Unified representation method of heterogeneous data in industrial Internet
draft-zhang-ietf-heterogeneous-data-representation-01
| Document | Type |
Expired Internet-Draft
(individual)
Expired & archived
|
|
|---|---|---|---|
| Authors | Yaqian Zhang , Shengsheng He , YP Chen , ZM Wang , H Xia | ||
| Last updated | 2022-08-14 (Latest revision 2022-02-10) | ||
| RFC stream | (None) | ||
| Intended RFC status | (None) | ||
| Formats | |||
| Stream | Stream state | (No stream defined) | |
| Consensus boilerplate | Unknown | ||
| RFC Editor Note | (None) | ||
| IESG | IESG state | Expired | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:
Abstract
With the advent of 5G era, sensing devices and mobile Internet devices in smart factories are everywhere, and a variety of industrial data from different spatial devices becomes widely available and interwoven. These data are usually generated by streaming, with huge differences in data sources and structures, massive scale, strong correlation and complicated relationship. The great richness of data makes the problem of how to quickly, accurately and deeply dig the hidden value behind the data more complicated than ever. The data generated in different fields are distributed in a variety of business systems, and these data have different structures and forms, so it is difficult to use an efficient form of unified analysis. Based on the data characteristics of heterogeneous data, the multi-source heterogeneous data fusion method is studied based on tensor.
Authors
Yaqian Zhang
Shengsheng He
YP Chen
ZM Wang
H Xia
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)