena, Italy
Interests: linked data; open data; semantic web; recommendation systems; ontologies; web of data; smart cities; eGovernment

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Guest Editor
University of Zaragoza, Spain
Interests: semantic web; information systems; data management

Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to provide a venue to show the practical progress made in the area of data management for sensors, particularly regarding the use of semantic techniques to obtain and exploit smart data from the raw sensor data. The terms “semantic web” and "sensor web" were firstly used more than 15 years ago. Despite this, they are mature topics on which the research community is still very active and with significant research challenges to address.

Since its first inception in 2001, the application of the Semantic Web has carried out an extensive use of ontologies, reasoning, and semantics in diverse field, such as Information Integration, Software Engineering, Bioinformatics, eGovernment, eHealth, and social networks.

This widespread use of ontologies has led to an incredible advance in the development of techniques to manipulate, share, reuse and integrate information across heterogeneous data sources. 

In recent years, to face the “Big Data” problem for sensor data, research areas like NLP (Natural Language Processing), ontology matching, and ontology alignment, are providing efficient methodologies based on the RDF and OWL languages to provide standard ways to convert such datasets into Linked Data sources. Due to its interest, in several fields, such as social networks, smart cities, or context-aware mobile applications, it is very relevant to publish the data available as Linked Data. Therefore, the development of techniques to enable users to publish, visualize and manipulate data in an easy way is in high demand. Besides, the standardization of this linked format has completely revolutionized the way of representing and analyzing data, which requires new graph-based machine learning and data mining techniques to explore such representation.

On the other hand, sensing technologies have become an important field for computer scientists. Sensors are sparsely distributed across the globe, leading to an overwhelming amount of data about our environment. Sensors can range from stationary environmental sensors to drones or autonomous vehicles collecting data, or even to humans acting as sensors using smartphones, and can be used to detect a multitude of observations, from simple phenomena to complex events. Moreover, the Sensor Web has realized the idea of a standardized, interoperable platform for everyone to easily share, find, and access sensor data.

However, the various characteristics of sensor data and their corresponding processing requirements, such as their multisource, heterogeneous, real-time, voluminous, streaming, and spatio-temporal features, has led many traditional data processing and integration approaches to show their limitations. Moreover, the lack of integration and communication between sensor networks often isolates important data streams and intensifies the existing problem of having too much data and not enough knowledge.

In this area, Semantic Web technologies have provided particular means to achieve these aims. Specifically, the Semantic Sensor Web (SSW) proposes that sensor data be annotated with semantic metadata, which will increase the interoperability and provide contextual information essential for situational knowledge. Social applications, ubiquitous and pervasive computing are examples of areas making use of semantic measured or processed data. Semantisation, context awareness, community management, and data visualization are core issues related to this area.

This Special Issue intends to provide insights on recent advances in these topics by soliciting original scientific contributions in the form of theoretical foundations, models, experimental research and case studies for developing semantic Web-based applications. We aim to bring together research related to several disciplines, such as Data Management, Knowledge Representation and Engineering, Web of Data, and Sensor Networks, among others. We invite original research contributions on all aspects of the Semantic Web and Sensor Web, as well as their applications. We encourage theoretical, methodological, empirical, and application papers. The submitted papers should describe original work, present significant results, and provide rigorous, principled, and repeatable evaluation. Besides, we appreciate the submission of papers incorporating links to data sets and other material used for evaluation, as well as to live demos and software source code.

Particularly, we encourage submissions focusing on the following themes for this Special Issue.

1. Semantics and Sensor Data
  • Real-time sensor data streams

  • Data management for sensor data

  • Obtention of smart data from sensors

  • Analytics of sensor data streams

  • Semantic modelling and annotation of sensor data

  • Scaling sematic sensor systems

  • Sensor data representation, acquisition, and cleaning

  • Semantic integration of heterogeneous data sources

  • Semantic data management technologies for sensor data

  • Challenges with managing and integrating real-time and historical sensor data

  • Provenance, access control and privacy-preserving issues in semantic data and sensor data

2. Linked Data

  • Linked Data applications and case studies

  • Visualizations and user interfaces for ontologies, sensor data and linked data

  • Machine learning and data mining for the Web of Data

3. Sensor-Based Applications

  • Semantic modelling of Smart City data

  • Mobile web, sensors and semantic streams

  • Smart cities, urban and geospatial data

  • Semantics and sensor data for smart cities

  • Semantics and eGovernment

  • Managing sensor data in transportation applications

  • Collaborative sensing and spatial crowdsourcing

Dr. Sergio Ilarri
Dr. Laura Po
Dr. Raquel Trillo
Guest Editors

Manuscript Submission Information

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Published Papers (6 papers)

