On Mining Group Patterns of Mobile Users

Wang, Yida; Lim, Ee-Peng; Hwang, San‐Yih

doi:10.1007/978-3-540-45227-0_29

Cited by 41 publications

(29 citation statements)

References 13 publications

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“…In the second category, group information could be obtained. Although these studies provide different types of group information services such as efficient location prediction method [2], data allocation schemes [3] and group purchase pattern [4], they do not provide group preference in diverse and multi-dimensional ways.…”

Section: Related Workmentioning

confidence: 99%

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Context awareness of social group by topic mining on visiting logs of mobile users in two dimensions

Jeong

Kim

et al. 2011

2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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Ubiquitous computing technologies have been developed fast and various decision support systems were proposed. Consecutively, in recently, people are working on an implicit service agent of mobile phone to make people to be provided useful services without paying attentions. However, when people want to buy some products or to go somewhere, people are still going to use Internet to get preference information of group of people. They should put queries and click one or more documents. In this sense, how the implicit services could be perfectly available by only mining on the contextual data obtainable from smart phones? In this paper, we propose a model providing group preference information implicitly through topic-mining on the data obtainable from smart phones. We would introduce developed context awareness model in smart phones and apply a topic model which automatically learns a hierarchy of topics to our server. We would also show our simple experimentation to ensure the effectiveness of topic-mining and of provided implicit group preference information.

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Section: Related Workmentioning

confidence: 99%

“…Several studies using location data [1,2,3,4] have focused on serving group preference based on collected log information. They are mainly divided into two categories according to what kind of result information is inferred from the location data.…”

Section: Related Workmentioning

confidence: 99%

Context awareness of social group by topic mining on visiting logs of mobile users in two dimensions

Jeong

Kim

et al. 2011

2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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“…This is because the idea of spatio-temporal co-occurrence as indicative of association of social nature has not been much explored. Group pattern mining (Wang et al, 2003) is the closest to this direction, arguing that people who are consistently moving together may belong to a group. However, its focus is less on constructing a network formed by pairwise ties than on finding groups of increasing cardinality.…”

Section: Mining Co-occurrencesmentioning

confidence: 99%

“…Spatio-temporal data is treated as a collection of time series of each item's wherebeing over time. Using time series similarity measures such as Euclidean (Wang et al, 2003) or LCSS (Vlachos et al, 2002) distance functions, the distance between two time series is evaluated. If it is below a certain threshold, the time series are considered similar enough, and the corresponding items are deemed to be co-occurring.…”

Section: Mining Co-occurrencesmentioning

confidence: 99%

Social Network Discovery by Mining Spatio-Temporal Events

Lauw

Lim

Pang

et al. 2005

Comput Math Organiz Theor

Self Cite

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Knowing patterns of relationship in a social network is very useful for law enforcement agencies to investigate collaborations among criminals, for businesses to exploit relationships to sell products, or for individuals who wish to network with others. After all, it is not just what you know, but also whom you know, that matters. However, finding out who is related to whom on a large scale is a complex problem. Asking every single individual would be impractical, given the huge number of individuals and the changing dynamics of relationships. Recent advancement in technology has allowed more data about activities of individuals to be collected. Such data may be mined to reveal associations between these individuals. Specifically, we focus on data having space and time elements, such as logs of people's movement over various locations or of their Internet activities at various cyber locations. Reasoning that individuals who are frequently found together are likely to be associated with each other, we mine from the data instances where several actors co-occur in space and time, presumably due to an underlying interaction. We call these spatio-temporal co-occurrences events, which we use to establish relationships between pairs of individuals. In this paper, we propose a model for constructing a social network from events, and provide an algorithm that mines these events from the data. Experiments on a real-life data tracking people's accesses to cyber locations have also yielded encouraging results.

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“…For the purpose of our simulation, we chose the Arena® Professional Edition (PE) software, that is one of the most common(about 13.000 installations worldwide) academic, general-purpose, discrete-time simulator capable analyze various types of queuing systems [24][25][26]. First released in 1993, Arena® employs an object-oriented design for entirely graphic model development.…”

Section: Arena Simulation Softwarementioning

confidence: 99%