Water utility decision support through the semantic web of things

Howell, Shaun; Rezgui, Yacine; Beach, Thomas

doi:10.1016/j.envsoft.2018.01.006

Cited by 22 publications

(21 citation statements)

References 21 publications

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“…Currently, the systematic development of knowledge employs human learning as an inspiration for dealing with machine and system analytics, incorporating memory models, and learning protocols, as in AI (Anum et al, 2018). As a result, a new term called the Semantic Web of Things (SWoT) has contributed to the creation of machines that are capable of auto‐interpreting and auto‐describing (Howell, Rezgui, & Beach, 2018). However, becuase the SWoT requires robust semantic interoperability to create a shared understanding of the context, meaning, and sourcing of the data (Howell et al, 2018), it is still challenging to execute this model in the real world (Shahpasand & Rahimzadeh, 2018).…”

Section: Resultsmentioning

confidence: 99%

The relationship between the Internet of Things and knowledge management in smart ecosystem development

Ikeda

Silva

Penha

et al. 2021

Knowl Process Manag

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Creating an environment capable of responding to its users and organizations in real‐time is a challenging task. However, with the advent of the Internet of Things (IoT) ecosystem, a new perspective about creating an intelligent and interconnected world has become a reality. The goal of the present study aims to explore the relationship between IoT and Knowledge Management (KM) when generating a Smart environment. Herein, we conducted a Systematic Literature Review and present a discussion about the evolution of the IoT concept and the IoT ecosystem. More specifically we focused on the main IoT elements that serve as the foundation of the IoT ecosystem and the relationship between the IoT and KM. Although there is a lack of studies about KM supporting the IoT, our study provides evidence that the IoT is composed of a set of technologies that enable KM and KM processes can facilitate the development of IoT Smart environments. Thus, there is an opportunity to study how to generate more intelligence and connectivity, which will support the players of the ecosystem, promote interactions among objects and people and ultimately add value to organizations and users.

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Section: Resultsmentioning

confidence: 99%

The relationship between the Internet of Things and knowledge management in smart ecosystem development

Ikeda

Silva

Penha

et al. 2021

Knowl Process Manag

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“…The proposed rules in this paper describe the pollution type, the position, the sampling time, the observation product, and the pollution alert. However, Howell et al [34] described the rules of water alert that affected the entity of the water utility decision support by leveraging the semantic web to address the heterogeneity of web resources.…”

Section: Comparison With Pollution Alert Rulesmentioning

confidence: 99%

An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring

Wang

Wei

Chen

et al. 2020

Water

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The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.

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“…One step beyond the definition of simple vocabularies and terminologies is the incorporation of complex constraints and knowledge rules that may be expressed in the Semantic Web Rule Language (SWRL) [49], a rule language that combines the representation power of OWL with the reasoning power of RuleML. Such knowledge may next be used to perform inferences to achieve for example complex data validation [50] and advanced decision support tools [51].…”

Section: Geospatial Smart Representationsmentioning

confidence: 99%

“…Regarding geospatial data, according to [86], new semantic data integration solutions should pay attention to the special characteristics of spatial data both during data representation and integration. Various semantic data integration solutions have already been proposed in various environmental application domains, including crop modeling in agriculture [87], solar terrestrial observation [88], water management [51] and geology [89]. Approaches defined from a more general purpose perspective have also been proposed in the scope of OGC data models and services, focusing on geospatial data [90] and more specifically on environmental observation data [45,91].…”

Section: Smart Environmental Data Integrationmentioning

confidence: 99%

“…Next, data integration knowledge that maps local concepts of each data source to global concepts, has to be generated. Currently available solutions assume in general that such knowledge is already available [45,51,[87][88][89][90][91]. However, defining such knowledge is a tedious process that requires the implication of application domain experts.…”

Section: Challenge 5: Geospatial Data Integration Knowledge Discoverymentioning

confidence: 99%

See 1 more Smart Citation

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

et al. 2020

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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.

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Water utility decision support through the semantic web of things

Cited by 22 publications

References 21 publications

The relationship between the Internet of Things and knowledge management in smart ecosystem development

The relationship between the Internet of Things and knowledge management in smart ecosystem development

An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

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