Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

Mert Ozer, Ilkcan Keles, Hakki Toroslu, Pinar Karagoz, Hasan Davulcu

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

9 Citationer (Scopus)

Resumé

In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user’s location changes. We have developed sequential pattern mining-based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets.
OriginalsprogEngelsk
TidsskriftComputer Journal
Vol/bind59
Udgave nummer6
Sider (fra-til)908-922
ISSN0010-4620
DOI
StatusUdgivet - 16 jun. 2016

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Ozer, Mert ; Keles, Ilkcan ; Toroslu, Hakki ; Karagoz, Pinar ; Davulcu, Hasan. / Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques. I: Computer Journal. 2016 ; Bind 59, Nr. 6. s. 908-922.
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abstract = "In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user’s location changes. We have developed sequential pattern mining-based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets.",
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Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques. / Ozer, Mert; Keles, Ilkcan; Toroslu, Hakki; Karagoz, Pinar; Davulcu, Hasan.

I: Computer Journal, Bind 59, Nr. 6, 16.06.2016, s. 908-922.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

AU - Ozer, Mert

AU - Keles, Ilkcan

AU - Toroslu, Hakki

AU - Karagoz, Pinar

AU - Davulcu, Hasan

PY - 2016/6/16

Y1 - 2016/6/16

N2 - In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user’s location changes. We have developed sequential pattern mining-based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets.

AB - In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user’s location changes. We have developed sequential pattern mining-based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets.

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KW - mobile phone user

KW - sequence mining

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