Abstract
Humans are playing critical roles in the management of data at large scales, through activities including schema building, matching data elements, resolving conflicts, and ranking results. The application of human-in-the-loop within intelligent systems in smart environments presents challenges in the areas of programming paradigms, execution methods, and task design. This chapter examines current human-in-the-loop approaches for data management tasks, including data integration, data collection (e.g. citizen sensing), and query refinement. A comparison of approaches (Augmented Algorithms, Declarative Programming, and Stand-alone Platforms) that can enable human tasks within data management is presented. The chapter also covers spatial tasks where users within the smart environment are requested to take physical actions in the environment in the form of citizen actuation.
| Original language | English |
|---|---|
| Title of host publication | Real-time Linked Dataspaces |
| Subtitle of host publication | Enabling Data Ecosystems for Intelligent Systems |
| Publisher | Springer International Publishing |
| Pages | 139-158 |
| Number of pages | 20 |
| ISBN (Electronic) | 9783030296650 |
| ISBN (Print) | 9783030296643 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
Keywords
- Citizen sensing
- Crowdsourcing
- Dataspaces
- Human-in-the-loop
- Smart environments
- Smart environments
- Task routing
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