Research Article
Real-Time Collaborative Planning with Big Data: Technical Challenges and In-Place Computing
@INPROCEEDINGS{10.4108/icst.collaboratecom.2013.254100, author={Wenwey Hseush and Yi-Cheng Huang and Shih-Chang Hsu and Calton Pu}, title={Real-Time Collaborative Planning with Big Data: Technical Challenges and In-Place Computing}, proceedings={9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing}, publisher={ICST}, proceedings_a={COLLABORATECOM}, year={2013}, month={11}, keywords={in-place computing big data database real-time system transformation programming in-memory computing}, doi={10.4108/icst.collaboratecom.2013.254100} }
- Wenwey Hseush
Yi-Cheng Huang
Shih-Chang Hsu
Calton Pu
Year: 2013
Real-Time Collaborative Planning with Big Data: Technical Challenges and In-Place Computing
COLLABORATECOM
IEEE
DOI: 10.4108/icst.collaboratecom.2013.254100
Abstract
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.