Mining trajectory profiles for discovering user communities

Hung, Chih‐Chieh; Chang, Chih‐Wen; Peng, Wen-Chih

doi:10.1145/1629890.1629892

Cited by 36 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Profiles in this work are computed for car pooling. Similarly, [6] defines object profile as a sequence of regions frequently visited by the object, and those with similar visits are clustered to infer communities of people. Both previous works focus on raw trajectories, where the object history is a set of space-time points, while we focus on semantic trajectories.…”

Section: Related Workmentioning

confidence: 99%

A Rule-based Method for Discovering Trajectory Profiles

Alencar

Älvares

Renso

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Abstract-The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people's profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery.

show abstract

Section: Related Workmentioning

confidence: 99%

A Rule-based Method for Discovering Trajectory Profiles

Alencar

Älvares

Renso

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…In order to improve application awareness during trajectory data analysis, Alvares et al [2] proposed adding semantic information during trajectory preprocessing. Hung et al [20] proposed the complementary approach of using a probabilistic suffix tree to measure separation among users trajectories. Xie et al [32] addressed the problem of predicting social activities based on users' trajectories.…”

Section: Row-merge Algorithm Versus Mdlp Algorithmmentioning

confidence: 99%

Efficient location aware intrusion detection to protect mobile devices

Yazji

Scheuermann

Dick

et al. 2013

Pers Ubiquit Comput

View full text Add to dashboard Cite

This paper addresses the problem of efficient intrusion detection for mobile devices via correlating the user's location and time data. We developed two statistical profiling approaches for modeling the normal spatio-temporal behavior of the users: one based on an empirical cumulative probability measure and the other based on the Markov properties of trajectories. An anomaly is detected when the probability of a particular (location, time) evolution matching the normal behavior of a given user becomes lower than a certain threshold, determined by controlling the recall rate of the model of the normal user's behavior. We used compression techniques to reduce processing overhead while maintaining high accuracy. Our evaluation based on the Reality Mining and Geolife data sets shows that the proposed system is capable of detecting a potential intrusion within 15 min and with 94 % accuracy.

show abstract

“…When the denominator is a week, sometimes we specify the exact days during the week when the event occurs. For instance, [2,3,4,5,6] as a numerator when the denominator is week, refers to the workdays. Formally, a time pattern has the form T P = (num, denum, st, et) where num is the numerator, denom is the denominator, st is the start time and et is the end time during the day.…”

Section: Frameworkmentioning

confidence: 99%

“…Hung et al [6] showed how to categorize people and create communities based on the routes of people. Jeung et al [7] presented methods for discovering people who travel in a convoy, from their location tracing.…”

Section: Related Workmentioning

confidence: 99%

Storing routes in socio-spatial networks and supporting social-based route recommendation

Doytsher

Galon

Kanza

2011

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks

View full text Add to dashboard Cite

Cellular phones and GPS-based navigation systems allow recording the location history of users, to find places the users frequently visit and routes along which the users frequently travel. This provides associations between users and geographic entities. Considering these associations as edges that connect users of a social network to geographical entities on a spatial network yields an integrated socio-spatial network. Queries over a socio-spatial network glean information on users, in correspondence with their location history, and retrieve geographical entities in association with the users who frequently visit these entities.In this paper we present a graph model for socio-spatial networks that store information on frequently traveled routes. We present a query language that consists of graph traversal operations, aiming at facilitating the formulation of queries, and we show how queries over the network can be evaluated efficiently. We also show how social-based route recommendation can be implemented using our query language. We describe an implementation of the suggested model over a graph-based database system and provide an experimental evaluation, to illustrate the effectiveness of our model.

show abstract

Mining trajectory profiles for discovering user communities

Cited by 36 publications

References 14 publications

A Rule-based Method for Discovering Trajectory Profiles

A Rule-based Method for Discovering Trajectory Profiles

Efficient location aware intrusion detection to protect mobile devices

Storing routes in socio-spatial networks and supporting social-based route recommendation

Contact Info

Product

Resources

About