Name
Abstract | ||
|---|---|---|
There have been efforts taken by different research projects to understand the complexity and the performance of a mobile broadband network. Various mobile network measurement platforms are proposed to collect performance metrics for analysis. Data integration would provide more thorough data analyses as well as prediction and decision models from one dataset to another. The crucial part of the data integration is to find out, whether two datasets have corresponding features (performance metrics). However, finding common features across datasets is a challenging task. For example, features might: 1) have similar names but be different metrics, 2) have different names but be similar metrics, or 3) be same metrics but have differences in the underlying methodology.We designed a feature mapping methodology between two crowdsourced LTE measurement-based datasets. Our method is based on correlations between the features and the mapping algorithm is solving a maximum constraint satisfaction problem (CSP). We define our constraints as inequality patterns between the correlation coefficients of the measured features. Our results show that the method maps measurement features based on their correlation coefficients with high confidence scores (between 0.78 to 1.0 depending on the amount of features). We observe that mapping score increases as a function of the amount of features. Altogether, our results show that this methodology can be used as an automated tool in the measurement data integration. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PIMRC.2018.8580999 | 2018 IEEE 29TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC) |
Field | DocType | Citations |
Data integration,Data mining,Feature mapping,Computer science,Real-time computing,Constraint satisfaction problem,Correlation,Decision model,Cellular network,Mapping algorithm,Mobile broadband | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kasper Apajalahti | 1 | 0 | 0.34 |
| Ermias Andargie Walelgne | 2 | 3 | 2.42 |
| Jukka Manner | 3 | 290 | 39.24 |
| Eero Hyvönen | 4 | 843 | 103.43 |