Correlation-Based Feature Mapping of Crowdsourced LTE Data

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorApajalahti, Kasperen_US
dc.contributor.authorWalelgne, Ermiasen_US
dc.contributor.authorManner, Jukkaen_US
dc.contributor.authorHyvönen, Eeroen_US
dc.contributor.departmentProfessorship Hyvönen Eeroen_US
dc.contributor.departmentDepartment of Communications and Networkingen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.date.accessioned2019-02-25T08:41:15Z
dc.date.available2019-02-25T08:41:15Z
dc.date.issued2018en_US
dc.description| openaire: EC/FP7/607728/EU//METRICS
dc.description.abstractThere 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.en
dc.description.versionPeer revieweden
dc.format.extent7
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationApajalahti , K , Walelgne , E , Manner , J & Hyvönen , E 2018 , Correlation-Based Feature Mapping of Crowdsourced LTE Data . in IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications Workshops . IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops , IEEE , IEEE International Symposium on Personal, Indoor and Mobile Radio Communications , Bologna , Italy , 09/09/2018 . https://doi.org/10.1109/PIMRC.2018.8580999en
dc.identifier.doi10.1109/PIMRC.2018.8580999en_US
dc.identifier.isbn978-1-5386-6009-6
dc.identifier.issn2166-9570
dc.identifier.issn2166-9589
dc.identifier.otherPURE UUID: 1657a046-82c1-41cf-9131-cc53967b3b8ben_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/1657a046-82c1-41cf-9131-cc53967b3b8ben_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/31971289/ELEC_apajalahti_et_al_correlation_based.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/36656
dc.identifier.urnURN:NBN:fi:aalto-201902251813
dc.language.isoenen
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/607728/EU//METRICSen_US
dc.relation.ispartofIEEE International Symposium on Personal, Indoor, and Mobile Radio Communicationsen
dc.relation.ispartofseriesIEEE International Symposium on Personal, Indoor, and Mobile Radio Communications Workshopsen
dc.relation.ispartofseriesIEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshopsen
dc.rightsopenAccessen
dc.titleCorrelation-Based Feature Mapping of Crowdsourced LTE Dataen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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