Name
Abstract | ||
|---|---|---|
The increasing reach of the Internet of Things (IoT) is leading to a world rich in sensors [3] that can be used to support physical analytics -- analogous to web analytics but targeted at user interactions with physical devices in the real-world (e.g. [2]). In contrast to web analytics, physical analytics systems typically only provide data relating to sensors and objects without consideration of individual users. This is mainly a consequence of an inability to track individual mobile user interactions across multiple physical objects (or across sessions of interaction with a single object) using, for example, an analogue of a web cookie. Indeed, such a \"physical analytics cookie\" could raise significant privacy concerns. However, in many cases a more \"human-centric\" approach to analytics would enable us to provide new and interesting insights into interactions between mobile users and the physical world [1]. In our work we endeavour to leverage synthetic user traces of human mobility, and data from real IoT systems, to provide such insights. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2938559.2938607 | MobiSys (Companion Volume) |
Field | DocType | Citations |
World Wide Web,Software analytics,Web analytics,Computer science,Internet of Things,Analytics | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mateusz Mikusz | 1 | 33 | 9.84 |
| Oliver Bates | 2 | 120 | 19.09 |
| Sarah Clinch | 3 | 10 | 1.79 |
| Nigel Davies | 4 | 6143 | 560.89 |
| Adrian Friday | 5 | 2437 | 278.52 |
| Anastasios Noulas | 6 | 1162 | 53.35 |