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Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior

Iqbal H Sarker, Alan Colman, Muhammad Ashad Kabir, Jun Han
2017 Computer journal  
Currently, researchers use either equal or unequal interval-based segmentation of time for mining mobile phone users' behavior.  ...  of high confidence temporal rules in order to mine individual mobile phone users' behavior.  ...  RELATED WORK In recent years, variety of time series segmentations for mining mobile phone user behavior have been used in various purposes.  ... 
doi:10.1093/comjnl/bxx082 fatcat:jxxe62cuvvd57nmidizgrckevq

Context-aware rule learning from smartphone data: survey, challenges and future directions

Iqbal H. Sarker
2019 Journal of Big Data  
Thus, time-series segmentation becomes one of the research focuses in this study as exact time in mobile phone data is not very informative to mine behavioral rules of individual mobile phone users.  ...  Such logs can be used for mining the contextual behavioral patterns of individual mobile phone users that is, which app is preferred by a particular user under a certain context to provide personalized  ... 
doi:10.1186/s40537-019-0258-4 fatcat:hnbskalstzaexaf3fptvxf6qey

Research Issues in Mining User Behavioral Rules for Context-Aware Intelligent Mobile Applications [article]

Iqbal H. Sarker
2018 arXiv   pre-print
In order to build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key.  ...  In this paper, we briefly discuss these issues and their potential solution directions for mining individuals' behavioral rules, for the purpose of building various context-aware intelligent mobile applications  ...  Ashad Kabir, Charles Sturt University, Australia, for their relevant discussions.  ... 
arXiv:1810.12692v1 fatcat:uw6uhqlc3fbg3kypzsgnmrtyo4

Research issues in mining user behavioral rules for context-aware intelligent mobile applications

Iqbal H. Sarker
2018 Iran Journal of Computer Science  
To build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key.  ...  In this paper, we briefly discuss these issues and their potential solution directions for mining individuals' behavioral rules, for the purpose of building various context-aware intelligent mobile applications  ...  Ashad Kabir, Charles Sturt University, Australia, for their relevant discussions.  ... 
doi:10.1007/s42044-018-0026-1 fatcat:wmfisres2rgl5csg5f6ms4hwpy

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

Iqbal H. Sarker, Alan Colman, Jun Han
2019 Journal of Big Data  
However, an individual's phone usage behavior may not be static in the real-world changing over time. The volatility of usage behavior will also vary from user-to-user.  ...  Behavioral patterns of smartphone users may vary greatly between individuals in different contexts-for example, temporal, spatial, or social contexts.  ...  Ashad Kabir, Charles Sturt University, Australia for his relevant support.  ... 
doi:10.1186/s40537-019-0211-6 fatcat:dqbhlaesjbbdxaub5k5vwm7c6m

A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data [article]

Iqbal H. Sarker
2019 arXiv   pre-print
In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model.  ...  Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure  ...  Ashad Kabir, Charles Sturt University, Australia for their relevant discussions.  ... 
arXiv:1902.07588v1 fatcat:oylakibpcnad7brktskheytcye

Identifying Recent Behavioral Data Length in Mobile Phone Log [article]

Iqbal H. Sarker, Muhammad Ashad Kabir, Alan Colman, Jun Han
2017 arXiv   pre-print
Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individ- ual'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 in- dividual's behavior.  ...  We also pre-process the time-series data in mobile phone log as it is continuous and numeric. For this, we use BOTS technique [4] for producing behavior-oriented time segments.  ... 
arXiv:1711.06837v2 fatcat:pe6t33rfyjfpjnyc77w3xjsyky

E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management

Iqbal H. Sarker, A. S. M. Kayes, Md Hasan Furhad, Mohammad Mainul Islam, Md Shohidul Islam
2019 AI & Society: The Journal of Human-Centred Systems and Machine Intelligence  
Such interruptions may impact on the work attention not only for the mobile phone owners but also the surrounding people.  ...  The experimental results on individuals' real life mobile telephony datasets show that our E-MIIM model is more effective and outperforms existing MIIM model for predicting and managing individual's mobile  ...  To use such time-series temporal information in modeling users' mobile telephony behavior, we use our earlier behavior-oriented time-series segmentation technique BOTS [32] for converting into nominal  ... 
doi:10.1007/s00146-019-00898-8 fatcat:uz65wwro5nehxhbrmzjenmkwui

Mining User Behavioral Rules from Smartphone Data through Association Analysis [article]

