Efficient mining and prediction of user behavior patterns in mobile web systems

Tseng, Vincent S.; Lin, Kun‐Wei

doi:10.1016/j.infsof.2005.12.014

Cited by 87 publications

(42 citation statements)

References 22 publications

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“…In this paper we are focusing in the temporal characteristic of the adaptation. Research efforts in prediction include a large variety of domains such as: online failure prediction [28,32], resource and demand prediction [2,16], and user behavior prediction [30], among others.…”

Section: Related Workmentioning

confidence: 99%

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A prediction-driven adaptation approach for self-adaptive sensor networks

Anaya

Šimko²,

Bourcier

et al. 2014

Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Engineering self-adaptive software in unpredictable environments such as pervasive systems, where network's ability, remaining battery power and environmental conditions may vary over the lifetime of the system is a very challenging task. Many current software engineering approaches leverage run-time architectural models to ease the design of the autonomic control loop of these self-adaptive systems. While these approaches perform well in reacting to various evolutions of the runtime environment, implementations based on reactive paradigms have a limited ability to anticipate problems, leading to transient unavailability of the system, useless costly adaptations, or resources waste. In this paper, we follow a proactive self-adaptation approach that aims at overcoming the limitation of reactive approaches. Based on predictive analysis of internal and external context information, our approach regulates new architecture reconfigurations and deploys them using models at runtime. We have evaluated our approach on a case study where we combined hourly temperature readings provided by National Climatic Data Center (NCDC) with fire reports from Moderate Resolution Imaging Spectroradiometer (MODIS) and simulated the behavior of multiple systems. The results confirm that our proactive approach outperforms a typical reactive system in scenarios with seasonal behavior.

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Section: Related Workmentioning

confidence: 99%

“…We differ from [30], as we are focusing on predicting environment behavior based on historical data, where is it easier to identify trend and seasonal components.…”

Section: Related Workmentioning

confidence: 99%

A prediction-driven adaptation approach for self-adaptive sensor networks

Anaya

Šimko²,

Bourcier

et al. 2014

Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

View full text Add to dashboard Cite

show abstract

“…Shani, Brafman and Heckerman (2005) view the recommendation process as a sequential decision problem and propose using Markov decision processes (a well-known stochastic technique for modeling sequential decisions) for generating recommendations [12]. Tseng and Lin (2006) use n-gram (another derivative from the Markov chain model) based sequential pattern mining techniques to mind the mobile user's web usage sequence, aligned with the location sequence where the user uses the web [15]. Although their objective is to predict the next user request and the next location, to reduce mobile web surfing latency, their work is strongly related the recommender system.…”

Section: Recommender System In Sequential Contextmentioning

confidence: 99%

“…Shani, Brafman and Heckerman (2005) and Tseng and Lin (2006) both seek to extract correlation between users by examining the sub-sequences of the users' action sequences in specifc problems [12] [15]. Their methods are both based on Markov chain models, more specifically the n-gram model, which is a type of probabilistic model for predicting the next item in a sequence.…”

Section: Sequential Routing Patternsmentioning

confidence: 99%

Recommend Touring Routes to Travelers According to Their Sequential Wandering Behaviours

Liu

Chang

2009

2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks

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“…Yavaş et al [12] proposed a three-phase model based on the Apriori algorithm to predict movementsidentification of movement patterns precedes the generation of rules, which are then utilized to predict movements. Tseng and Lin [10] introduced sequential mobile access patterns (SMAP) and presented a SMAP-Mine algorithm that can mine user movement patterns according to the contents of services requested by users. The SMAP-Tree data structure stores movement sequences, allowing all further mining tasks to be performed by the given SMAP-Tree.…”

Section: Related Workmentioning

confidence: 99%

Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

Wang

Cheng

2009

Appl Intell

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In m-commerce services, the periodic movement trends of customers at specific periods can be adopted to allocate the resources of telecommunications systems effectively and offer personalized location-based services. This study explores the mining of periodic maximal promising movement patterns. A detailed process for mining periodic maximal promising movement patterns based on graph mapping and sampling techniques is devised to enhance mining efficiency. First, a random sample of movement paths from time intervals is taken. Second, a unique path graph structure is built to store the movement paths obtained from the sample. Third, a graph traversal algorithm is developed to identify the maximal promising movement patterns. Finally, vector operations are undertaken to examine the maximal promising movement patterns in order to derive the periodic maximal promising movement patterns. Experimental results reveal that the sampling approach with mining has excellent execution efficiency and scalability in the investigation of periodic maximal promising movement patterns.

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Efficient mining and prediction of user behavior patterns in mobile web systems

Cited by 87 publications

References 22 publications

A prediction-driven adaptation approach for self-adaptive sensor networks

A prediction-driven adaptation approach for self-adaptive sensor networks

Recommend Touring Routes to Travelers According to Their Sequential Wandering Behaviours

Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

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