Semantic Interoperability in Heterogeneous IoT Infrastructure for Healthcare

Jabbar, Sohail; Ullah, Farhan; Khalid, Shehzad; Khan, Murad; Han, Kijun

doi:10.1155/2017/9731806

Cited by 147 publications

(98 citation statements)

References 31 publications

(32 reference statements)

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“…Semantic interoperability is combined with the cloud services which in turn are tied with the big data analytics. Their proposed model contains three components like sensor network, cloud and big-data semantic interoperability [10]. The researcher has developed a machine learning health care prediction model that takes the input form the tweets and sends it to the machine learning component of the system.…”

Section: Literature Surveymentioning

confidence: 99%

Big data management with machine learning inscribed by domain knowledge for health care

Ephzibah

Sujatha

2017

IJET

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In this work, a framework that helps in the disease diagnosis process with big-data management and machine learning using rule based, instance based, statistical, neural network and support vector method is given. Concerning this, big-data that contains the details of various diseases are collected, preprocessed and managed for classification. Diagnosis is a day-to-day activity for the medical practitioners and is also a decision-making task that requires domain knowledge and expertise in the specific field. This framework suggests different machine learning methods to aid the practitioner to diagnose disease based on the best classifier that is identified in the health care system. The framework has three main segments like big-data management, machine learning and input/output details of the patient. It has been already proved in the literature that the computing methods do help in disease diagnosis, provided the data about that particular disease is available in the data center. Thus this framework will provide a source of confidence and satisfaction to the doctors, as the model generated is based on the accuracy of the classifier compared to other classifiers.

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Section: Literature Surveymentioning

confidence: 99%

Big data management with machine learning inscribed by domain knowledge for health care

Ephzibah

Sujatha

2017

IJET

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“…Interoperability permits the interfaces of a framework to work with another framework with no confined access or execution [20]. This interoperability can be syntactic, semantic or Cross-area from the viewpoint of associated web objects i.e., the Web of Things (WoT) [11].…”

Section: Internet Of Things (Iot)mentioning

confidence: 99%

“…The absence of similar guidelines and calculations together increment the many-sided quality, wasteful aspects, client disappointment and the expense too. Displaying the knowledge of IoT articles can include a huge commitment in making this interoperability conceivable [20,21]. An inventive rundown of arrangements can be employed from computational insight areas (Fuzzy Logic, Neural Networks, Artificial Intelligence, Swarm Intelligence, and Genetic Algorithms), Machine adapting, Deep learning and their best in class augmentations.…”

Section: Internet Of Things (Iot)mentioning

confidence: 99%

Data Compatibility to Enhance Sustainable Capabilities for Autonomous Analytics in IoT

et al. 2017

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Abstract:The collection of raw data is based on sensors embedded in devices or the environment for real-time data extraction. Nowadays, the Internet of Things (IoT) environment is used to support autonomous data collection by reducing human involvement. It is hard to analyze such data, especially when working with the sensors in a real-time environment. On using data analytics in IoT with the help of RDF, many issues can be resolved. Resultant data will have a better chance of quality analytics by reforming data into the semantical annotation. Industrial correspondence through data will be improved by the induction of semantics at large due to efficient data capturing, whereas one popular medium of sensors' data storage is Relational Database (RDB). This study provides a complete automated mechanism to transform from loosely structured data stored in RDB into RDF. These data are obtained from sensors in semantically annotated RDF stores. The given study comprises methodology for improving compatibility by introducing bidirectional transformation between classical DB and RDF data stores to enhance the sustainable capabilities of IoT systems for autonomous analytics. Two case studies, one on weather and another on heart-rate measurement collections through IoT sensors, are used to show the transformation process in operation.

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“…A methodology for improving compatibility to become capable of transforming from the classical database (DB) into RDF base DB and then transforming back to classical DB is proposed. Such method would turn out to be useful when working in traditional systems along with semantically enriched systems even on the Internet …”

Section: Introductionmentioning

confidence: 99%

A generic methodology for geo‐related data semantic annotation

Malik

Habib

Khalid

et al. 2018

Concurrency and Computation

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Summary Geo‐related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real‐time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This research's major focus is on a data transformation methodology for geo‐related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi‐directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data.

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Semantic Interoperability in Heterogeneous IoT Infrastructure for Healthcare

Cited by 147 publications

References 31 publications

Big data management with machine learning inscribed by domain knowledge for health care

Big data management with machine learning inscribed by domain knowledge for health care

Data Compatibility to Enhance Sustainable Capabilities for Autonomous Analytics in IoT

A generic methodology for geo‐related data semantic annotation

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