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 26519
Special Issue Editors
Interests: data management; mobile computing; recommendation systems; vehicular networks
Special Issues, Collections and Topics in MDPI journals
Interests: big data; digital twin; NLP; traffic modelling; air quality; event detection; sensors data streams analysis; time series; graph analytics; large language model; anomaly detection
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
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Data management for sensor data
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Obtention of smart data from sensors
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Analytics of sensor data streams
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Semantic modelling and annotation of sensor data
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Scaling sematic sensor systems
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Sensor data representation, acquisition, and cleaning
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Semantic integration of heterogeneous data sources
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Semantic data management technologies for sensor data
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Challenges with managing and integrating real-time and historical sensor data
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Provenance, access control and privacy-preserving issues in semantic data and sensor data
2. Linked Data
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Linked Data applications and case studies
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Visualizations and user interfaces for ontologies, sensor data and linked data
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Machine learning and data mining for the Web of Data
3. Sensor-Based Applications
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Semantic modelling of Smart City data
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Mobile web, sensors and semantic streams
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Smart cities, urban and geospatial data
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Semantics and sensor data for smart cities
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Semantics and eGovernment
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Managing sensor data in transportation applications
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Collaborative sensing and spatial crowdsourcing
Dr. Sergio Ilarri
Dr. Laura Po
Dr. Raquel Trillo
Guest Editors
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