The embeddedness of ecosystems interpreted as the connectivity between data sources has been the ... more The embeddedness of ecosystems interpreted as the connectivity between data sources has been the research focus of ecosystem service providers. Heterogeneity of data sources, linked with embedded systems, is challenging in the ecosystem integration process. Big data is an added motivation in the ecosystem integration process. The purpose of the research is to provide an improved understanding of ecosystem inherent connectivity by integrating multiple ecosystems through their big data in a multidimensional repository system, with a focus on data analytics. We need an architecture to drive the composite congruence existing between disease-human-environment-business systems. We propose an Embedded Digital Ecosystem Architecture (EDEA), from which the associations hidden among big data sources of multiple ecosystems are analysed in new knowledge domains. We construe in our research that pandemic-related disease ecologies have connectivity with the human, environment and economic ecosystems, ascertaining the potential benefits of data science in embedded digital ecosystems' research.
From a digital ecosystem perspective, sustainability is a manifestation of a composite entity wit... more From a digital ecosystem perspective, sustainability is a manifestation of a composite entity with multiple data attribute dimensions. The data relationships may emerge between geographically distributed supply chain management ecosystems and their linked human, economic and environment ecologies. The ecosystems may exhibit inherent connections and interactions. For making connections more resilient, we characterize models that serve multiple industries through numerous data associations, even in Big Data scales. In the context of Integrated Project Management (IPM), the knowledge of boundaries between systems is mysterious, analysing diverse ecosystems through a sustainable framework can uncover new insights of inherent connections. The purpose of this research is to develop a holistic information system approach, in which multidimensional data and their connectivity are analysed, recognizing the ontological cogency, uniqueness of ecosystems and their data sources. The research outcome has facilitated the tactical development of strategies for ameliorating the sustainability challenges in the IPM contexts.
The research is aimed at investigating gender equality that affects the social and economic devel... more The research is aimed at investigating gender equality that affects the social and economic development in African countries. The existing data sources are examined based on the research question: “What is the role of female education and workforce on the social and economic development in African contexts, especially in East Africa?” To explore this question, we investigate the socio-economic development indicators such as employment, education, and population growth. We examine the effects of female population on impending education and employment indicators. For analysing the benchmarks, the empirical and observational research methods are deployed. Various data schemas are designed to test the gender based ecosystems and their models. Our results show the data relationship has a very strong positive connection between female education and social economic development dimensions. This is validated by qualitative research, obtained from questionnaires from one of the municipalities...
Business process models are abstract descriptions and as such should be applicable in different s... more Business process models are abstract descriptions and as such should be applicable in different situations. In order for a single process model to be reused, we need support for configuration and customisation. Often, process objects and activities are domain-specific. We use this observation and allow domain models to drive the customisation. Process variability models, known from product line modelling and manufacturing, can control this customisation by taking into account the domain models. While activities and objects have already been studied, we investigate here the constraints that govern a process execution. In order to integrate these constraints into a process model, we use a rule-based constraints language for a workflow and process model. A modelling framework will be presented as a development approach for customised rules through a feature model. Our use case is content processing, represented by an abstract ontology-based domain model in the framework and implemented by a customisation engine. The key contribution is a conceptual definition of a domain-specific rule variability language.
Proceedings of the International Conference on Computer-Human Interaction Research and Applications, 2017
The domain-specific model-driven development requires effective and flexible techniques for imple... more The domain-specific model-driven development requires effective and flexible techniques for implementing domain-specific rule generators. In this paper, we present a framework for rule generation through model translation with feature model, a high-level of the domain model to translate into low-level of rule language based on the paradigm of software reuse in terms of customisation and configuration with domain-specific rule strategies benefit mode-to-text translations. This framework is domain-specific where non-technical domain user can customise and configure the business process models. These compositions support two dimensional of translation modularity by using software product line engineering. The domain engineering is achieved by designing the domain and process model as a requirement space, it is also called template model, connecting with feature model through weaving model. The feature model is a high-level input model to customise the template model to an implementation. The application engineering is achieved by supporting the rule definition and configuring the generated rules. We discuss the development approach of the framework in a domain-specific environment; we present a case study in a Digital Content Technology (DCT) domain.
Applied Drug Research, Clinical Trials and Regulatory Affairs, 2021
In this note we consider a system of financial institutions and study systemic risk measures in t... more In this note we consider a system of financial institutions and study systemic risk measures in the presence of a financial market and in a robust setting, namely, where no reference probability is assigned. We obtain a dual representation for convex robust systemic risk measures adjusted to the financial market and show its relation to some appropriate noarbitrage conditions.
