Creation of column-oriented NoSQL databases automatically in Big Data environments and its impact on energy consumption

This study investigates the automatic creation of column-oriented NoSQL databases in Big Data environments and their impact on energy consumption. Traditional row-oriented databases face limitations in handling large volumes of data, resulting in slower query response times and energy inefficiencies. In contrast, column-oriented NoSQL databases store data in columns, enabling efficient compression, retrieval, and query processing. Innovative techniques are employed to automatically create these databases, optimizing performance and minimizing manual intervention. Storing data in a columnar format reduces storage requirements and power consumption while improving data locality and reducing I/O operations. This study emphasizes the benefits of adopting column-oriented NoSQL databases, including improved performance, scalability, and energy efficiency in Big Data environments

Design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model

An energy supply and demand forecasting system can help decision-makers grasp more comprehensive information, make accurate decisions and even plan a carbon-neutral future when adjusting energy structure, developing alternative energy resources and so on. This paper presents a hierarchical design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model called GM(1,1) which covers all the process of data collection, data analysis and data prediction. It mainly consists of three services, namely Crawler Service (CS), Algorithm Service (AS), Data Service (DS). The architecture of multiple loose coupling services makes the system flexible in more data, and more advanced prediction algorithms for future energy forecasting works. In order to make higher prediction accuracy based on GM(1,1), this paper illustrates some basic enhanced methods and their combinations with adaptable variable weights. An implementation for testing the system was applied, where the model was set up for coal, oil and natural gas separately, and the enhanced GM was better with relative error about 9.18% than original GM on validation data between 2010 and 2020. All results are available for reference on adjusting of energy structure and developing alternative energy resources.This research was funded by NSFC grant number 61972174, Guangdong Science and Technology Planning Project grant number 2020A0505100018, Guangdong Universities’ Innovation Team Project grant number 2021KCXTD015, Guangdong Key Disciplines Project grant number 2021ZDJS138, and 2021 University-level Teaching Quality Project grant number ZLGC20210203

EldyIoT: IoT assistive system for elderly

The Internet of Things (IoT) is one of the most promising technologies for the near future. IoT has penetrated many industries such as smart cities, smart homes, smart cars, healthcare, agriculture and manufacturing. In the health sector, the number of use cases has increased as a consequence of the improvement in the quality and effectiveness of the provision of hospital and care services. This work presents a solution to reduce the distance between caregivers and patients in a non-intrusive way, allowing information about the health status and mobility of patients to be available and accessible anywhere, anytime. Consecutively, it will increase the confidence of caregivers in relation to the health status of monitored patients and, on the other hand, it will increase the confidence of patients that their health status is being monitored frequently. Thus, this thesis presents a framework for physical monitoring of elderly people based on smartwatch devices. As an integral part of the solution, we present a cross-platform mobile application and a software framework assembled to support the processes of collecting, processing, storing, displaying and analyzing data.A Internet das Coisas (IoT) é uma das tecnologias mais promissoras para o futuro próximo. A IoT penetrou em muitos setores, como cidades inteligentes, casas inteligentes, carros inteligentes, saúde, agricultura e manufatura. No sector da saúde, o número de casos de uso, tem aumentado como consequência do incremento na qualidade e eficácia na prestação de serviços hospitalares e de cuidados. Este trabalho, apresenta uma solução para reduzir a distância entre cuidadores e pacientes de forma não intrusiva, permitindo que informações sobre o estado de saúde e mobilidade dos pacientes estejam disponíveis e acessíveis em qualquer lugar, e a qualquer hora. Consecutivamente, aumentará a confiança dos cuidadores em relação ao estado de saúde dos pacientes acompanhados e, por outro lado, aumentará a confiança dos pacientes de que o seu estado de saúde está a ser monitorizado com frequência. Assim, esta tese apresenta um framework para monitorização física de idosos baseada em dispositivos smartwatch. Como parte integrante da solução, apresentamos uma aplicação móvel cross-platform e uma estrutura de software montada para dar suporte aos processos de recolher, processamento, armazenamento, exibição e análise de dados

Database management system performance comparisons: A systematic literature review

Efficiency has been a pivotal aspect of the software industry since its inception, as a system that serves the end-user fast, and the service provider cost-efficiently benefits all parties. A database management system (DBMS) is an integral part of effectively all software systems, and therefore it is logical that different studies have compared the performance of different DBMSs in hopes of finding the most efficient one. This study systematically synthesizes the results and approaches of studies that compare DBMS performance and provides recommendations for industry and research. The results show that performance is usually tested in a way that does not reflect real-world use cases, and that tests are typically reported in insufficient detail for replication or for drawing conclusions from the stated results.Comment: 36 page

Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing

yesA large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases intergation, cooperation, and can also lead to innovative use of the data. Multicloud, privacy-aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads

Big Data in the Cloud: A Survey

Big Data has become a hot topic across several business areas requiring the storage and processing of huge volumes of data. Cloud computing leverages Big Data by providing high storage and processing capabilities and enables corporations to consume resources in a pay-as-you-go model making clouds the optimal environment for storing and processing huge quantities of data. By using virtualized resources, Cloud can scale very easily, be highly available and provide massive storage capacity and processing power. This paper surveys existing databases models to store and process Big Data within a Cloud environment. Particularly, we detail the following traditional NoSQL databases: BigTable, Cassandra, DynamoDB, HBase, Hypertable, and MongoDB. The MapReduce framework and its developments Apache Spark, HaLoop, Twister, and other alternatives such as Apache Giraph, GraphLab, Pregel and MapD - a novel platform that uses GPU processing to accelerate Big Data processing - are also analyzed. Finally, we present two case studies that demonstrate the successful use of Big Data within Cloud environments and the challenges that must be addressed in the future

Industry 4.0: Industrial IoT Enhancement and WSN Performance Analysis

L'abstract è presente nell'allegato / the abstract is in the attachmen

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Growth of relational model: Interdependence and complementary to big data

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system