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Editorial

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4 pages, 157 KiB  
Editorial
Special Issue on Smart Data and Semantics in a Sensor World
by Sergio Ilarri, Laura Po and Raquel Trillo-Lado
Appl. Sci. 2020, 10(18), 6355; https://doi.org/10.3390/app10186355 - 12 Sep 2020
Viewed by 1146
Abstract
Since its first inception in 2001, the application of the Semantic Web [...] Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)

Research

Jump to: Editorial

30 pages, 3070 KiB  
Article
Semantic Traffic Sensor Data: The TRAFAIR Experience
by Federico Desimoni, Sergio Ilarri, Laura Po, Federica Rollo and Raquel Trillo-Lado
Appl. Sci. 2020, 10(17), 5882; https://doi.org/10.3390/app10175882 - 25 Aug 2020
Cited by 9 | Viewed by 2816
Abstract
Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the [...] Read more.
Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective. Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)
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32 pages, 945 KiB  
Article
Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
by José R. R. Viqueira, Sebastián Villarroya, David Mera and José A. Taboada
Appl. Sci. 2020, 10(3), 856; https://doi.org/10.3390/app10030856 - 25 Jan 2020
Cited by 9 | Viewed by 3528
Abstract
The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, [...] Read more.
The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domains. Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)
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26 pages, 1322 KiB  
Article
MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
by Cheol Young Park and Kathryn Blackmond Laskey
Appl. Sci. 2019, 9(9), 1743; https://doi.org/10.3390/app9091743 - 26 Apr 2019
Cited by 5 | Viewed by 3899
Abstract
Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BNs) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning, and is the logical basis of Probabilistic Web Ontology Language (PR-OWL), a representation language for [...] Read more.
Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BNs) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning, and is the logical basis of Probabilistic Web Ontology Language (PR-OWL), a representation language for probabilistic ontologies. Developing an MEBN model to support a given application is a challenge, requiring definition of entities, relationships, random variables, conditional dependence relationships, and probability distributions. When available, data can be invaluable both to improve performance and to streamline development. By far the most common format for available data is the relational database (RDB). Relational databases describe and organize data according to the Relational Model (RM). Developing an MEBN model from data stored in an RDB therefore requires mapping between the two formalisms. This paper presents MEBN-RM, a set of mapping rules between key elements of MEBN and RM. We identify links between the two languages (RM and MEBN) and define four levels of mapping from elements of RM to elements of MEBN. These definitions are implemented in the MEBN-RM algorithm, which converts a relational schema in RM to a partial MEBN model. Through this research, the software has been released as an MEBN-RM open-source software tool. The method is illustrated through two example use cases using MEBN-RM to develop MEBN models: a Critical Infrastructure Defense System and a Smart Manufacturing System. Both systems are proof-of-concept systems used for situation awareness, where data coming from various sensors are stored in RDBs and converted into MEBN models through the MEBN-RM algorithm. In these use cases, we evaluate the performance of the MEBN-RM algorithm in terms of mapping speed and quality to show its efficiency in MEBN modeling. Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)
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23 pages, 465 KiB  
Article
Ontological Representation of Smart City Data: From Devices to Cities
by Paola Espinoza-Arias, María Poveda-Villalón, Raúl García-Castro and Oscar Corcho
Appl. Sci. 2019, 9(1), 32; https://doi.org/10.3390/app9010032 - 22 Dec 2018
Cited by 43 | Viewed by 7658
Abstract
Existing smart city ontologies allow representing different types of city-related data from cities. They have been developed according to different ontological commitments and hence do not share a minimum core model that would facilitate interoperability among smart city information systems. In this work, [...] Read more.
Existing smart city ontologies allow representing different types of city-related data from cities. They have been developed according to different ontological commitments and hence do not share a minimum core model that would facilitate interoperability among smart city information systems. In this work, a survey has been carried out in order to study available smart city ontologies and to identify the domains they are representing. Taking into account the findings of the survey and a set of ontological requirements for smart city data, a list of ontology design patterns is proposed. These patterns aim to be easily replicated and provide a minimum set of core concepts in order to guide the development of smart city ontologies. Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)
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18 pages, 3296 KiB  
Article
Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting
by Hao Lu, Yifan Zhu, Kaize Shi, Yisheng Lv, Pengfei Shi and Zhendong Niu
Appl. Sci. 2018, 8(7), 1193; https://doi.org/10.3390/app8071193 - 20 Jul 2018
Cited by 25 | Viewed by 5168
Abstract
Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different [...] Read more.
Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance. Full article
(This article belongs to the Special Issue Smart Data and Semantics in a Sensor World)
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