Identifying recent behavioral data length in mobile phone log

Iqbal H. Sarker; Muhammad Ashad Kabir; Alan Colman; Jun Han

doi:10.4108/eai.7-11-2017.2275019

Identifying recent behavioral data length in mobile phone log

Iqbal H. Sarker, Muhammad Ashad Kabir, Alan Colman, Jun Han

Swinburne University of Technology

Research output: Book chapter/Published conference paper › Conference paper › peer-review

Abstract

Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individual’s diverse behaviors with mobile phones. Since human behavior changes over time, the most recent pattern is more interesting and signiﬁcant than older ones for predicting individual’s behavior. The goal of this poster paper is to identify the recent behavioral data length dynamically from the entire phone log for recency-based behavior modeling. To the best of our knowledge, this is the ﬁrst dynamic recent log-based study that takes into account individual’s recent behavioral patterns for modeling their phone call behaviors.

Original language	English
Title of host publication	Proceedings of the 14th EAI international conference on mobile and ubiquitous systems
Subtitle of host publication	computing, networking and services (MobiQuitous 2017)
Editors	Tao Gu, Ramamohanarao Kotagiri, Dali Kaafar, Gang Zhou
Place of Publication	United States
Publisher	Association for Computing Machinery (ACM)
Pages	545-546
Number of pages	2
ISBN (Electronic)	9781450353687
DOIs	https://doi.org/10.4108/eai.7-11-2017.2275019
Publication status	Published - 18 Apr 2018
Event	MobiQuitous 2017 - 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services - RMIT University Storey Hall, Melbourne, Australia Duration: 07 Nov 2017 → 10 Nov 2017 https://mobiquitous.eai-conferences.org/2017/ (Conference website)

Conference

Conference	MobiQuitous 2017 - 14th EAI International Conference on Mobile and Ubiquitous Systems
Country/Territory	Australia
City	Melbourne
Period	07/11/17 → 10/11/17
Other	The fourteenth Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017) aims to provide a forum for presenting and discussing top-notch quality research. MobiQuitous 2017 fosters research collaboration through focused discussions, interaction and exchange of experiences that will designate future research efforts and open new leading directions for innovation and cutting-edge technologies. Areas addressed by the conference include the intersection of mobile and ubiquitous computing systems with social networks, Internet of things, middleware, networking, data management and services, domain-specific applications, user behaviour, models and algorithms.
Internet address	https://mobiquitous.eai-conferences.org/2017/ (Conference website)

Access to Document

10.4108/eai.7-11-2017.2275019

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Sarker, I. H., Kabir, M. A., Colman, A., & Han, J. (2018). Identifying recent behavioral data length in mobile phone log. In T. Gu, R. Kotagiri, D. Kaafar, & G. Zhou (Eds.), Proceedings of the 14th EAI international conference on mobile and ubiquitous systems: computing, networking and services (MobiQuitous 2017) (pp. 545-546). Association for Computing Machinery (ACM). https://doi.org/10.4108/eai.7-11-2017.2275019

Sarker, Iqbal H. ; Kabir, Muhammad Ashad ; Colman, Alan et al. / Identifying recent behavioral data length in mobile phone log. Proceedings of the 14th EAI international conference on mobile and ubiquitous systems: computing, networking and services (MobiQuitous 2017). editor / Tao Gu ; Ramamohanarao Kotagiri ; Dali Kaafar ; Gang Zhou. United States : Association for Computing Machinery (ACM), 2018. pp. 545-546

