Big data analytics for proactive industrial decision support: Approaches and frst experiences in the FEE Project
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project.
Details
| Original language | English |
|---|---|
| Pages (from-to) | 62-74 |
| Number of pages | 13 |
| Journal | GWF, Wasser - Abwasser |
| Volume | 157 |
| Issue number | 9 |
| Publication status | Published - 2016 |
| Peer-reviewed | Yes |
External IDs
| ORCID | /0000-0001-5165-4459/work/174432568 |
|---|
Keywords
DFG Classification of Subject Areas according to Review Boards
Subject groups, research areas, subject areas according to Destatis
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- Big data, Data analytics, Decision support