Abstract
Pervasiveness of mobile phones and the fact that the phones have sensors make them ideal as personal sensors. Smart phones are equipped with a wide range of motion, location and environment sensors, that allow us to analyze, model
and predict mobility in urban areas. Raw sensory data is being collected as time-stamped sequences of records, and this data needs to be preprocessed and aggregated before any predictive modeling can be done. This paper presents a
case study in preprocessing such data, collected by one person over six months period. Our goal with this exploratory pilot study is to discuss data aggregation challenges from machine learning point of view, and identify relevant directions
for future research in preprocessing mobile sensing data for human mobility analysis.
and predict mobility in urban areas. Raw sensory data is being collected as time-stamped sequences of records, and this data needs to be preprocessed and aggregated before any predictive modeling can be done. This paper presents a
case study in preprocessing such data, collected by one person over six months period. Our goal with this exploratory pilot study is to discuss data aggregation challenges from machine learning point of view, and identify relevant directions
for future research in preprocessing mobile sensing data for human mobility analysis.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014 |
| Publisher | CEUR |
| Pages | 309-314 |
| Publication status | Published - 2014 |
| MoE publication type | A4 Article in a conference publication |
Publication series
| Name | Ceur Workshop Proceedings |
|---|---|
| Publisher | CEUR |
| Volume | 1133 |
| ISSN (Print) | 1613-0073 |
| ISSN (Electronic) | 1613-0073 |