35 research outputs found
DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories
Since semantic trajectories can discover more semantic meanings of a user\u27s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
New research cutting across architecture, urban studies, and psychology is
contextualizing the understanding of urban spaces according to the perceptions
of their inhabitants. One fundamental construct that relates place and
experience is ambiance, which is defined as "the mood or feeling associated
with a particular place". We posit that the systematic study of ambiance
dimensions in cities is a new domain for which multimedia research can make
pivotal contributions. We present a study to examine how images collected from
social media can be used for the crowdsourced characterization of indoor
ambiance impressions in popular urban places. We design a crowdsourcing
framework to understand suitability of social images as data source to convey
place ambiance, to examine what type of images are most suitable to describe
ambiance, and to assess how people perceive places socially from the
perspective of ambiance along 13 dimensions. Our study is based on 50,000
Foursquare images collected from 300 popular places across six cities
worldwide. The results show that reliable estimates of ambiance can be obtained
for several of the dimensions. Furthermore, we found that most aggregate
impressions of ambiance are similar across popular places in all studied
cities. We conclude by presenting a multidisciplinary research agenda for
future research in this domain
Location Prediction: Communities Speak Louder than Friends
Humans are social animals, they interact with different communities of
friends to conduct different activities. The literature shows that human
mobility is constrained by their social relations. In this paper, we
investigate the social impact of a person's communities on his mobility,
instead of all friends from his online social networks. This study can be
particularly useful, as certain social behaviors are influenced by specific
communities but not all friends. To achieve our goal, we first develop a
measure to characterize a person's social diversity, which we term `community
entropy'. Through analysis of two real-life datasets, we demonstrate that a
person's mobility is influenced only by a small fraction of his communities and
the influence depends on the social contexts of the communities. We then
exploit machine learning techniques to predict users' future movement based on
their communities' information. Extensive experiments demonstrate the
prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201
Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory
This paper attempts to mine the hidden individual behavior pattern from the raw users’ trajectory data. Based on DBSCAN, a novel spatio-temporal data clustering algorithm named Speed-based Clustering Algorithm was put forward to find slow-speed subtrajectories (i.e., stops) of the single trajectory that the user stopped for a longer time. The algorithm used maximal speed and minimal stopping time to compute the stops and introduced the quantile function to estimate the value of the parameter, which showed more effectively and accurately than DBSCAN and certain improved DBSCAN algorithms in the experimental results. In addition, after the stops are connected with POIs that have the characteristic of an information presentation, the paper designed a POI-Behavior Mapping Table to analyze the user’s activities according to the stopping time and visiting frequency, on the basis of which the user’s daily regular behavior pattern can be mined from the history trajectories. In the end, LBS operators are able to provide intelligent and personalized services so as to achieve precise marketing in terms of the characteristics of the individual behavior.</p
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Ministry of Education, Singapore under its Academic Research Funding Tier