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A Geo-Social Data Model for Moving Objects

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Data Mining and Big Data (DMBD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9714))

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Abstract

In this paper, we combine moving-object database and social network systems and present a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for massive moving objects. A bulk of data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects.

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References

  1. Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. (CSUR) 40(1), 1–10 (2008)

    Article  Google Scholar 

  2. Bogorny, V., Renso, C., Aquino, A.R., Lucca Siqueira, F., Alvares, L.O.: CONSTAnT-a conceptual data model for semantic trajectories of moving objects. Trans. GIS 18(1), 66–88 (2014)

    Article  Google Scholar 

  3. Crandall, D.J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proc. Natl. Acad. Sci. 107(52), 22436–22441 (2010)

    Article  Google Scholar 

  4. Egenhofer, M.J., Franzosa, R.D.: Point-set topological spatial relations. Int. J. Geogr. Inf. