Semantic Sensor Network Ontology.Document. Abstract.The Semantic Sensor Network SSN ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators.SSN. follows a horizontal and vertical modularization architecture by including a lightweight but self contained core ontology called SOSA Sensor, Observation, Sample, and Actuator for its elementary classes and properties.With their different.SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large scale scientific monitoring, industrial and household infrastructures, social sensing.Web of Things. Both ontologies are described below, and examples of their usage are given.Abstract. The Web Ontology Language OWL is a semantic markup language for publishing and sharing ontologies on the World Wide Web.OWL is developed as a vocabulary.A library of 1200 current and historical NIST cybersecurity publications, including FIPS, SP 800s, NISTIRs, journal articles, conference papers, etc.ACM Learning Webinars.ACM keeps you at the cutting edge of the latest technical and technological developments with our ACM Learning Webinar series.The namespace for SSN terms is http www.The namespace for SOSA terms is.The suggested prefix for the SSN namespace is ssn.The suggested prefix for the SOSA namespace is sosa.The SSN ontology is available at http www.The SOSA ontology is available at http www.Status of This Document.SoftwareQualityCharacteristicAttributeRelationship.png/450px-SoftwareQualityCharacteristicAttributeRelationship.png' alt='An Ontology For Software Measurement Framework' title='An Ontology For Software Measurement Framework' />This section describes the status of this document at the time of its publication.Other documents may supersede this document.A list of current W3.C publications and the latest revision of this technical report can be found in the W3.C technical reports index at https www.TR. For OGC this document was prepared by the Spatial Data on the Web Working Group SDWWG a joint W3.C OGC. project see charter.The document is prepared following W3.C conventions. At the time of publication, this document was in the approval process for the OGC Full standards track and thus its status in the OGC will be indicated in Discussion Papers or OGC Standards on the OGC website.Recipients of this document are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to provide supporting documentation.This document was published by the Spatial Data on the Web Working Group as a Recommendation.Comments regarding this document are welcome.Please send them to the Git.Hub repository. An archive of the mailing list is also available.Please see the Working Groups implementation.This document has been reviewed by W3.C Members, by software developers, and by other W3.C groups and interested parties, and is endorsed by the Director.W3. C Recommendation.It is a stable document and may be used as reference material or cited from another document.W3. Cs role in making the Recommendation.This enhances the functionality and interoperability of the Web.This document was produced by a group operating under the.W3. C Patent. Policy.W3. C maintains a public list of any patent.An individual who has actual knowledge of a patent which the individual believes contains.Essential. Claims must disclose the information in accordance with.W3. C Patent Policy.This document is governed by the 1 March 2.W3. C Process Document.Introduction. This section is non normative.Sensors are a major source of data available on the Web today.While sensor data may be published as mere values, searching, reusing, integrating, and interpreting these data requires more than just the observation results.Of equal importance.OGCs Sensor Web Enablement standards.Oand. M, Sensor. ML provide a means to annotate sensors and their observations.However, these standards are not integrated and aligned.W3. C Semantic Web technologies and Linked Data in particular, which are key drivers for creating and maintaining a global and densely interconnected graph of data.With the rise of the Web.Things and smart cities and homes more generally, actuators and the data they produce also become first class citizens of the Web.Given their close relation to sensors, observations, procedures, and features of interest, it is desirable.Finally, with the increasing diversity of data and data providers, definitions such as those for sensors need to be broadened, e.The following. specifications introduce the new Semantic Sensor Network SSN and Sensor, Observation, Sample, and Actuator SOSA ontologies that are set out to provide flexible but coherent perspectives for representing the entities, relations, and activities.SOSA provides a lightweight core for SSN and aims at broadening the target audience and application areas that can make use of Semantic Web ontologies.At the same time, SOSA acts as minimal interoperability.SSN, its modules, and SOSA.Modularization. This section is non normative.Practitioners using the original Semantic Sensor Network Ontology as defined in the W3.C Semantic Sensor Network Incubator Group SSNO.Dolce Ultra. Lite DUL upper level ontology.In response to this, the new Semantic Sensor Network SSN ontology offers several ontology subsets that.This section explains the rationale and method for modularizing SSN, i.For example, SOSA is intended to provide Schema.SSN based repositories.Ontology modularization is a common method used in ontology engineering to segment an ontology into smaller parts.In general, ontology modularization aims at providing users of ontologies with the knowledge they require, reducing the scope as.Two main categories of ontology modularization can be distinguished.The first category comprises those approaches that focus on the composition of existing ontologies by means of integrating and mapping ontologies, most commonly through owl import statements.OWL import has a direction from a dependent.Although import is transitive, knowledge is propagated in only one direction.The importing ontology assumes all the meaning of the imported terms used, by including all axioms relevant to the meaning of.However, the imported ontology does not capture any of the semantics of the importing ontology.The second category comprises of mapping approaches that aim to partition and extract parts of ontologies as modules. Smartdraw 2014 Full Version Free Download Crack For Windows here. These mapping approaches are not necessarily directional, but most approaches of ontology extraction rely on the directionality.The main feature of an ontology module under the second category is that it is self contained, i.This means, that the result of certain reasoning tasks such as subsumption or query answering within a single module should be possible and result in the same answers without the need to access other modules of the ontology.Our modularization uses the first approach by composing the ontology into several modules that use owl import statements, whereby we distinguish two methods depending on the directionality of the segmentation a vertical segmentation.Figure 1. The SOSA and SSN ontologies and their vertical and horizontal modules.Vertical Segmentation.Vertical modules build upon each other, i.Lower level modules are independent of their higher level modules and logically consistent on their own.For example, the Dolce Ultra.Lite Alignment Module imports the SSN.Ontology which itself imports the SOSA Ontology.However, in reverse, neither SOSA nor SSN import the Dolce Ultra.Lite. Alignment Module.In fact, SOSA as the core, does not import any other ontologies, which makes it truly independent of vertical modules that add more expressivity and further ontological commitments to the lightweight semantics of.SOSA. Note that higher level here is not to be confused with upper level ontologies.Upper level ontologies are general knowledge ontologies that can be directionally imported in many domains, whereas our definition of higher level ontologies here refers.Horizontal Segmentation.Modules that are horizontally layered may depend on each other, i.Only one horizontal module that is dependent on the SSN ontology is presented in this specification, the.Sample Relations Module.Other ontologies that add domain specific terms to SSN, but require the import of SSN, can be considered horizontal modules.Origins of SSN and SOSAThis section is non normative.Here we briefly review the origins of SSN and SOSA, namely the initial SSN version published by the W3.C Semantic Sensor Network Incubator.Group SSNO and work on Sensor Web Enablement by the OGC.We also highlight the most substantial changes made since the initial release of the SSN ontology.Starting in 2. 00.OGCs Sensor Web Enablement initiative has developed a generic framework for delivering sensor data, dealing with remote sensing, moving platforms, and in situ monitoring and sensing.The Sensor Observation Service defines.Business process modelling Review and framework.A business process is the combination of a set of activities within an enterprise with a structure describing their logical order and dependence whose objective is to produce a desired result.Business process modelling enables a common understanding and analysis of a business process.A process model can provide a comprehensive understanding of a process.An enterprise can be analysed and integrated through its business processes.Hence the importance of correctly modelling its business processes.Using the right model involves taking into account the purpose of the analysis and, knowledge of the available process modelling techniques and tools.The number of references on business modelling is huge, thus making it very time consuming to get an overview and understand many of the concepts and vocabulary involved.The primary concern of this paper is to make that job easier, i.Also a framework for classifying business process modelling techniques according to their purpose is proposed and discussed.
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