基于大数据的实时协同规划:技术挑战和就地计算(特邀论文)

Wenwey Hseush, Yi-Cheng Huang, Shih-Chang Hsu, C. Pu
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引用次数: 7

摘要

在新一代供应链规划应用程序中,协作越来越多,供应链上的参与者通过互联网一起分析和规划大量销售数据。为了实现对大数据的实时协同规划,我们开发了一种非传统的技术,BigObject,基于两种方式的就地计算方法。首先,不要移动(大)数据,而是将(小)代码移动到数据所在的位置执行。其次,通过确定计算的基本功能单位(对象)来组织复杂性,就像确定活细胞的大分子一样。术语“就地”表示数据驻留在内存空间中并准备进行计算。BigObject是一个就地计算系统,用于存储和计算多维数据。我们的实验表明,就地计算方法在两个数量级上优于传统计算方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time collaborative planning with big data: Technical challenges and in-place computing (invited paper)
There is increasing collaboration in new generation supply chain planning applications, where participants across a supply chain analyze and plan on a big volume of sales data over the internet together. To achieve real-time collaborative planning over big data, we have developed an unconventional technology, BigObject, based on an in-place computing approach in two ways. First, instead of moving (big) data around, move (small) code to where data resides for execution. Second, organize the complexity by determining the basic functional units (objects) for computing in the same sense that macromolecules are determined for living cells. The term ”in-place” indicates that data is in residence in memory space and ready for computing. BigObject is an in-place computing system, designed for storing and computing multidimensional data. Our experiment shows that in-place computing approach outperforms traditional computing approach in two orders of magnitude.
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