We are primarily concerned with the utilization of a conceptual domain model for rule generation,... more We are primarily concerned with the utilization of a conceptual domain model for rule generation, specifically to define a domain-specific rule language (DSRL) [1, 2] syntax, its grammar for business process model and domain constraint management. We present a conceptual approach for outlining a DSRL for process constraints [3]. The domain-specific content model (DSCM) definition needs to consider two challenges. The first relates to the knowledge transfer from domain concept to conceptual model, where model inaccuracies and defects may have been translated because of misunderstandings, model errors, human errors or inherent semantic mismatches (e.g. between classes). The other problem relates to inconsistency, redundancy and incorrectness resulting from multiple views and abstractions. A domain-specific approach provides a dedicated solution for a defined set of
The challenges faced by domain experts, commitments made to domain-specific rule (DSR) languages ... more The challenges faced by domain experts, commitments made to domain-specific rule (DSR) languages and process design are described. We investigate the business application developments and existing challenges of evaluation strategies of DSR articulations. Often, multiple domain scenarios pose end-user predicaments complicating the computational ability of DSR. In addition, implementation of DSR and its configuration are belated due to poorly evaluated usability criteria. A new framework is needed, facilitating the DSR language and enhancing the computational intelligence. We intend to evaluate the performance of DSR generation and framework integration with variety of usability conditions including efficiency and effectiveness of configuration through system usability score (SUS). Empirical research involving experimental data, questionnaire surveys, and interview outcomes provide conclusive evaluation attributes and their fact instances from SUS. Both manual and semi-automatic configurations are tested. Semiautomatic configuration appears to be more efficient and satisfactory with regard to artefact performance, quality, learnability, user-friendly and reliability.
The unconventional digital petroleum ecosystems are associated with fractured reservoirs that are... more The unconventional digital petroleum ecosystems are associated with fractured reservoirs that are usually unpredictable, but can produce for longer periods depending on size of petroleum systems and basins. Currently, conventional reservoirs do produce oil & gas even without integrated workflows and solutions. The heterogeneity and multidimensionality of data sources at times can make the data documentation and integration complicated affecting the exploration and field development. We examine the conventional database technologies and their failures in organizing the data of unconventional digital ecosystems. Big Data driven intelligent information system solutions are needed for addressing the issues of complex data systems of unconventional digital ecosystems. Geographically distributed petroleum systems and their associated reservoirs too demand such integrated and innovative digital ecosystem solutions. We propose an innovative design science information system (DSIS), an integrated digital framework solution to explorers, dealing with unconventional fractured reservoirs. The integrated Big Data analytics solutions are effective in interpreting unconventional digital petroleum ecosystems that are impacted by shale prospect businesses worldwide.
Cancer is a deadly complex disease. Poorly aligned institutions, their data sources, and doctor-p... more Cancer is a deadly complex disease. Poorly aligned institutions, their data sources, and doctor-patient disengagement motivate us, strengthening collaborative cancer research. The research explores informatics solutions, by collating various cancer-linked data attributes and interconnecting them with spatial-temporal dimensions. The study is aimed at developing a methodological framework and investigating the open source empirical cancer data that involve diverse human ecosystems. We propose a Multidimensional Cancer Research Framework (MUCARF), responding to challenges of reporting, documenting and collaborating the data sources that characterize different cancer ailments including their worldwide causalities. Information system artefacts built based on cancer-domain ontologies, with similar and dissimilar attributes are integrated into MUCARF to dig the diagnostic cancer metadata views in new knowledge domains. Metadata model that replicates the design and development of MUCARF is...
Background: In shale basins, petroleum systems are complex; they hold data sources in Big Data sc... more Background: In shale basins, petroleum systems are complex; they hold data sources in Big Data scales. The motivation of research lies with the facts of exploring effective inherent connectivity between unconventional petroleum systems. The connectivity between energy reservoir systems is ambiguous within a distinctive petroleum ecosystem. Heterogeneity and multidimensionality of unstructured data sources are additional challenges, precluding systematic modelling of diverse petroleum systems and their data integration process, including growing demand for storage systems. The research aims to establish the knowledge-based connectivity between petroleum systems through Information System (IS) articulations, visual analytics and data management. Method: We investigate the knowledge-based IS guided exploration and production systems to explore the connectivity between diverse unconventional petroleum systems and forecast the reservoir energy. We articulate Design Science Information Sy...
