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EPUI: Experimental Platform for Urban Informatics

Authors:

Panos K. Chrysanthis,

Konstantinos Pelechrinis,

Demetrios Zeinalipour-YaztiAuthors Info & Claims

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

Pages 1761 - 1764

https://doi.org/10.1145/3183713.3193560

Published: 27 May 2018 Publication History

Abstract

Recent studies in urban navigation have revealed new demands (e.g., diversity, safety, happiness, serendipity) for the navigation services that are critical to providing useful recommendations to travelers. This exposes the need to design next-generation navigation services that accommodate these newly emerging aspects. In this paper, we present a prototype system, namely, EPUI (an Experimental Platform of Urban Informatics), which provides a testbed for exploring and evaluating venues and route recommendation solutions that balance between different objectives (i.e., demands) including the newly discovered ones. In addition, EPUI incorporates a modularized design, enabling researchers to upload their own algorithms and compare them to well-known algorithms using different performance metrics. Its user interface makes it easily usable by both end-user and experienced researchers.

References

[1]

Openstreetmap. http://www.openstreetmap.org, 2017.

[2]

Pittsburgh police arrest data. https://catalog.data.gov/dataset/pittsburgh-police-arrest-data, 2017.

[3]

V. Ceikute and C. S. Jensen. Vehicle routing with user-generated trajectory data. In 16th IEEE MDM, 2015.

Digital Library

[4]

Z. Chen, H. T. Shen, and X. Zhou. Discovering popular routes from trajectories. In IEEE ICDE, 2011.

Digital Library

[5]

P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In VLDB, 1997.

Digital Library

[6]

M. Drosou and E. Pitoura. Multiple radii disc diversity: Result diversification based on dissimilarity and coverage. ACM TODS, 40(1), Article No. 4, 2015.

Digital Library

[7]

E. Galbrun, K. Pelechrinis, and E. Terzi. Urban navigation beyond shortest route: The case of safe paths. Information Systems, 57(C):160--171, 2016.

Digital Library

[8]

X. Ge, P. K. Chrysanthis, and A. Labrinidis. Preferential diversity. In ACM ExploreDB, 2015.

Digital Library

[9]

X. Ge, P. K. Chrysanthis, and K. Pelechrinis. Mpg: Not so random exploration of a city. In IEEE MDM, 2016.

[10]

X. Ge, A. Daphalapurkar, M. Shimpi, D. Kohli, K. Pelechrinis, P. K. Chrysanthis, and D. Zeinalipour-Yazti. Data-driven serendipity navigation in urban places. In IEEE ICDCS, 2017.

[11]

A. Gionis, T. Lappas, K. Pelechrinis, and E. Terzi. Customized tour recommendations in urban areas. In ACM WSDM, 2014.

Digital Library

[12]

T. Kurashima, T. Iwata, G. Irie, and K. Fujimura. Travel route recommendation using geotags in photo sharing sites. In ACM CIKM, 2010.

Digital Library

[13]

T. Mikolov, K. Chen, G. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.

Digital Library

[14]

D. Quercia, R. Schifanella, and L. M. Aiello. The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In ACM HT, 2014.

Digital Library

[15]

S. Shang, R. Ding, B. Yuan, K. Xie, K. Zheng, and P. Kalnis. User oriented trajectory search for trip recommendation. In EDBT, 2012.

Digital Library

[16]

S. Shang, R. Ding, K. Zheng, C. S. Jensen, P. Kalnis, and X. Zhou. Personalized trajectory matching in spatial networks. The VLDB Journal, 23(3):449--468, 2014.

Digital Library

[17]

L.-Y. Wei, Y. Zheng, and W.-C. Peng. Constructing popular routes from uncertain trajectories. In ACM KDD, 2012.

Digital Library

[18]

J. Yuan, Y. Zheng, X. Xie, and G. Sun. T-drive: Enhancing driving directions with taxi drivers' intelligence. IEEE TKDE, 25(1):220--232, 2013.

Digital Library

Cited By

Tomaras D(2024)On Urban Data Analytics and Applications in the Big Data Era2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00067(328-330)Online publication date: 24-Jun-2024
https://doi.org/10.1109/MDM61037.2024.00067
Ge XChrysanthis PPelechrinis KZeinalipour-Yazti DSharaf M(2020)Serendipity-based Points-of-Interest NavigationACM Transactions on Internet Technology10.1145/339119720:4(1-32)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1145/3391197

Index Terms

EPUI: Experimental Platform for Urban Informatics
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

May 2018

1874 pages

ISBN:9781450347037

DOI:10.1145/3183713

General Chairs:
Gautam Das
University of Texas at Arlington, USA
,
Christopher Jermaine
Rice University, USA
,
Philip Bernstein
Microsoft Research, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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Funding Sources

National Science Foundation
National Institutes of Health

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SIGMOD/PODS '18

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SIGMOD

SIGMOD/PODS '18: International Conference on Management of Data

June 10 - 15, 2018

TX, Houston, USA

Acceptance Rates

SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Tomaras D(2024)On Urban Data Analytics and Applications in the Big Data Era2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00067(328-330)Online publication date: 24-Jun-2024
https://doi.org/10.1109/MDM61037.2024.00067
Ge XChrysanthis PPelechrinis KZeinalipour-Yazti DSharaf M(2020)Serendipity-based Points-of-Interest NavigationACM Transactions on Internet Technology10.1145/339119720:4(1-32)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1145/3391197

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