Architecture for Virtualization in Data Warehouse

Nasir, Junaid; Shahzad, Muhammad Khurram

doi:10.1007/978-1-4020-6268-1_44

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…According to an exploratory study, the implementation of warehouse management systems can bring benefits related to increasing inventory accuracy, turnaround time, throughput, workload management, productivity, besides reducing labor cost and paperwork [12]. In a proposed architecture for virtualization of data in warehouses, the authors replace conventional data with a non-subjective, consistent, and time-variant type of data, making a synthetic warehouse for analytical processing, aiming scalability and source dynamics [13]. Radiofrequency identification, short-term scheduling and online monitoring are largely used in warehouses and industry [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

et al. 2021

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Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

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