Mining temporal mobile sequential patterns in location-based service environments

Tseng, Vincent S.; Lu, Erl-Huei; Huang, Cheng-Hsien

doi:10.1109/icpads.2007.4447725

Cited by 21 publications

(6 citation statements)

References 13 publications

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“…Hwang et al [8] propose a method to get the group of similar users by the movement pattern mined based on the user's trajectory. Tseng et al [9] propose a mining algorithm for instantaneous moving sequence patterns, which is based on the user's mobile path and time interval. Fang et al [10] propose a binary mining algorithm mining the association rules of the spatial location based on spatial database.…”

Section: Related Workmentioning

confidence: 99%

Periodic Pattern Mining Based on GPS Trajectories

Chen¹,

Shi²,

Zhao³

et al. 2016

Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering

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With the rise of LBS (Location Based Service), lots of user recommendation system based on location trajectory analysis has emerged. Finding periodic behavior is essential to analysis user's activity. In order to solve the problems with trajectory outliers and human intervention in periodic parameters, we propose a three-stage framework called PPM (Periodic Pattern Mining) to detect periodic pattern based on people's trajectory. First of all, we preprocess the trajectory data to extract stay points. Secondly, the sequence of stay points are clustered to construct the important places. At the last stage, the movement sequence is transformed into a binary sequence. Then period of every binary sequence is detected by a probabilistic model. The experiment based on public mobile dataset shows that the proposed method can be used to mine people's periodic activity pattern effectively.

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Section: Related Workmentioning

confidence: 99%

Periodic Pattern Mining Based on GPS Trajectories

Chen¹,

Shi²,

Zhao³

et al. 2016

Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering

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“…It was only until recently that both spatio and temporal relationships are considered together in mining sequential patterns [6], [23], [24]. Li and Li take a step further to combine movement and access pattern analysis for better services in cellular systems [7].…”

Section: Related Workmentioning

confidence: 99%

Activity-Based Proactive Data Management in Mobile Environments

Fan

2010

IEEE Trans. on Mobile Comput.

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Most users in a mobile environment are moving and accessing wireless services for the activities they are currently engaged in. We propose the idea of complex activity for characterizing the continuously changing complex behavior patterns of mobile users. For the purpose of data management, a complex activity is modeled as a sequence of location movement, service requests, the cooccurrence of location and service, or the interleaving of all above. An activity may be composed of subactivities. Different activities may exhibit dependencies that affect user behaviors. We argue that the complex activity concept provides a more precise, rich, and detail description of user behavioral patterns which are invaluable for data management in mobile environments. Proper exploration of user activities has the potential of providing much higher quality and personalized services to individual user at the right place on the right time. We, therefore, propose new methods for complex activity mining, incremental maintenance, online detection and proactive data management based on user activities. In particular, we devise prefetching and pushing techniques with cost-sensitive control to facilitate predictive data allocation. Preliminary implementation and simulation results demonstrate that the proposed framework and techniques can significantly increase local availability, conserve execution cost, reduce response time, and improve cache utilization.

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“…The potential events indicate the preferred service sequences which users frequently request for each user group. Each user may request services by adhering to potential events with probability P E or randomly [30], [31].…”

Section: Simulation Modelmentioning

confidence: 99%

Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments

Tseng

2011

IEEE Trans. Knowl. Data Eng.

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Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. In this paper, we propose a novel algorithm, namely, Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP-Mine, user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location-Based Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent performance.

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Mining temporal mobile sequential patterns in location-based service environments

Cited by 21 publications

References 13 publications

Periodic Pattern Mining Based on GPS Trajectories

Periodic Pattern Mining Based on GPS Trajectories

Activity-Based Proactive Data Management in Mobile Environments

Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments

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