In today’s changing world, there is an ever-increasing demand and need for software reuse in appl... more In today’s changing world, there is an ever-increasing demand and need for software reuse in applications, where the process model needs to be reused in different applications in a domain-specific environment. The process model is required to adapt and implement changes promptly at run-time, in response of the end-user configuration requirements. Furthermore, reusability is emerging strongly as a necessary underlying capability, particularly for customization of business in a dynamic environment where end-users can select their requirements to achieve a specific goal. Such adaptations are in general, performed by non-technical end-users which can lead to losing a significant number of person-days and which can also open up possibilities to introduce errors into the system. These scenarios call for - indeed cry out for - a system with a configurable and customizable business process, operable by users with limited technical expertise. Research aims to provide a framework for generati...
The research is aimed at investigating knowledge-based social informatics solutions. Socio-econom... more The research is aimed at investigating knowledge-based social informatics solutions. Socio-economic development relies on technology use in education and employment sectors. To explore such challenges, we examine the existing indicators of socio-economic development, such as gender equalities, employment, and education and population growth attribute dimensions. To understand them precisely, we analyse large-size human ecosystems and their data analytics. Social-informatics and -intelligence analysis are proposed with the design of logical and physical data schemas in diverse socio-economic contexts and their interoperability in varied geographies. We compute predictive models for different attribute dimensions, usable by technology developers and policy-makers. We interpret the data views of digital human ecosystems in the form of various graphs, tables, and polynomial regressions to envisage the influence of technology on societal collisions. The polynomial regressions suggest a strong positive relationship between different socio-economic attributes, cognizing the social intelligence and its knowledge management in Asia-Pacific contexts
Business process models are abstract descriptions that are applicable in different situations. To... more Business process models are abstract descriptions that are applicable in different situations. To allow a single process model to be reused, configuration and customisation features can help. Variability models, known from product line modelling and manufacturing, can control this customisation. While activities and objects have already been subject of similar investigations, we focus on the constraints that govern a process execution. We report here on the development a rule-based constraints language for a workflow and process model. The aim is a conceptual definition of a domain-specific rule variability language, integrated with the principles of a common business workflow or process notation. This modelling framework will be presented as a development approach for customised rules through a feature model. Our use case is content processing, represented by an abstract ontology-based domain model in the framework.
We identify various sports-related challenges pertinent to teams, player selections, and event ma... more We identify various sports-related challenges pertinent to teams, player selections, and event management. We examine both technical and managerial decisions made during player promotion, and various factors influenced the matches. We conceptualize that the game strategies, player and team selections, performance-based sports ecology management entities can blend into an informatics paradigm for which sports data modelling and analytics are required. In addition, reviews of players in social media have significance in weighing the economic value of players. With a motivation to systematize and franchise the branded teams and players to generate revenues, we aim at articulating a logical Sports Information System (SIS), for which various design-science guided artefacts are proposed. The research evaluates game strategies, the promotion of promising players, team-based performance, and economic indicators. Documentation of sports facts and logical storage models are vital to generate metadata and assess how players performed and what factors can motivate future matches
This research study the the influence of gender and family income on online educational experienc... more This research study the the influence of gender and family income on online educational experience dimensions in the Indian context.. The instrument used consisted of twenty items and demographics variables. Changes in demographic variable affect the online education experience of the user's dimension. Based on results the significant changes are identified and interpreted. Changes concerning demographic variables were observed in the factors of online user experience. The changes were most significant for the dimension: pragmatic-pleasurable experience and hedonic & exhaustive experience.
Fog Computing is a new computing paradigm which is grown ever since it is being used. It is aimed... more Fog Computing is a new computing paradigm which is grown ever since it is being used. It is aimed at bringing the computations close to data sources from healthcare centers. IoT driven Fog Computing is developed in the healthcare industry that can expedite facilities and services among the mass population and help in saving billions of lives. The new computing platform, founded as fog computing paradigm may help to ease latency while transmitting and communicating signals with remote servers, which can accelerate medical services in spatial-temporal dimensions. The latency reduction is one of the necessary features of computing platforms which can enable completing the healthcare operations, especially in large-size medical projects and in relation to providing sensitive and intensive services. Reducing the cost of delivering data to the cloud is one of the research objectives.
