Geo skip list data structure - storing spatial data and efficient search of geographical locations

Barewar, Amol; Radke, Mansi A.; Deshpande, Umesh

doi:10.1109/icacci.2014.6968370

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…For our algorithm, we use another famous indexing structure, K-d tree [31], which is similar to R-tree [31] but easier to deploy and more efficient in terms of building time. The only difference between R-and K-trees is that while the former uses MBRs to group data points, the latter recursively calculates median points and splits the points into two halves around them, thus reducing the building time.…”

Section: B Conceptual Foundations Of the Rknn Algorithmmentioning

confidence: 99%

Online Data Center Traffic Classification Based on Inter-Flow Correlations

et al. 2020

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Today, increasing attention is being paid to Data Center (DC) traffic classification since these infrastructures have become the heart of a variety of time-sensitive and data-intensive service platforms. Classification provides the required tools for better understanding traffic patterns in order to ensure high Quality of Service (QoS) performances and solve scalability problems. Unfortunately, existing classification algorithms cannot deal efficiently with two critical challenges in DC traffic: inter-class imbalance and critical time constraints. In this paper, we propose a novel correlation-based algorithm following a cost-sensitive approach combined with a Bagged Random Forest (BRF) ensemble algorithm, to address the inter-class imbalance problem while meeting time requirements in a data center environment. In this strategy, a new method based on Reverse k-Nearest Neighbors (RkNN) is proposed to capture the rebalancing weights expressing inter-flow correlations, in order to perform an online classification approach. We demonstrate the efficiency of the algorithm by comparing its performance to several existing methods from data level, algorithm level, and cost-sensitive strategies on four real-world datasets. The results reveal that the proposed algorithm outperforms most approaches in the different datasets in terms of precision, recall, F1 measure, AUC and Kappa, as opposed to other algorithms that result in either high precision with low recall and low precision and high recall causing congestion or resource over provisioning.

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Section: B Conceptual Foundations Of the Rknn Algorithmmentioning

confidence: 99%