Abstract
In current location-based services, there are numerous travel route patterns hidden in the user check-in behaviors over locations in a city. Such records rapidly accumulate and update over time, so that an efficient and scalable algorithm is demanded to mine the useful travel patterns from the big check-in data. However, discovering travel patterns under efficiency and scalability concerns from large-scaled location data had not ever carefully tackled yet. In this paper, we propose to mine the Time-aware Transit Patterns (TTP), which capture the representative traveling behaviors over consecutive locations, from the big check-in data. We model the travel behaviors among different locations into a Route Transit Graph (RTG), in which nodes represents locations, and edges denotes the transit behaviors of users between locations with certain time intervals. The time-aware transit patterns, which are required to satisfy frequent, closed, and connected requirements due to respectively physical meanings, are mined based on the RTG transaction database. To achieve such goal, we propose a novel TTPM-algorithm, which is devised to only need to scan the database once and generate no unnecessary candidates, and thus guarantee better time efficiency lower and memory usage. Experiments conducted on different cities demonstrate the promising performance of our TTPM-algorithm, comparing to a modified Apriori method.
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8643 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 |
|---|---|
| Country | Taiwan |
| City | Tainan |
| Period | 14-05-13 → 14-05-16 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Mining time-aware transit patterns for route recommendation in big check-in data. / Hsieh, Hsun-Ping; Li, Cheng-Te.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. ed. / Wen-Chih Peng; Haixun Wang; Zhi-Hua Zhou; Tu Bao Ho; Vincent S. Tseng; Arbee L.P. Chen; James Bailey. Springer Verlag, 2014. p. 818-830 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Mining time-aware transit patterns for route recommendation in big check-in data
AU - Hsieh, Hsun-Ping
AU - Li, Cheng-Te
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In current location-based services, there are numerous travel route patterns hidden in the user check-in behaviors over locations in a city. Such records rapidly accumulate and update over time, so that an efficient and scalable algorithm is demanded to mine the useful travel patterns from the big check-in data. However, discovering travel patterns under efficiency and scalability concerns from large-scaled location data had not ever carefully tackled yet. In this paper, we propose to mine the Time-aware Transit Patterns (TTP), which capture the representative traveling behaviors over consecutive locations, from the big check-in data. We model the travel behaviors among different locations into a Route Transit Graph (RTG), in which nodes represents locations, and edges denotes the transit behaviors of users between locations with certain time intervals. The time-aware transit patterns, which are required to satisfy frequent, closed, and connected requirements due to respectively physical meanings, are mined based on the RTG transaction database. To achieve such goal, we propose a novel TTPM-algorithm, which is devised to only need to scan the database once and generate no unnecessary candidates, and thus guarantee better time efficiency lower and memory usage. Experiments conducted on different cities demonstrate the promising performance of our TTPM-algorithm, comparing to a modified Apriori method.
AB - In current location-based services, there are numerous travel route patterns hidden in the user check-in behaviors over locations in a city. Such records rapidly accumulate and update over time, so that an efficient and scalable algorithm is demanded to mine the useful travel patterns from the big check-in data. However, discovering travel patterns under efficiency and scalability concerns from large-scaled location data had not ever carefully tackled yet. In this paper, we propose to mine the Time-aware Transit Patterns (TTP), which capture the representative traveling behaviors over consecutive locations, from the big check-in data. We model the travel behaviors among different locations into a Route Transit Graph (RTG), in which nodes represents locations, and edges denotes the transit behaviors of users between locations with certain time intervals. The time-aware transit patterns, which are required to satisfy frequent, closed, and connected requirements due to respectively physical meanings, are mined based on the RTG transaction database. To achieve such goal, we propose a novel TTPM-algorithm, which is devised to only need to scan the database once and generate no unnecessary candidates, and thus guarantee better time efficiency lower and memory usage. Experiments conducted on different cities demonstrate the promising performance of our TTPM-algorithm, comparing to a modified Apriori method.
UR - http://www.scopus.com/inward/record.url?scp=84915745461&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84915745461&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-13186-3_73
DO - 10.1007/978-3-319-13186-3_73
M3 - Conference contribution
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 818
EP - 830
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
A2 - Peng, Wen-Chih
A2 - Wang, Haixun
A2 - Zhou, Zhi-Hua
A2 - Ho, Tu Bao
A2 - Tseng, Vincent S.
A2 - Chen, Arbee L.P.
A2 - Bailey, James
PB - Springer Verlag
ER -