@inproceedings{59105ab951e24a5cbcffb4f22198f197,

title = "Identifying recent behavioral data length in mobile phone log",

abstract = "Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individual{\textquoteright}s diverse behaviors with mobile phones. Since human behavior changes over time, the most recent pattern is more interesting and signiﬁcant than older ones for predicting individual{\textquoteright}s behavior. The goal of this poster paper is to identify the recent behavioral data length dynamically from the entire phone log for recency-based behavior modeling. To the best of our knowledge, this is the ﬁrst dynamic recent log-based study that takes into account individual{\textquoteright}s recent behavioral patterns for modeling their phone call behaviors. ",

keywords = "Mobile data mining, Behavior modeling, Contexts, Recency",

author = "Sarker, {Iqbal H.} and Kabir, {Muhammad Ashad} and Alan Colman and Jun Han",

note = "Includes bibliographical references.; MobiQuitous 2017 - 14th EAI International Conference on Mobile and Ubiquitous Systems : Computing, Networking and Services ; Conference date: 07-11-2017 Through 10-11-2017",

year = "2018",

month = apr,

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doi = "10.4108/eai.7-11-2017.2275019",

language = "English",

pages = "545--546",

editor = "Tao Gu and Ramamohanarao Kotagiri and Dali Kaafar and Gang Zhou",

booktitle = "Proceedings of the 14th EAI international conference on mobile and ubiquitous systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

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}

Sarker, IH, Kabir, MA, Colman, A & Han, J 2018, Identifying recent behavioral data length in mobile phone log. in T Gu, R Kotagiri, D Kaafar & G Zhou (eds), Proceedings of the 14th EAI international conference on mobile and ubiquitous systems: computing, networking and services (MobiQuitous 2017). Association for Computing Machinery (ACM), United States, pp. 545-546, MobiQuitous 2017 - 14th EAI International Conference on Mobile and Ubiquitous Systems, Melbourne, Victoria, Australia, 07/11/17. https://doi.org/10.4108/eai.7-11-2017.2275019

Identifying recent behavioral data length in mobile phone log. / Sarker, Iqbal H.; Kabir, Muhammad Ashad; Colman, Alan et al.
Proceedings of the 14th EAI international conference on mobile and ubiquitous systems: computing, networking and services (MobiQuitous 2017). ed. / Tao Gu; Ramamohanarao Kotagiri; Dali Kaafar; Gang Zhou. United States: Association for Computing Machinery (ACM), 2018. p. 545-546.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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AU - Colman, Alan

AU - Han, Jun

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PY - 2018/4/18

Y1 - 2018/4/18

N2 - Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individual’s diverse behaviors with mobile phones. Since human behavior changes over time, the most recent pattern is more interesting and signiﬁcant than older ones for predicting individual’s behavior. The goal of this poster paper is to identify the recent behavioral data length dynamically from the entire phone log for recency-based behavior modeling. To the best of our knowledge, this is the ﬁrst dynamic recent log-based study that takes into account individual’s recent behavioral patterns for modeling their phone call behaviors.

AB - Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individual’s diverse behaviors with mobile phones. Since human behavior changes over time, the most recent pattern is more interesting and signiﬁcant than older ones for predicting individual’s behavior. The goal of this poster paper is to identify the recent behavioral data length dynamically from the entire phone log for recency-based behavior modeling. To the best of our knowledge, this is the ﬁrst dynamic recent log-based study that takes into account individual’s recent behavioral patterns for modeling their phone call behaviors.

KW - Mobile data mining

KW - Behavior modeling

KW - Contexts

KW - Recency

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DO - 10.4108/eai.7-11-2017.2275019

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BT - Proceedings of the 14th EAI international conference on mobile and ubiquitous systems

A2 - Gu, Tao

A2 - Kotagiri, Ramamohanarao

A2 - Kaafar, Dali

A2 - Zhou, Gang

PB - Association for Computing Machinery (ACM)

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Sarker IH, Kabir MA, Colman A, Han J. Identifying recent behavioral data length in mobile phone log. In Gu T, Kotagiri R, Kaafar D, Zhou G, editors, Proceedings of the 14th EAI international conference on mobile and ubiquitous systems: computing, networking and services (MobiQuitous 2017). United States: Association for Computing Machinery (ACM). 2018. p. 545-546 Epub 2017. doi: 10.4108/eai.7-11-2017.2275019

Identifying recent behavioral data length in mobile phone log

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