scholarly journals A Dynamic Dashboarding Application for Fleet Monitoring Using Semantic Web of Things Technologies

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1152 ◽  
Author(s):  
Sander Vanden Hautte ◽  
Pieter Moens ◽  
Joachim Van Herwegen ◽  
Dieter De Paepe ◽  
Bram Steenwinckel ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Exchange ◽  
State Of The Art ◽  
Sensor Data ◽  
Vast Amount ◽  
Data Formats ◽  
Sensor Configuration ◽  
Fixed Structure ◽  
The Web ◽  
At Will

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored—these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.

scholarly journals Ontology mapping: the state of the art

2003 ◽  
Vol 18 (1) ◽  
pp. 1-31 ◽  
Author(s):  
YANNIS KALFOGLOU ◽  
MARCO SCHORLEMMER
Keyword(s):  
Semantic Web ◽  
Theoretical Approach ◽  
State Of The Art ◽  
The State ◽  
Ontology Mapping ◽  
Distributed Environment ◽  
Diverse Communities ◽  
Exchange Information ◽  
Multiple Ontologies ◽  
The Web

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

Quality Measures for Semantic Web Application

2018 ◽  
pp. 1497-1507
Author(s):  
Adiraju Prasanth Rao
Keyword(s):  
Semantic Web ◽  
Web Application ◽  
Web Pages ◽  
Data Formats ◽  
Quality Principles ◽  
Common Framework ◽  
Structured Documents ◽  
Combine Information ◽  
The Web

The Semantic Web is a standard of Common Data Formats on WWW with aim to convert the current web data of unstructured and semi-structured documents into a common framework that allows data to be shared and reused across applications, enterprises. The main purpose of the Semantic Web is driving the evolution of the current Web by enabling users to find, share, and combine information more easily. Humans are capable of using the Web to carry out tasks such as searching for the lowest price for a LAPTOP. However, machines cannot accomplish all of these tasks without human direction, because web pages are designed to be read by people, not machines. The semantic web is a vision of information that can be readily interpreted by machines, so machines can perform more of the tedious work involved in finding, combining, and acting upon information on the web. The chapter presents the architecture of semantic web, its challenging issues and also data quality principles. These principles provide a better decision making within organization and will maximize long term data integration and interoperability.

XML Mining for Semantic Web

2011 ◽  
pp. 317-342
Author(s):  
Rafael Berlanga ◽  
Victoria Nebot
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Large Scale ◽  
Huge Amount ◽  
Semantic Annotations ◽  
Data Formats ◽  
Wide Acceptance ◽  
Domain Ontologies ◽  
The Web

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amount of both knowledge and semantic-annotated data is available in the web. DM methods have been very successful in discovering interesting patterns which are hidden in very large amounts of data. However, DM methods have been largely based on simple and flat data formats which are far from those available in the SW. This chapter reviews and discusses the main DM approaches proposed so far to mine SW data as well as those that have taken into account the SW resources and tools to define semantics-aware methods.

XML Mining for Semantic Web

Data Mining ◽  
2013 ◽  
pp. 625-649
Author(s):  
Rafael Berlanga ◽  
Victoria Nebot
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Large Scale ◽  
Huge Amount ◽  
Semantic Annotations ◽  
Data Formats ◽  
Wide Acceptance ◽  
Domain Ontologies ◽  
The Web

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amount of both knowledge and semantic-annotated data is available in the web. DM methods have been very successful in discovering interesting patterns which are hidden in very large amounts of data. However, DM methods have been largely based on simple and flat data formats which are far from those available in the SW. This chapter reviews and discusses the main DM approaches proposed so far to mine SW data as well as those that have taken into account the SW resources and tools to define semantics-aware methods.

