References of "Brorsson, Mats Hakan 50035393"
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See detailPROJECT DASHBOARD
Blanco, Braulio UL; Brorsson, Mats Hakan UL

Report (2022)

4th deliverable SCRIPT Project

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See detailREPORT OF DATA FUSION AND EVALUATION
Wang, Xin Lin UL; Blanco, Braulio UL; Brorsson, Mats Hakan UL

Report (2022)

The third deliverable for the SCRIPT Project

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See detailAt the Edge of a Seamless Cloud Experience
Rac, Samuel UL; Brorsson, Mats Hakan UL

E-print/Working paper (2021)

There is a growing need for low latency for many devices and users. The traditional cloud computing paradigm can not meet this requirement, legitimizing the need for a new paradigm. Edge computing ... [more ▼]

There is a growing need for low latency for many devices and users. The traditional cloud computing paradigm can not meet this requirement, legitimizing the need for a new paradigm. Edge computing proposes to move computing capacities to the edge of the network, closer to where data is produced and consumed. However, edge computing raises new challenges. At the edge, devices are more heterogeneous than in the data centre, where everything is optimized to achieve economies of scale. Edge devices can be mobile, like a car, which complicates architecture with dynamic topologies. IoT devices produce a considerable amount of data that can be processed at the Edge. In this paper, we discuss the main challenges to be met in edge computing and solutions to achieve a seamless cloud experience. We propose to use technologies like containers and WebAssembly to manage applications' execution on heterogeneous devices. [less ▲]

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See detailDATA DISTRIBUTION API SPECIFICATION
Blanco, Braulio UL; Brorsson, Mats Hakan UL

Report (2021)

The second deliverable for the Script Project: API Specification

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See detailREPORT OF DATA SOURCES
Wang, Xin Lin UL; Blanco, Braulio UL; Brorsson, Mats Hakan UL

Report (2021)

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Peer Reviewed
See detailTime Series Modeling of Market Price in Real-Time Bidding
Du, Manxing UL; Hammerschmidt, Christian UL; Varisteas, Georgios UL et al

in 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2019, April)

Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions ... [more ▼]

Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions fluctuate heavily within short time spans. State-of-the-art methods neglect the temporal dependencies of bidders’ behaviors. In this paper, the bid requests are aggregated by time and the mean market price per aggregated segment is modeled as a time series. We show that the Long Short Term Memory (LSTM) neural network outperforms the state-of-the-art univariate time series models by capturing the nonlinear temporal dependencies in the market price. We further improve the predicting performance by adding a summary of exogenous features from bid requests. [less ▲]

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See detailRegression-based prediction for task-based program performance
Oz, Isil; Bhatti, Muhammad Khurram; Popov, Konstantin et al

in Journal of Circuits, Systems and Computers (2019), 28(04), 1950060

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See detailKnow Your Enemies and Know Yourself in the Real-Time Bidding Function Optimisation
Du, Manxing UL; Cowen-Rivers, Alexander I.; Wen, Ying et al

in Proceedings of the 19th IEEE International Conference on Data Mining Workshops (ICDMW 2019) (2019)

Detailed reference viewed: 404 (5 UL)