Name
Abstract | ||
|---|---|---|
Measuring change is increasingly a computational task, but understanding change and its implications are fundamentally human challenges. Successful human/machine teams for streaming data analysis effectively balance data velocity with people’s capacity to ingest, reason about, and act upon the data. Computational support is critical to aiding humans with finding what is needed when it is needed. This is particularly evident in supporting complex sensemaking, situation awareness, and decision making in streaming contexts. Herein, we conceptualize human/machine teams as interacting streams of data, generated from the interactions that are core to the human/machine team activity. These streams capture the relative velocities of the human and machine activities, which allows the machine to balance the capabilities of the two halves of the system. We review the known challenges in handling interacting streams that have been distilled in computational systems. And we use this perspective to understand some of the open challenges to designing effective human/machine systems that support the disparate velocities of humans and machines. |
Year | Venue | Field |
|---|---|---|
2018 | HCI | Human–machine system,Situation awareness,Computer science,Sensemaking,Visual analytics,Human–computer interaction,Streaming data,Big data,Human machine interaction |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph A. Cottam | 1 | 0 | 0.34 |
| Leslie M. Blaha | 2 | 43 | 6.51 |
| Kristin A. Cook | 3 | 236 | 17.03 |
| Mark Whiting | 4 | 61 | 6.02 |