Iqbal H. Sarker, Flora D. Salim
2018 arXiv   pre-print
This paper formulates the problem of mining behavioral association rules of individual mobile phone users utilizing their smartphone data.  ...  The effectiveness of the proposed approach is examined by considering the real mobile phone datasets of individual users.  ...  model phone call behavior of individual mobile phone users.  ... 
arXiv:1804.01379v1 fatcat:t2ghxvjeofd2pbab6lpqfnrgry

Visual Analysis of E-Commerce User Behavior Based on Log Mining

Tingzhong Wang, Nanjie Li, Hailong Wang, Junhong Xian, Jiayi Guo, Qiangyi Li
2022 Advances in Multimedia  
Mining the behavior of individual users and group users from massive user behavior data and analyzing the value and law behind the data are of great significance to the development of e-commerce.  ...  Through the effective mining and multidimensional visual analysis of user behavior data, the behavior analysis of group users and individual users, as well as the analysis of commodity sales flow and sales  ...  For mobile phone brands with low payment conversion rate, the reason may be that the e-commerce platform has poor compatibility with this type of mobile phone, and poor user experience can be used as a  ... 
doi:10.1155/2022/4291978 fatcat:x24i7iswpzd4ldrcxprfingpcy

Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data [chapter]

Yihong Yuan, Martin Raubal
2012 Lecture Notes in Computer Science  
A Dynamic Time Warping (DTW) algorithm is applied to measure the similarity between these time series, which also provides input for classifying different urban areas based on their mobility patterns.  ...  In this paper, we aim to go one step further from identifying aggregated mobility patterns. Using hourly time series we extract and represent the dynamic mobility patterns in different urban areas.  ...  Moreover, DTW can also be used to examine individual mobility patterns of phone users (i.e., characterizing user trajectories based on the abnormality of visited areas).  ... 
doi:10.1007/978-3-642-33024-7_26 fatcat:4r4xgn6ibraebgul6csjhfsjzu

Profiling presence patterns and segmenting user locations from cell phone data [article]

Yan Leng, Haris Koutsopoulos, Jinhua Zhao
2020 arXiv   pre-print
person will appear in that location for a given the time of day.  ...  Starting with the historical data of geo-temporal travel patterns for a panel of individuals, we create, for each person-location, a vector of probability distribution capturing the likelihood that the  ...  To characterize the temporal presence patterns of individuals at the user locations and condense the time series data in a structured way, we propose a new feature: Normalized Hourly Presence (NHP).  ... 
arXiv:1805.12208v2 fatcat:e57hgg5nivdc7bsryzxbzdobbu

Urban Sensing Using Mobile Phone Network Data: A Survey of Research

Francesco Calabrese, Laura Ferrari, Vincent D. Blondel
2014 ACM Computing Surveys  
In particular, mobile phone datasets offer access to insights into urban dynamics and human activities at an unprecedented scale and level of details, representing a huge opportunity for research and real  ...  We strongly believe the material and recommendations presented here to become increasingly important as mobile phone network datasets are becoming more accessible to the research community.  ...  For example, in the authors try to estimate the bias of user behavior in mobile phone data taking into account the imprecision of data, with a trigonometric approach to describe both mobility values and  ... 
doi:10.1145/2655691 fatcat:bctetyuz5rdehln4uieeb5nbf4

Workshop Synthesis: System Based Passive Data Streams Systems; Smart Cards, Phone Data, GPS

Martin Trépanier, Toshiyuki Yamamoto
2015 Transportation Research Procedia  
Each day, huge quantities of passive data streams are collected by smart card, GPS, Bluetooth and mobile phone systems all over the world.  ...  Many issues are discussed here: definitions, data collection and processing, privacy, how to use these data for transport planning, how to integrate these data with traditional and more "active" data sources  ...  For GPS, mobile phone and Bluetooth traces, the trip mode can be derived using a series of logic related to travel speed, location and readings from accelerometers.  ... 
doi:10.1016/j.trpro.2015.12.029 fatcat:jzj3y6tdbjcmhcewuljophkboy

Mobile Expert System: Exploring Context-Aware Machine Learning Rules for Personalized Decision-Making in Mobile Applications

Iqbal H. Sarker, Asif Irshad Khan, Yoosef B. Abushark, Fawaz Alsolami
2021 Symmetry  
Our experiment section shows that the context-aware machine learning rules discovered from users' mobile phone data can contribute in building a mobile expert system to solve a particular problem, through  ...  The reason is that different mobile users may behave differently in various day-to-day situations, i.e., not identical, and thus the rules for personalized services must be reflected according to their  ...  To generate rules from the phone call datasets, we have used our earlier behavior-oriented time segmentation (BOTS) technique [40] to pre-process the raw time-series data to create dynamic time segments  ... 
doi:10.3390/sym13101975 fatcat:n24rycmrhzc5blkoxqnma4ikim
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