Optimizing Intelligent Reduction Techniques for Big Data

Pop, Florin; Negru, Cătălin; Ciolofan, Sorin N.; Mocanu, Mariana; Cristea, Valentin

doi:10.1007/978-3-319-30265-2_3

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Moreover, thanks to the new real-time systems of air and maritime pollution detection (Negru et al 2015), it is now possible to identify the most polluted areas of the city and, as a consequence, to adapt both the urban traffic (Gosman et al 2016) and the transportation of the goods. Usually these tools generate a huge quantity of information (Big Data) that can be processed only with appropriate techniques as described by Pop et al 2016. In the present paper, we will make evidence that when the planning of urban distribution tours is made including environmental aspects in the cost function to be minimized, the choice of the selected vehicles changes accordingly. In particular, we will show that the optimal solution obtained considering at the same time the travel and pollution costs is better than that obtained by adding the pollution cost to the optimal solution obtained considering only the traveling cost.…”

Section: Introduction and Problem Definitionmentioning

confidence: 97%

An exact approach for the grocery delivery problem in urban areas

2016

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In this paper, we face the problem of delivering a given amount of goods in urban areas in a business-to-consumer (B2C) electronic commerce (EC) environment. This problem can be considered as a particular case of vehicle routing problem. As a novel issue, here we have to determine the fleet of no homogeneous vehicles to be used for satisfying the demands of clients coming from grocery e-channels, and their related itineraries, given the traveling limits imposed by the urban government; in fact, commercial vehicles are not allowed to go everywhere and can travel only in restricted daily time windows, according to their pollution emissions. We have to minimize the overall distribution costs, taking into account traveling components and setup ones, together with operative aspects and environmental issues; customer requirements, vehicle capacity and daily shift constraints have to be satisfied too. We outline the main characteristics of the problem in a B2C EC environment and propose a mixed integer linear programming model to solve this NP-hard problem. Computational results of test bed cases related to different sized transportation networks and delivery demands are presented and analyzed with respect to the fleet of vehicles chosen for satisfying the customer demand and the street traffic limitations. Then, a realistic case study derived from the e-distribution channel of a grocery company of Genoa, Italy, is reported. Considerations about CPU time and optimality gap are also given with the idea of making the proposed model effectively used and solved with any commercial software

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Section: Introduction and Problem Definitionmentioning

confidence: 97%

An exact approach for the grocery delivery problem in urban areas

2016

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“…The benefits resulting from this synergistic relationship are exposed by new Big Data infrastructures, tools and technologies that have adopted bio-inspired algorithms to reach a higher level of efficiency in their tasks. Some few examples of technologies that take advantage of the capabilities of bio-inspired algorithms are, among many others, NoSQL databases [ 13 – 15 ], load planners/schedulers [ 16 ], or tools assisting analytical tasks such as feature selection [ 17 ], dimensionality reduction [ 18 ] or data fusion [ 19 ]. On the other hand, through bio-inspired computation perspective, Big Data provides the possibility of great volumes and varieties of data and the efficient implementation of solvers through new technologies, which offer parallel, distributable and scalable workloads.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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How big data-driven organizational capabilities shape innovation performance? An empirical study from small and medium manufacturing enterprises

Al-Khatib

2023

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PurposeThis study mainly aims to explore the causal nexus between big data-driven organizational capabilities (BDDOC) and supply chain innovation capabilities (SCIC) and innovation performance (IP), then explore the indirect effect of SCIC and also test the moderating effects for both internal supply chain integration (ISCI) and external supply chain integration (ESCI) into the relationship between BDDOC and SCIC.Design/methodology/approachIn order to test the conceptual model and the hypothesized relationships between all the constructs, the data were collected using a self-reported questionnaire by workers in Jordanian small and medium manufacturing enterprises. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the model.FindingsThe paper reached a set of interesting results where it was confirmed that there is a positive and statistically significant relationship between BDDOC, SCIC and IP in addition to confirming the indirect effect of SCIC between BDDOC and IP. The results also showed that there is a moderating role for both ESCI and ISCI.Originality/valueThis study can be considered the first study in the current literature that investigates these constructs as shown in the research model. Therefore, the paper presents an interesting set of theoretical and managerial contributions that may contribute to covering part of the research gap in the literature.

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Optimizing Intelligent Reduction Techniques for Big Data

Cited by 3 publications

References 39 publications

An exact approach for the grocery delivery problem in urban areas

An exact approach for the grocery delivery problem in urban areas

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

How big data-driven organizational capabilities shape innovation performance? An empirical study from small and medium manufacturing enterprises

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