Using microservices and event driven architecture for big data stream processing

Zhelev, Svetoslav; Rozeva, Anna

doi:10.1063/1.5133587

Cited by 17 publications

(12 citation statements)

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“…For instance, in the preprocessing phase, the abstraction and thus simplification, as well as the extraction of features and the removal of noise [62] and unneeded attributes by intelligent systems can lower the workload and thereby improve the performance [53]. Due to the fact that both, knowledge in the problem domain as well as in the technology landscape [63], are required to adjust a big data analytics system in a specific scenario, the time consumption for the entire development team would dynamically increase with the upcoming of new data structures. At some point, human workforce would not be capable of integrating new tailored algorithmic solutions as quickly as new requirements arise.…”

Section: Unforeseeable Consequences Of Dynamic Business Environments mentioning

confidence: 99%

“…As a result, those microservice architectures were not only mentioned as a promising approach in the course of the interviews, but also gain interest from academics and practitioners [73]. These services are described as autonomous and isolated pieces of software, which means that they are designed, developed, tested and deployed independently and have only one small purpose each [63], [71]. Accordingly, the individual components are equipped with dedicated tools with optimum suitability [70].…”

Section: Facilitating the Modifiability Of Bda Solutionsmentioning

confidence: 99%

“…Accordingly, the individual components are equipped with dedicated tools with optimum suitability [70]. This includes the possibility of using distinct programming languages for different services, allowing the most appropriate frameworks to be utilized for each purpose [63]. In contrast to the monolithic approach, microservice architectures let the designed solutions evolve dynamically alongside the general purpose of the big data analytics system.…”

Section: Facilitating the Modifiability Of Bda Solutionsmentioning

confidence: 99%

“…The example in Figure 9 shows how a microservice architecture can be implemented in big data analytics. As recommended by [63] for larger applications, an API gateway is used as an entry point from the client side to distribute user requests to the appropriate services. In the figure, the services are organized in logical domains based on the layers in the layer architecture.…”

Section: Facilitating the Modifiability Of Bda Solutionsmentioning

confidence: 99%

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Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Staegemann

Volk

Daase

et al. 2020

CSIMQ

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Big data attracts researchers and practitioners around the globe in their desire to effectively manage the data deluge resulting from the ongoing evolution of the information systems domain. Consequently, many decision makers attempt to harness the potentials arising with the use of those modern technologies in a multitude of application scenarios. As a result, big data has gained an important role for many businesses. However, as of today, the developed solutions are oftentimes perceived as completed products, without considering that the application in highly dynamic environments might benefit from a deviation of this approach. Relevant data sources as well as the questions that are supposed to be answered by their analysis may change rapidly and so do subsequently the requirements regarding the functionalities of the system. To our knowledge, while big data itself is a prominent topic, fields of application that are likely to evolve in a short period of time and the resulting consequences were not specifically investigated until now. Therefore, this research aims to overcome this paucity by clarifying the relation between dynamic business environments and big data analytics (BDA), sensitizing researchers and practitioners for future big data engineering activities. Apart from a thorough literature review, expert interviews are conducted that evaluate the made inferences regarding dynamic and stable influencing factors, the influence of dynamic environments on BDA applications as well as possible countermeasures. The ascertained insights are condensed into a proposal for decision making, facilitating the alignment of BDA and business needs in dynamic business environments.

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Section: Unforeseeable Consequences Of Dynamic Business Environments mentioning

confidence: 99%

Section: Facilitating the Modifiability Of Bda Solutionsmentioning

confidence: 99%

Section: Facilitating the Modifiability Of Bda Solutionsmentioning

confidence: 99%

Section: Facilitating the Modifiability Of Bda Solutionsmentioning

confidence: 99%

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Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Staegemann

Volk

Daase

et al. 2020

CSIMQ

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“…Researchers are trying to introduce miro-service architecture into many different arear [17]. Svetoslav Zhelev et al use microservices and event driven architecture to implement big data stream processing [36]. Alexandr Krylovskiy et al apply the miroservice architecture style to design a Smart City IoT platform [20].…”

Section: Related Workmentioning

confidence: 99%

Clustering-Based Emotion Recognition Micro-Service Cloud Framework for Mobile Computing

Wang

Dong

et al. 2020

IEEE Access

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In a situation where life becomes more stressful and challenging, people feel compelled to be more concerned about their mental situation. Different emotional statuses are external reactions to different mental states. Therefore, researchers always identify people's mental situation by monitoring their real-time emotions. At the same time, due to the availability of built-in sensors in a smartphone, applications that can identify real-time emotions of mobile users are constantly emerging. However, compared to most emotion recognition algorithms, computing resources and battery life in mobile phones are always limited. This makes accuracy and latency of these applications are unsatisfactory. In this paper, we propose a micro-service platform for mobile emotion recognition application developers (MSPMERAD) which can supply high performance. First, a classifier fusion emotion recognition algorithm is proposed by using a dynamic adaptive fusion strategy. Second, this new algorithm is encapsulated into a micro-service. With other affiliated microservices such as data uploading, preprocessing, etc., developers can ignore the implementation of the emotion recognition algorithm and just focus on how to collect sensor data and interact with users. The accuracy and latency of one application based on the MSPMERAD are compared with another application that is implemented using a locale emotion recognition algorithm. Experiments based on the daily behavior data of 50 student volunteers show that the application based on our platform has higher recognition accuracy with a more reasonable time.

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Phi: A Generic Microservices-Based Big Data Architecture

Maamouri

Sfaxi

Robbana

2022

Information Systems

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Using microservices and event driven architecture for big data stream processing

Cited by 17 publications

References 1 publication

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Clustering-Based Emotion Recognition Micro-Service Cloud Framework for Mobile Computing

Phi: A Generic Microservices-Based Big Data Architecture

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