The embeddedness of ecosystems interpreted as the connectivity between data sources has been the ... more The embeddedness of ecosystems interpreted as the connectivity between data sources has been the research focus of ecosystem service providers. Heterogeneity of data sources, linked with embedded systems, is challenging in the ecosystem integration process. Big data is an added motivation in the ecosystem integration process. The purpose of the research is to provide an improved understanding of ecosystem inherent connectivity by integrating multiple ecosystems through their big data in a multidimensional repository system, with a focus on data analytics. We need an architecture to drive the composite congruence existing between disease-human-environment-business systems. We propose an Embedded Digital Ecosystem Architecture (EDEA), from which the associations hidden among big data sources of multiple ecosystems are analysed in new knowledge domains. We construe in our research that pandemic-related disease ecologies have connectivity with the human, environment and economic ecosystems, ascertaining the potential benefits of data science in embedded digital ecosystems' research.
From a digital ecosystem perspective, sustainability is a manifestation of a composite entity wit... more From a digital ecosystem perspective, sustainability is a manifestation of a composite entity with multiple data attribute dimensions. The data relationships may emerge between geographically distributed supply chain management ecosystems and their linked human, economic and environment ecologies. The ecosystems may exhibit inherent connections and interactions. For making connections more resilient, we characterize models that serve multiple industries through numerous data associations, even in Big Data scales. In the context of Integrated Project Management (IPM), the knowledge of boundaries between systems is mysterious, analysing diverse ecosystems through a sustainable framework can uncover new insights of inherent connections. The purpose of this research is to develop a holistic information system approach, in which multidimensional data and their connectivity are analysed, recognizing the ontological cogency, uniqueness of ecosystems and their data sources. The research outcome has facilitated the tactical development of strategies for ameliorating the sustainability challenges in the IPM contexts.
The research is aimed at investigating gender equality that affects the social and economic devel... more The research is aimed at investigating gender equality that affects the social and economic development in African countries. The existing data sources are examined based on the research question: “What is the role of female education and workforce on the social and economic development in African contexts, especially in East Africa?” To explore this question, we investigate the socio-economic development indicators such as employment, education, and population growth. We examine the effects of female population on impending education and employment indicators. For analysing the benchmarks, the empirical and observational research methods are deployed. Various data schemas are designed to test the gender based ecosystems and their models. Our results show the data relationship has a very strong positive connection between female education and social economic development dimensions. This is validated by qualitative research, obtained from questionnaires from one of the municipalities...
Business process models are abstract descriptions and as such should be applicable in different s... more Business process models are abstract descriptions and as such should be applicable in different situations. In order for a single process model to be reused, we need support for configuration and customisation. Often, process objects and activities are domain-specific. We use this observation and allow domain models to drive the customisation. Process variability models, known from product line modelling and manufacturing, can control this customisation by taking into account the domain models. While activities and objects have already been studied, we investigate here the constraints that govern a process execution. In order to integrate these constraints into a process model, we use a rule-based constraints language for a workflow and process model. A modelling framework will be presented as a development approach for customised rules through a feature model. Our use case is content processing, represented by an abstract ontology-based domain model in the framework and implemented by a customisation engine. The key contribution is a conceptual definition of a domain-specific rule variability language.
Proceedings of the International Conference on Computer-Human Interaction Research and Applications, 2017
The domain-specific model-driven development requires effective and flexible techniques for imple... more The domain-specific model-driven development requires effective and flexible techniques for implementing domain-specific rule generators. In this paper, we present a framework for rule generation through model translation with feature model, a high-level of the domain model to translate into low-level of rule language based on the paradigm of software reuse in terms of customisation and configuration with domain-specific rule strategies benefit mode-to-text translations. This framework is domain-specific where non-technical domain user can customise and configure the business process models. These compositions support two dimensional of translation modularity by using software product line engineering. The domain engineering is achieved by designing the domain and process model as a requirement space, it is also called template model, connecting with feature model through weaving model. The feature model is a high-level input model to customise the template model to an implementation. The application engineering is achieved by supporting the rule definition and configuring the generated rules. We discuss the development approach of the framework in a domain-specific environment; we present a case study in a Digital Content Technology (DCT) domain.
Applied Drug Research, Clinical Trials and Regulatory Affairs, 2021
In this note we consider a system of financial institutions and study systemic risk measures in t... more In this note we consider a system of financial institutions and study systemic risk measures in the presence of a financial market and in a robust setting, namely, where no reference probability is assigned. We obtain a dual representation for convex robust systemic risk measures adjusted to the financial market and show its relation to some appropriate noarbitrage conditions.
