Data-driven decision support under concept drift in streamed big data
- Publisher:
- Springer
- Publication Type:
- Journal Article
- Citation:
- Complex and Intelligent Systems, 2020, 6, (1), pp. 157-163
- Issue Date:
- 2020-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Lu2020_Article_Data-drivenDecisionSupportUnde.pdf | Published version | 609.24 kB |
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Data-driven decision-making ( D3 M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for D3 M. The study first establishes a technical framework for real-time D3 M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting D3 M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of D3 M in streamed big data environments.
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