A methodology for real-time data sustainability in smart city: Towards inferencing and analytics for big-data

Malik, Kaleem Razzaq; Sam, Yacine; Hussain, Majid; Abuarqoub, Abdelrahman

doi:10.1016/j.scs.2017.11.031

Cited by 51 publications

(35 citation statements)

References 10 publications

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“…The process of data transformation in all aspects of keeping lossless to less is hard but not impossible to achieve. Must needed requirement is to be carefully checking the availability of mapping mechanism to become able in constructing a new one in case of noncompliance with transformation process to complete . To achieve similar capabilities, the proposed model being introduced is using XML as an intermediate level of data transformation process.…”

Section: Spatial Data Transformation Methodologymentioning

confidence: 99%

“…Must needed requirement is to be carefully checking the availability of mapping mechanism to become able in constructing a new one in case of noncompliance with transformation process to complete. 3,30 To achieve similar capabilities, the proposed model being introduced is using XML as an intermediate level of data transformation process. The abstract model of the proposed bi-directional transformation process is represented showing transformation-taking place between RDB and RDF, as shown in Figure 3, whereas XML is mandatory coming in-between of both transformations of data is from RDB to RDF or RDF to RDB.…”

Section: Spatial Data Transformation Methodologymentioning

confidence: 99%

“…In this research, an idea is introduced that allows doing data mapping used to understand their differences at the level of data types. 3 This mapping is done using Extensible Markup Language (XML)-based data structure as their intermediate data presenter. We performed control experiment to investigate whether Extensible Markup Language Schema (XMLS) works better for performing the transformation from RDB to RDF.…”

Section: Figurementioning

confidence: 99%

“…The literature describes several rigid techniques that do the transformation from RDB to RDF with limited customization but failed to present a common way that helps to avoid compatibility issue. In this research, an idea is introduced that allows doing data mapping used to understand their differences at the level of data types . This mapping is done using Extensible Markup Language (XML)–based data structure as their intermediate data presenter.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Spatial Data Transformation Methodologymentioning

confidence: 99%

Section: Spatial Data Transformation Methodologymentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A generic methodology for geo‐related data semantic annotation

Malik

Habib

Khalid

et al. 2018

Concurrency and Computation

Self Cite

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“…We use a microcontroller to interlink with other kind of devices and to make decisions, as listed in table 1. Figure 7 illustrates how the setup operates [27].…”

Section: Case Study: Semantic Interoperability For Home Appliance Autmentioning

confidence: 99%

Semantic Interoperability for Context-Aware Autonomous Control using IoT and Edge Computing

Malik

Khan

Aloufi

et al. 2020

Preprint

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The exponential development of Internet of things (IoT) services over edge computing and cloud networks has increased the utilities of remote monitoring, control systems, continuous maintenance and effective utilization of services for applications, such as smart cities. However, data modelling is required to manage such heterogeneous data sources. IoT applications gather data from diverse sources. These applications sometimes obtain data in the form of datasets. Heterogeneous datasets are used for various purposes, and the issue of semantic interoperability arises. Therefore, this paper presents an empirical study of IoT-based semantic interoperability. This study aims at combining portable and fixed sensors with an intermediate microcontroller module and annotating data semantically for the smart autonomous environment, smart home. A context model is devised for developing a mechanism over an ontology schema for managing and passing controlling and monitoring messages to home appliances effectively. The proposed model integrates the environment with the context of a person in a smart autonomous environment for efficient energy consumption and enhanced living context model experience.

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Hybrid approach for big data localization and semantic annotation

Ramay

Cheng-Yin

Rahman

et al. 2018

Concurrency and Computation

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Summary Most of the data concerning business‐oriented systems are still based on either NoSQL or the relational data model. On the other hand, Semantic Web data model Resource Description Framework (RDF) has become the new standard for data modeling and analysis. Due to this situation integration of NoSQL, Relational Database (RDB) and RDF data models are becoming a required feature of the systems. Many solutions like tools and languages are provided in the shape of the transformation of data from RDB to RDF. This research is aimed to compare and map data models used for transformation between NoSQL, RDB, and Semantic Web. This study will help in achieving much better and enhanced technology‐based systems for retrieval and storage of data among Big‐data annotation using Semantic Web. It is aimed to reduce the response time of queries and offer compatibility with the web and semantically enriched data format. A drugs dataset is being used and transformed to have semantical meaning embedded and linked to support big data localization. At the end of this paper, RDF graph and bar chart are used to represent transformed data after passing through the proposed model. Big data localization helps in gaining fast and accurate results.

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A methodology for real-time data sustainability in smart city: Towards inferencing and analytics for big-data

Cited by 51 publications

References 10 publications

A generic methodology for geo‐related data semantic annotation

A generic methodology for geo‐related data semantic annotation

Semantic Interoperability for Context-Aware Autonomous Control using IoT and Edge Computing

Hybrid approach for big data localization and semantic annotation

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