We are primarily concerned with the utilization of a conceptual domain model for rule generation,... more We are primarily concerned with the utilization of a conceptual domain model for rule generation, specifically to define a domain-specific rule language (DSRL) [1, 2] syntax, its grammar for business process model and domain constraint management. We present a conceptual approach for outlining a DSRL for process constraints [3]. The domain-specific content model (DSCM) definition needs to consider two challenges. The first relates to the knowledge transfer from domain concept to conceptual model, where model inaccuracies and defects may have been translated because of misunderstandings, model errors, human errors or inherent semantic mismatches (e.g. between classes). The other problem relates to inconsistency, redundancy and incorrectness resulting from multiple views and abstractions. A domain-specific approach provides a dedicated solution for a defined set of
The challenges faced by domain experts, commitments made to domain-specific rule (DSR) languages ... more The challenges faced by domain experts, commitments made to domain-specific rule (DSR) languages and process design are described. We investigate the business application developments and existing challenges of evaluation strategies of DSR articulations. Often, multiple domain scenarios pose end-user predicaments complicating the computational ability of DSR. In addition, implementation of DSR and its configuration are belated due to poorly evaluated usability criteria. A new framework is needed, facilitating the DSR language and enhancing the computational intelligence. We intend to evaluate the performance of DSR generation and framework integration with variety of usability conditions including efficiency and effectiveness of configuration through system usability score (SUS). Empirical research involving experimental data, questionnaire surveys, and interview outcomes provide conclusive evaluation attributes and their fact instances from SUS. Both manual and semi-automatic configurations are tested. Semiautomatic configuration appears to be more efficient and satisfactory with regard to artefact performance, quality, learnability, user-friendly and reliability.
The unconventional digital petroleum ecosystems are associated with fractured reservoirs that are... more The unconventional digital petroleum ecosystems are associated with fractured reservoirs that are usually unpredictable, but can produce for longer periods depending on size of petroleum systems and basins. Currently, conventional reservoirs do produce oil & gas even without integrated workflows and solutions. The heterogeneity and multidimensionality of data sources at times can make the data documentation and integration complicated affecting the exploration and field development. We examine the conventional database technologies and their failures in organizing the data of unconventional digital ecosystems. Big Data driven intelligent information system solutions are needed for addressing the issues of complex data systems of unconventional digital ecosystems. Geographically distributed petroleum systems and their associated reservoirs too demand such integrated and innovative digital ecosystem solutions. We propose an innovative design science information system (DSIS), an integrated digital framework solution to explorers, dealing with unconventional fractured reservoirs. The integrated Big Data analytics solutions are effective in interpreting unconventional digital petroleum ecosystems that are impacted by shale prospect businesses worldwide.
Cancer is a deadly complex disease. Poorly aligned institutions, their data sources, and doctor-p... more Cancer is a deadly complex disease. Poorly aligned institutions, their data sources, and doctor-patient disengagement motivate us, strengthening collaborative cancer research. The research explores informatics solutions, by collating various cancer-linked data attributes and interconnecting them with spatial-temporal dimensions. The study is aimed at developing a methodological framework and investigating the open source empirical cancer data that involve diverse human ecosystems. We propose a Multidimensional Cancer Research Framework (MUCARF), responding to challenges of reporting, documenting and collaborating the data sources that characterize different cancer ailments including their worldwide causalities. Information system artefacts built based on cancer-domain ontologies, with similar and dissimilar attributes are integrated into MUCARF to dig the diagnostic cancer metadata views in new knowledge domains. Metadata model that replicates the design and development of MUCARF is...
Background: In shale basins, petroleum systems are complex; they hold data sources in Big Data sc... more Background: In shale basins, petroleum systems are complex; they hold data sources in Big Data scales. The motivation of research lies with the facts of exploring effective inherent connectivity between unconventional petroleum systems. The connectivity between energy reservoir systems is ambiguous within a distinctive petroleum ecosystem. Heterogeneity and multidimensionality of unstructured data sources are additional challenges, precluding systematic modelling of diverse petroleum systems and their data integration process, including growing demand for storage systems. The research aims to establish the knowledge-based connectivity between petroleum systems through Information System (IS) articulations, visual analytics and data management. Method: We investigate the knowledge-based IS guided exploration and production systems to explore the connectivity between diverse unconventional petroleum systems and forecast the reservoir energy. We articulate Design Science Information Sy...
