SmartOntoSensor: Ontology for Semantic Interpretation of Smartphone Sensors Data for Context-Aware Applications

Ali, Shaukat; Khusro, Shah; Ullah, Irfan; Khan, Akif; Khan, Inayat

doi:10.1155/2017/8790198

Cited by 29 publications

(17 citation statements)

References 27 publications

(74 reference statements)

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“…26 Identifying and describing set of possible CQs helps in determining the coverage, consistency, completeness, verifiability, and understanding of requirements and could be used during ontology testing process in the form of SPARQL queries. 26,52 The CQs are to be answered by OntoSuSD by executing SPARQL queries such as "which agile principal is related to which technical sustainability goal? ", "which lean, agile, and green principals have the same economical sustainability goals?," and "which decision criteria and indicator must be used for determining environmental sustainability from lean, agile, and green values?"…”

Section: Ontosusd Requirements Specificationsmentioning

confidence: 99%

OntoSuSD: Software engineering approaches integration ontology for sustainable software development

et al. 2022

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Different software development approaches (SDAs) are developed with broad portfolios of development processes. Each of the approaches has certain exclusive principles, practices, thinking, and values, which are informally represented, implemented, and improperly institutionalized. Ontologies are developed for the representation, assessment, and adaptation of SDAs separately without having a shared terminology which may lead to terminological conflict and confusion affecting the simultaneous representation and implementation in software development industry and academia. The software engineering approaches does not consider and support sustainability as priority concern. However, the approaches have capabilities of supporting sustainable software development in different sustainability aspects. This research article aims for the designing and development of an integrated ontology of software engineering approaches (i.e., agile, lean, and green) named OntoSuSD (ontology for sustainable software development) to support sustainable software development knowledge, awareness, and implementation. The goal of OntoSuSD is to propose, design and develop a formal, generic, consistent, and shared knowledge base containing semantic terminology and description of concepts and relationships generated around the representation and implementation of lean, agile, and green approaches in software development processes, which will facilitate their simultaneous implementation and assessment for sustainable software development. The OntoSuSD is developed using practical ontology engineering methodology by reusing relevant ontologies and explicit concepts and properties are defined to fulfill knowledge requirements and representations of the domain.The OntoSuSD is evaluated, and results infer OntoSuSD has high ontological design, good domain coverage, potential applications and achieves purpose of the ontology development.

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Section: Ontosusd Requirements Specificationsmentioning

confidence: 99%

OntoSuSD: Software engineering approaches integration ontology for sustainable software development

et al. 2022

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“…e availability of a vast amount of necessary data makes it essential to find the best methodology to organize the data landscape into a widely agreed and explicit format to enable representation and understanding by the users and machines for reliable and scalable statistical analysis and visualization. e ontology provides a solution to develop a domain data model by formally and explicitly defining its concepts and meaningful data linkages [29]. e COVID-19 datasets not only vary in data formats heterogeneity but also the number of data items provided and their headings (terminologies).…”

Section: Architecture and Developmentmentioning

confidence: 99%

“…e Likert-Scale data is ordinal, where the order of the values is significant but the exact difference between the values is not known. e Chi-Square is an important descriptive statistical method for the analysis of the categorical data [29,31]. To analyze the ordered scale 5 levels Likertscale responses data using Chi-Square descriptive statistics: (1) the negative questions are transformed into positive questions and the participants' responses are adjusted accordingly for uniformity (2) the five response categories (i.e., strongly disagree, disagree, neutral, agree, and strongly agree); are breakdown into two nominal categories (i.e., disagree and agree) by combining the lower level three categories and upper level two categories respectively.…”

Section: Descriptive Statistics Evaluationmentioning

confidence: 99%

Publishing and Interlinking COVID-19 Data Using Linked Open Data Principles: Toward Effective Healthcare Planning and Decision-Making

Ali

Zada

Mehmood

et al. 2022

Mathematical Problems in Engineering

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The COVID-19 data is critical to support countries and healthcare organizations for effective planning and evidence-based practices to counter the pressures of cost reduction, improved coordination, and outcome and produce more with less. Several COVID-19 datasets are published on the web to support stakeholders in gaining valuable insights for better planning and decision-making in healthcare. However, the datasets are produced in heterogeneous proprietary formats, which create data silos and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is usually performed by the domain experts manually, which is time-consuming and error-prone. Therefore, an explicit, flexible, and widely acceptable methodology to represent, store, query, and visualize COVID-19 data is needed. In this paper, we have presented the design and development of the Linked Open COVID-19 Data system for structuring and transforming COVID-19 data into semantic format using explicitly developed ontology and publishing on the web using Linked Open Data (LOD) principles. The key motivation of this research is the evaluation of LOD technology as a potential option and application of the available Semantic Web tools (i.e., Protégé, Excel2RDF, Fuseki, Silk, and Sgvizler) for building LOD-based COVID-19 information systems. We have also underpinned several use-case scenarios exploiting the LOD format of the COVID-19 data, which could be used by applications and services for providing relevant information to the end-users. The effectiveness of the proposed methodology and system is evaluated using the system usability scale and descriptive statistical methods and results are found promising.

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“…SmartOntoSensor [5] an ontological model aims to create a formal conception of smartphone resources and sensors, including their categories, taxonomy, connections, and information about sensor attributes, performance, and dependability. It includes logical statements that describe associations among components and sensor concepts.…”

Section: Ontology and Internet Of Thingsmentioning

confidence: 99%

Semantic Web and Internet of Things: Challenges, Applications and Perspectives

Amara

Hemam

Djezzar

et al. 2022

JICTS

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The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.

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SmartOntoSensor: Ontology for Semantic Interpretation of Smartphone Sensors Data for Context-Aware Applications

Cited by 29 publications

References 27 publications

OntoSuSD: Software engineering approaches integration ontology for sustainable software development

OntoSuSD: Software engineering approaches integration ontology for sustainable software development

Publishing and Interlinking COVID-19 Data Using Linked Open Data Principles: Toward Effective Healthcare Planning and Decision-Making

Semantic Web and Internet of Things: Challenges, Applications and Perspectives

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