Tuplespace-based computing for the Semantic Web: a survey of the state-of-the-art

2008 ◽  
Vol 23 (2) ◽  
pp. 181-212 ◽  
Author(s):  
LYNDON J. B. NIXON ◽  
ELENA SIMPERL ◽  
RETO KRUMMENACHER ◽  
FRANCISCO MARTIN-RECUERDA
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Message Passing ◽  
Web Applications ◽  
State Of The Art ◽  
Published Data ◽  
Communication Model ◽  
Representational State Transfer ◽  
Message Exchange ◽  
The Web

AbstractSemantic technologies promise to solve many challenging problems of the present Web applications. As they achieve a feasible level of maturity, they become increasingly accepted in various business settings at enterprise level. By contrast, their usability in open environments such as the Web—with respect to issues such as scalability, dynamism and openness—still requires additional investigation. In particular, Semantic Web services have inherited the Web service communication model, which is primarily based on synchronous message exchange technology such as remote procedure call (RPC), thus being incompatible with the REST (REpresentational State Transfer) architectural model of the Web. Recent advances in the field of middleware propose ‘semantic tuplespace computing’ as an instrument for coping with this situation. Arguing that truly Web-compliant Web service communication should be based, analogously to the conventional Web, on shared access to persistently published data instead of message passing, space-based middleware introduces a coordination infrastructure by means of which services can exchange information in a time- and reference-decoupled manner. In this article, we introduce the most important approaches in this newly emerging field. Our objective is to analyze and compare the solutions proposed so far, thus giving an account of the current state-of-the-art, and identifying new directions of research and development.

Metadata- and Ontology-Based Semantic Web Mining

2008 ◽  
pp. 3531-3556
Author(s):  
Marie Aude Aufaure ◽  
Bénédicte Le Grand ◽  
Michel Soto ◽  
Nacera Bennacer
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Semantic Web ◽  
Web Mining ◽  
State Of The Art ◽  
Next Generation ◽  
Research Areas ◽  
Semantic Data ◽  
Open Research ◽  
The Web

The increasing volume of data available on the Web makes information retrieval a tedious and difficult task. The vision of the Semantic Web introduces the next generation of the Web by establishing a layer of machine-understandable data, e.g., for software agents, sophisticated search engines and Web services. The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. This chapter is a state-of-the-art review of techniques which could make the Web more “semantic”. Beyond this state-of-the-art, we describe open research areas and we present major current research programs in this domain.

Metadata- and Ontology-Based Semantic Web Mining

2006 ◽  
pp. 259-296 ◽  
Author(s):  
Marie Aude Aufaure ◽  
Bénédicte Le Grand ◽  
Michel Soto ◽  
Nacera Bennacer
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Semantic Web ◽  
Web Mining ◽  
State Of The Art ◽  
Next Generation ◽  
Research Areas ◽  
Semantic Data ◽  
Open Research ◽  
The Web

The increasing volume of data available on the Web makes information retrieval a tedious and difficult task. The vision of the Semantic Web introduces the next generation of the Web by establishing a layer of machine-understandable data, e.g., for software agents, sophisticated search engines and Web services. The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. This chapter is a state-of-the-art review of techniques which could make the Web more “semantic”. Beyond this state-of-the-art, we describe open research areas and we present major current research programs in this domain.

Quality Measures for Semantic Web Application

2016 ◽  
pp. 130-139 ◽  
Author(s):  
Adiraju Prasanth Rao
Keyword(s):  
Semantic Web ◽  
Web Application ◽  
Web Pages ◽  
Data Formats ◽  
Quality Principles ◽  
Common Framework ◽  
Structured Documents ◽  
Combine Information ◽  
The Web

The Semantic Web is a standard of Common Data Formats on WWW with aim to convert the current web data of unstructured and semi-structured documents into a common framework that allows data to be shared and reused across applications, enterprises. The main purpose of the Semantic Web is driving the evolution of the current Web by enabling users to find, share, and combine information more easily. Humans are capable of using the Web to carry out tasks such as searching for the lowest price for a LAPTOP. However, machines cannot accomplish all of these tasks without human direction, because web pages are designed to be read by people, not machines. The semantic web is a vision of information that can be readily interpreted by machines, so machines can perform more of the tedious work involved in finding, combining, and acting upon information on the web. The chapter presents the architecture of semantic web, its challenging issues and also data quality principles. These principles provide a better decision making within organization and will maximize long term data integration and interoperability.

scholarly journals Making Linked Data accessible for One Health Surveillance with the "One Health Linked Data Toolbox"

