Semantic trajectory mining for location prediction

Ying, Josh Jia-Ching; Lee, Wang-Chien; Weng, Tz-Chiao; Tseng, Vincent S.

doi:10.1145/2093973.2093980

Cited by 249 publications

(118 citation statements)

References 15 publications

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“…Cao et al (2010) proposed a framework to discover significant and semantically meaningful locations. Ying et al (2011) proposed a method, which is based on clustering approaches, to predict next location of a user by considering geographical and semantic features. However, most of these studies are based on GPS data and these studies do not take into account social group preferences and they do not focus on developing computationally efficient algorithms.…”

Section: Related Workmentioning

confidence: 99%

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

Dokuz

Çelik

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20%.

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Section: Related Workmentioning

confidence: 99%

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

Dokuz

Çelik

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In [19], Ying et al propose an approach for predicting next location of an individual based on both geographic and semantic features of her trajectories. The predicting technique leverages clusters of similar users to predict a user's next location.…”

Section: Related Workmentioning

confidence: 99%

Discovering and predicting user routines by differential analysis of social network traces

Pianese¹,

An²,

Kawsar³

et al. 2013

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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Abstract-The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their location and the venues they visit. In this paper, we present automated techniques for filtering, aggregating, and processing combined social networking traces with the goal of extracting descriptions of regularly-occurring user activities, which we refer to as "user routines". We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful venue information about the locations we observe. We analyze and combine the two datasets to highlight their properties and show how the emergent features can enhance our understanding of users' daily schedule. Finally, we evaluate and discuss the potential of routine descriptions for predicting future user activity and location.

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“…HPM relies on frequent patterns discovered from previous trajectories as well as existing motion functions using the object's recent movements to support future location queries. As low-sampling-rate trajectories can be easily accumulated from location-based services and social services, studying low-sampling-rate trajectories becomes important [14][15][13] [18]. To the best of our knowledge, no attention has been paid to answer distant-time location queries in low-sampling-rate trajectory databases.…”

Section: B Location Predictionmentioning

confidence: 99%

Distant-Time Location Prediction in Low-Sampling-Rate Trajectories

Chiang

Zhu

Peng

et al. 2013

2013 IEEE 14th International Conference on Mobile Data Management

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Abstract-With the growth of location-based services and social services, low-sampling-rate trajectories from check-in data or photos with geo-tag information becomes ubiquitous. In general, most detailed moving information in low-sampling-rate trajectories are lost. Prior works have elaborated on distant-time location prediction in high-sampling-rate trajectories. However, existing prediction models are pattern-based and thus not applicable due to the sparsity of data points in low-sampling-rate trajectories. For example, it becomes difficult to derive trajectory patterns, let alone utilizing trajectory patterns for distant-time location prediction. In this paper, given a query time, the current location and time, we aim to predict the location of an object at the query time. To address the sparsity in low-sampling-rate trajectories, we develop a Reachability-based prediction model on Time-constrained Mobility Graph (abbreviated as RTMG) to predict locations for distant-time queries. Specifically, we design an adaptive temporal exploration approach to extract effective supporting trajectories that are temporally close to the query time. These data points are then represented as a Timeconstrained user mobility Graph (refers to as TG). In light of TG, we further derive the reachability probabilities among locations in TG. Thus, a location with maximum reachability from the current location among all possible locations in supporting trajectories is considered as the prediction result. To efficiently process queries, we proposed an index structure SOIT to organize location records for on-line query processing. We conduct extensive experiments on real low-sampling-rate datasets and demonstrate the effectiveness and efficiency of RTMG.

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Semantic trajectory mining for location prediction

Cited by 249 publications

References 15 publications

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

Discovering and predicting user routines by differential analysis of social network traces

Distant-Time Location Prediction in Low-Sampling-Rate Trajectories

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