In today’s changing world, there is an ever-increasing demand and need for software reuse in appl... more In today’s changing world, there is an ever-increasing demand and need for software reuse in applications, where the process model needs to be reused in different applications in a domain-specific environment. The process model is required to adapt and implement changes promptly at run-time, in response of the end-user configuration requirements. Furthermore, reusability is emerging strongly as a necessary underlying capability, particularly for customization of business in a dynamic environment where end-users can select their requirements to achieve a specific goal. Such adaptations are in general, performed by non-technical end-users which can lead to losing a significant number of person-days and which can also open up possibilities to introduce errors into the system. These scenarios call for - indeed cry out for - a system with a configurable and customizable business process, operable by users with limited technical expertise. Research aims to provide a framework for generati...
The research is aimed at investigating knowledge-based social informatics solutions. Socio-econom... more The research is aimed at investigating knowledge-based social informatics solutions. Socio-economic development relies on technology use in education and employment sectors. To explore such challenges, we examine the existing indicators of socio-economic development, such as gender equalities, employment, and education and population growth attribute dimensions. To understand them precisely, we analyse large-size human ecosystems and their data analytics. Social-informatics and -intelligence analysis are proposed with the design of logical and physical data schemas in diverse socio-economic contexts and their interoperability in varied geographies. We compute predictive models for different attribute dimensions, usable by technology developers and policy-makers. We interpret the data views of digital human ecosystems in the form of various graphs, tables, and polynomial regressions to envisage the influence of technology on societal collisions. The polynomial regressions suggest a strong positive relationship between different socio-economic attributes, cognizing the social intelligence and its knowledge management in Asia-Pacific contexts
Business process models are abstract descriptions that are applicable in different situations. To... more Business process models are abstract descriptions that are applicable in different situations. To allow a single process model to be reused, configuration and customisation features can help. Variability models, known from product line modelling and manufacturing, can control this customisation. While activities and objects have already been subject of similar investigations, we focus on the constraints that govern a process execution. We report here on the development a rule-based constraints language for a workflow and process model. The aim is a conceptual definition of a domain-specific rule variability language, integrated with the principles of a common business workflow or process notation. This modelling framework will be presented as a development approach for customised rules through a feature model. Our use case is content processing, represented by an abstract ontology-based domain model in the framework.
We identify various sports-related challenges pertinent to teams, player selections, and event ma... more We identify various sports-related challenges pertinent to teams, player selections, and event management. We examine both technical and managerial decisions made during player promotion, and various factors influenced the matches. We conceptualize that the game strategies, player and team selections, performance-based sports ecology management entities can blend into an informatics paradigm for which sports data modelling and analytics are required. In addition, reviews of players in social media have significance in weighing the economic value of players. With a motivation to systematize and franchise the branded teams and players to generate revenues, we aim at articulating a logical Sports Information System (SIS), for which various design-science guided artefacts are proposed. The research evaluates game strategies, the promotion of promising players, team-based performance, and economic indicators. Documentation of sports facts and logical storage models are vital to generate metadata and assess how players performed and what factors can motivate future matches
This research study the the influence of gender and family income on online educational experienc... more This research study the the influence of gender and family income on online educational experience dimensions in the Indian context.. The instrument used consisted of twenty items and demographics variables. Changes in demographic variable affect the online education experience of the user's dimension. Based on results the significant changes are identified and interpreted. Changes concerning demographic variables were observed in the factors of online user experience. The changes were most significant for the dimension: pragmatic-pleasurable experience and hedonic & exhaustive experience.
Fog Computing is a new computing paradigm which is grown ever since it is being used. It is aimed... more Fog Computing is a new computing paradigm which is grown ever since it is being used. It is aimed at bringing the computations close to data sources from healthcare centers. IoT driven Fog Computing is developed in the healthcare industry that can expedite facilities and services among the mass population and help in saving billions of lives. The new computing platform, founded as fog computing paradigm may help to ease latency while transmitting and communicating signals with remote servers, which can accelerate medical services in spatial-temporal dimensions. The latency reduction is one of the necessary features of computing platforms which can enable completing the healthcare operations, especially in large-size medical projects and in relation to providing sensitive and intensive services. Reducing the cost of delivering data to the cloud is one of the research objectives.
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Papers by Neel Mani