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- W4281557900 abstract "Data from many real-world applications often can be abstracted as graphs. However, the increasing graph size makes it impossible for existing popular exploratory data analysis tools to handle large data sets in the memory of a common laptop/personal computer. As a high-level exploratory data analytics framework, Arkouda aims to support not only flexible but also high-performance large-scale data analysis. There are three major parts: The detailed description of the edge and vertex arrays. The time and space complexity analysis based on the proposed data structure. A comparison with the CSR data structure. Multilocale algorithms can take advantage of both the distributed resources and parallel resources of single shared memory computing node. Experimental results on two kinds of large sparse graph streams, normal distribution and power law distribution, show that the proposed method can achieve very high approximate results with the mean error no larger than 4%." @default.
- W4281557900 created "2022-05-27" @default.
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- W4281557900 date "2022-05-26" @default.
- W4281557900 modified "2023-09-29" @default.
- W4281557900 title "Interactive Graph Analytics at Scale in Arkouda" @default.
- W4281557900 doi "https://doi.org/10.1201/9781003033707-26" @default.
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