Enactment of adaptation in data stream processing with latency implications—A systematic literature review

Qin, Cui; Eichelberger, Holger; Schmid, Klaus

doi:10.1016/j.infsof.2019.03.006

Cited by 19 publications

(26 citation statements)

References 48 publications

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“…Different trends toward the Internet of things (IoT), Industry 4.0 and 5G networks can improve logistics efficiency. Especially, sensors and sensor networks are embedded in the physical logistics infrastructure (Psyllidis, 2016; Qin et al , 2019) and they generate streams of data through, i.e. measurements and observations and deliver them directly to the system in a reliable, easy and quick manner.…”

Section: Literature Review: Domains and Dimensions Of Environmental Performance And Resilience In Logistics Infrastructurementioning

confidence: 99%

“…The data can report information in-real time about transport flows, trip generation, distribution and travel mode, environmental conditions, air quality, electricity usage, weather, temperature and humidity, sound levels, etc. In the continuous processing of data streams across transport supply chain for transport planning purposes, processing situation may vary over the time (Anda et al , 2017; Qin et al , 2019): while, dynamic characteristics of data streams needs to be handled effectively. During processing, the volume or velocity of data streams can change significantly.…”

Section: Literature Review: Domains and Dimensions Of Environmental Performance And Resilience In Logistics Infrastructurementioning

confidence: 99%

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Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Kayıkçı

2020

JEIM

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PurposeAs the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.Design/methodology/approachA causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.FindingsThe result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.Originality/valueIn this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

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Section: Literature Review: Domains and Dimensions Of Environmental Performance And Resilience In Logistics Infrastructurementioning

confidence: 99%

Section: Literature Review: Domains and Dimensions Of Environmental Performance And Resilience In Logistics Infrastructurementioning

confidence: 99%

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Kayıkçı

2020

JEIM

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“…The performance of the processing systems becomes an even bigger issues if the data is time-sensitive or if the success of whatever the data is used for depends on an immediate analysis. These data streams, which vary in volume and velocity and require a continuous processing, are, for instance, of importance in stock markets [56] and the tactical domain [57]. Especially in these fields of application, the focus is less on solely descriptive or predictive analyses, but rather the acquisition of prescriptive knowledge [58].…”

Section: Predictable Consequences Of Dynamic Business Environments Inmentioning

confidence: 99%

“…In a dynamic environment with distributed data sources, inter alia, the network capability might become the decisive bottleneck if a centralized approach is adopted [55]. To prevent the system from losing its value for the company due to bandwidth fluctuations [56], fog computing, in contrast to cloud computing, distributes the processing of big data at the edges of the network. Darwish and Abu Bakar [55] conducted a case study that investigates, how the dynamic environment of Intelligent Transport Systems (ITS) benefits from research in the big data area.…”

Section: Predictable Consequences Of Dynamic Business Environments Inmentioning

confidence: 99%

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Staegemann

Volk

Daase

et al. 2020

J. Complex Syst. Inform. Model. Q.

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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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“…Such variability, coming from both their workloads and the underlying computing infrastructure, require adaptation capabilities in order to achieve a consistent service level over time. Indeed, several adaptation mechanisms have been studied so far for of DSP systems [13], aiming to dynamically change, e.g., the accuracy of the algorithms used for processing data or the application deployment.…”

Section: Related Workmentioning

confidence: 99%

Model-based auto-scaling of distributed data stream processing applications

Russo

2020

Proceedings of the 21st International Middleware Conference Doctoral Symposium

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Enactment of adaptation in data stream processing with latency implications—A systematic literature review

Cited by 19 publications

References 48 publications

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Model-based auto-scaling of distributed data stream processing applications

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