Reinventing the Energy Bill in Smart Cities with NoSQL Technologies

Costa, Carlos; Santos, Maribel Yasmina

doi:10.1007/978-981-10-1088-0_29

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…Thus, data collected from heterogeneous sources such as energy metering devices, appliances, etc. can be utilized by consumers in making decisions on energy consumption and trading (Costa and Santos 2016;Silva, Khan, and Han 2018). There are four main characteristics of big data, which comprise of volume, velocity, variety, and veracity as presented in Figure 6.…”

Section: Applicability Of Big Data For Energy Prosumptionmentioning

confidence: 99%

“…Similarly, Schleicher et al (2016) recommended that the architecture should be flexible to extend its capacity of data processing to provide procedure to fully resolve the complex security compliance, regulations, restrictions, and ownership that arise. Likewise, Costa and Santos (2016) stated that such approach should facilitate municipality administrators, government and energy providers to efficiently manage energy consumption of cities.…”

Section: Applicability Of Big Data For Energy Prosumptionmentioning

confidence: 99%

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Smart city data architecture for energy prosumption in municipalities: concepts, requirements, and future directions

Anthony

2020

International Journal of Green Energy

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Big data is gaining visibility and importance, and its use is attaining higher levels of influence within municipalities. Due to this proliferation smart cities are posed to deploy architectures toward managing energy for Electric Vehicles (EV) and orchestrate the production, consumption, and distributing of energy from renewable sources such as solar, wind etc. in communities also known as prosumption. In smart city domain, Enterprise Architecture (EA) can be employed to facilitate alignment between municipality goals and the direction of the city in relation to Information Technology (IT) that supports stakeholders within the city. Hence, the alignment between IT and goals of the city is a critical process to support the continued growth and improvement of city services and energy sustainability. However, despite several research effort focused on data architecture in smart city, there have been few studies aimed at exploring how EA can be applied in smart cities to support residential buildings and EV for energy prosumption in municipalities. Therefore, this study conducts an extensive review and develops an architecture that can be employed in smart city domain based on big data management for energy prosumption in residential buildings and EV. Furthermore, secondary data was employed to present a case study to show the applications of the developed architecture in promoting energy prosumption. Findings suggest that the architecture provides interoperable open real-time, online, and historical data in facilitating energy prosumption. Respectively, this study offers exchange of data for sharing energy resources and provides insights to improve energy prosumption services.

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Section: Applicability Of Big Data For Energy Prosumptionmentioning

confidence: 99%

Section: Applicability Of Big Data For Energy Prosumptionmentioning

confidence: 99%

Smart city data architecture for energy prosumption in municipalities: concepts, requirements, and future directions

Anthony

2020

International Journal of Green Energy

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“…The above preprocessed data may be transferred and loaded from the temporary data stores into the Raw data store for further processing and analysis. For example, Hadoop Distributed File System (HDFS) for the storage of batch data, as well as Apache Cassandra and Apache HBase for the storage of streaming data [Costa and Santos 2016;Santos et al 2017]. -Information extraction, which aims at imposing a structured format on raw data, that is suitable for analysis.…”

Section: Introductionmentioning

confidence: 99%

Big Data Systems

Davoudian

Liu

2020

ACM Comput. Surv.

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Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.

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“…Therefore, related research works have academic significance and practical values. Typical applications of NoSQL databases involve many important areas, such as the Internet, mobile computation, telecommunications, bioinformatics, education, and energy …”

Section: Introductionmentioning

confidence: 99%

An integration approach of hybrid databases based on SQL in cloud computing environment

2018

Softw Pract Exp

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As the applications with big data in cloud computing environment grow, many existing systems expect to expand their service to support the dramatic increase of data, and modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture design. This paper proposes an integration approach to support hybrid database architecture, including MySQL, MongoDB, and Redis, to make it possible of allowing users to query data simultaneously from both relational SQL systems and NoSQL systems in a single SQL query. Two mechanisms are provided for constructing Redis's indexes and semantic transforming between SQL and Mon-goDB API to add the SQL feature for these NoSQL databases. With the proposed approach, hybrid database systems can be performed in a flexible manner, ie, access can be either relational database or NoSQL, depending on the size of data. The approach can effectively reduce development complexity and improve development efficiency of the software systems with multidatabases. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology. /journal/spe 401 402 LI AND GU applications of NoSQL (not only SQL) database systems become more and more popular, it also poses a greater challenge to the development methods and technical architecture of software systems. A dramatic change, or called a revolution, is taking place in the field of database management technology as the growing of applications with big data in cloud computing environment. In June 1970, Edgar Frank Codd, a researcher of IBM company, published the famous paper, ie, A Relational Model of Data for Large Shared Data Banks, in Communications of ACM, which established the theoretical model of relational database. After decades of development, the relational database management system has gradually become mature, and the SQL (structured query language) used to access the relational database system also has been developed into the standard of all relational databases. However, big data and hybrid database system are becoming popular as cloud service blooms, and NoSQL database systems, such as Redis, 9 MongoDB, 10 HBase, 11 and Neo4J, 12 also have been developed rapidly.NoSQL systems have been developed to support applications not well served by relational systems, often involving big data processing. 13 Therefore, related research works have academic significance and practical values. Typical applications of NoSQL databases involve many important areas, such as the Internet, 14 mobile computation, 15 telecommunications, 16 bioinformatics, 17 education, 18 and energy. 19 Major NoSQL databases can be classified into four categories, [20][21][22] including key-value store, column-oriented store, document-oriented store, and graph store. Representative NoSQL database products include...

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Reinventing the Energy Bill in Smart Cities with NoSQL Technologies

Cited by 8 publications

References 18 publications

Smart city data architecture for energy prosumption in municipalities: concepts, requirements, and future directions

Smart city data architecture for energy prosumption in municipalities: concepts, requirements, and future directions

Big Data Systems

An integration approach of hybrid databases based on SQL in cloud computing environment

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