Spatio-temporal pattern mining on trajectory data using ARM
(2017) Tehran's Joint ISPRS International Conferences of the 2nd Geospatial Information Research, GI Research 2017, the 4th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2017 and the 6th Earth Observation of Environmental Changes, EOEC 2017 In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42. p.395-399- Abstract
Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity... (More)
Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user's visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users' behaviour in a system and can be utilized in various location-based applications.
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- author
- Khoshahval, S. ; Farnaghi, M. LU and Taleai, M.
- publishing date
- 2017-09-26
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Apriori algorithm, Association rule mining, Frequent pattern mining, Location-based application, User trajectory
- host publication
- Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran
- series title
- International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
- volume
- 42
- edition
- 4W4
- pages
- 5 pages
- conference name
- Tehran's Joint ISPRS International Conferences of the 2nd Geospatial Information Research, GI Research 2017, the 4th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2017 and the 6th Earth Observation of Environmental Changes, EOEC 2017
- conference location
- Tehran, Iran, Islamic Republic of
- conference dates
- 2017-10-07 - 2017-10-10
- external identifiers
-
- scopus:85032334747
- ISSN
- 1682-1750
- DOI
- 10.5194/isprs-archives-XLII-4-W4-395-2017
- language
- English
- LU publication?
- no
- id
- ceca91e0-1503-4da9-9b52-a735a62691c8
- date added to LUP
- 2019-05-06 12:12:13
- date last changed
- 2022-04-26 00:26:30
@inproceedings{ceca91e0-1503-4da9-9b52-a735a62691c8,
abstract = {{<p>Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user's visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users' behaviour in a system and can be utilized in various location-based applications.</p>}},
author = {{Khoshahval, S. and Farnaghi, M. and Taleai, M.}},
booktitle = {{Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran}},
issn = {{1682-1750}},
keywords = {{Apriori algorithm; Association rule mining; Frequent pattern mining; Location-based application; User trajectory}},
language = {{eng}},
month = {{09}},
pages = {{395--399}},
series = {{International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives}},
title = {{Spatio-temporal pattern mining on trajectory data using ARM}},
url = {{http://dx.doi.org/10.5194/isprs-archives-XLII-4-W4-395-2017}},
doi = {{10.5194/isprs-archives-XLII-4-W4-395-2017}},
volume = {{42}},
year = {{2017}},
}