2021 ◽  
Vol 4 ◽  
Author(s):  
Taras Günther ◽  
Matthias Filter ◽  
Fernanda Dórea
Keyword(s):  
Semantic Web ◽  
Web Application ◽  
Linked Data ◽  
Data Exchange ◽  
One Health ◽  
Health Surveillance ◽  
Knowledge Model ◽  
Data Interoperability ◽  
The One ◽  
The Web

In times of emerging diseases, data sharing and data integration are of particular relevance for One Health Surveillance (OHS) and decision support. Furthermore, there is an increasing demand to provide governmental data in compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Semantic web technologies are key facilitators for providing data interoperability, as they allow explicit annotation of data with their meaning, enabling reuse without loss of the data collection context. Among these, we highlight ontologies as a tool for modeling knowledge in a field, which simplify the interpretation and mapping of datasets in a computer readable medium; and the Resource Description Format (RDF), which allows data to be shared among human and computer agents following this knowledge model. Despite their potential for enabling cross-sectoral interoperability and data linkage, the use and application of these technologies is often hindered by their complexity and the lack of easy-to-use software applications. To overcome these challenges the OHEJP Project ORION developed the Health Surveillance Ontology (HSO). This knowledge model forms a foundation for semantic interoperability in the domain of One Health Surveillance. It provides a solution to add data from the target sectors (public health, animal health and food safety) in compliance with the FAIR principles of findability, accessibility, interoperability, and reusability, supporting interdisciplinary data exchange and usage. To provide use cases and facilitate the accessibility to HSO, we developed the One Health Linked Data Toolbox (OHLDT), which consists of three new and custom-developed web applications with specific functionalities. The first web application allows users to convert surveillance data available in Excel files online into HSO-RDF and vice versa. The web application demonstrates that data provided in well-established data formats can be automatically translated in the linked data format HSO-RDF. The second application is a demonstrator of the usage of HSO-RDF in a HSO triplestore database. In the user interface of this application, the user can select HSO concepts based on which to search and filter among surveillance datasets stored in a HSO triplestore database. The service then provides automatically generated dashboards based on the context of the data. The third web application demonstrates the use of data interoperability in the OHS context by using HSO-RDF to annotate meta-data, and in this way link datasets across sectors. The web application provides a dashboard to compare public data on zoonosis surveillance provided by EFSA and ECDC. The first solution enables linked data production, while the second and third provide examples of linked data consumption, and their value in enabling data interoperability across sectors. All described solutions are based on the open-source software KNIME and are deployed as web service via a KNIME Server hosted at the German Federal Institute for Risk Assessment. The semantic web extension of KNIME, which is based on the Apache Jena Framework, allowed a rapid an easy development within the project. The underlying open source KNIME workflows are freely available and can be easily customized by interested end users. With our applications, we demonstrate that the use of linked data has a great potential strengthening the use of FAIR data in OHS and interdisciplinary data exchange.

Quality Measures for Semantic Web Application

Web Services ◽  
2019 ◽  
pp. 1907-1916
Author(s):  
Adiraju Prasanth Rao
Keyword(s):  
Semantic Web ◽  
Web Application ◽  
Web Pages ◽  
Data Formats ◽  
Quality Principles ◽  
Common Framework ◽  
Structured Documents ◽  
Combine Information ◽  
The Web

The Semantic Web is a standard of Common Data Formats on WWW with aim to convert the current web data of unstructured and semi-structured documents into a common framework that allows data to be shared and reused across applications, enterprises. The main purpose of the Semantic Web is driving the evolution of the current Web by enabling users to find, share, and combine information more easily. Humans are capable of using the Web to carry out tasks such as searching for the lowest price for a LAPTOP. However, machines cannot accomplish all of these tasks without human direction, because web pages are designed to be read by people, not machines. The semantic web is a vision of information that can be readily interpreted by machines, so machines can perform more of the tedious work involved in finding, combining, and acting upon information on the web. The chapter presents the architecture of semantic web, its challenging issues and also data quality principles. These principles provide a better decision making within organization and will maximize long term data integration and interoperability.

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