Design Methodology of Microservices to Support Predictive Analytics for IoT Applications

Abstract

In the era of digital transformation, the Internet of Things (IoT) is emerging with improved data collection methods, advanced data processing mechanisms, enhanced analytic techniques, and modern service platforms. However, one of the major challenges is to provide an integrated design that can provide analytic capability for heterogeneous types of data and support the IoT applications with modular and robust services in an environment where the requirements keep changing. An enhanced analytic functionality not only provides insights from IoT data, but also fosters productivity of processes. Developing an efficient and easily maintainable IoT analytic system is a challenging endeavor due to many reasons such as heterogeneous data sources, growing data volumes, and monolithic service development approaches. In this view, the article proposes a design methodology that presents analytic capabilities embedded in modular microservices to realize efficient and scalable services in order to support adaptive IoT applications. Algorithms for analytic procedures are developed to underpin the model. We implement the Web Objects to virtualize IoT resources. The semantic data modeling is used to promote interoperability across the heterogeneous systems. We demonstrate the use case scenario and validate the proposed design with a prototype implementation.

Transformação Digital Sob a Ótica Da Engenharia Do Conhecimento

“…A maior parte dos trabalhos (oito deles) foram classificados na área de Ciência da Computação. Information Management 2 (Bruno & Antonelli, 2018); (Zimmermann et al, 2015) Internet Of Things 2 (Ali, Jarwar & Chong, 2018); (Zimmermann et al, 2015) Knowledge Management 2 (Mizintseva & Gerbina, 2018); (Wang et al, 2017) Fonte: Dados da pesquisa (2019).…”

“…A Indústria 4.0 está sendo desenvolvida juntamente com a TD, pois se trata de uma mudança nos processos industriais, com a inclusão de tecnologias que permitem maior automação e controle nas linhas de produção. Há uma inclusão de dispositivos inteligentes interconectados que, através das redes de conhecimento ontológicas, permitem o reconhecimento de padrões e a reutilização de conhecimentos, sendo esta digitalização das operações conhecida por Internet das Coisas (IoT) (Ali, Jarwar & Chong, 2018).…”

Architecturing Elastic Edge Storage Services for Data-Driven Decision Making

“…Tooling: A repository of available and pluggable microservices can speed up the DevOps of storage services by supplying needed utilities. Different microservices can be used to enable elastic activities, such as data cleaning, normalization and data integration [2]. To keep relevant and complete data in space-limited storage, nodes might incorporate an adaptive algorithm for efficient edge storage management and an automatic mechanism for recovery of incomplete datasets.…”

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

“…The Web of Objects (WoO) is a simple but efficient services platform that supports a robust way to develop application services with data abstraction using Virtual Objects (VOs) and Composite Virtual Objects (CVOs) [20]. It fosters to analyze a very huge amount of data using microservices and VOs and CVOs ontologies [21]. The intrinsic and contextual data quality assessment processing services are distributed and scaled into many microservices to increase the availability of quality data for the semantic data applications [22,23].…”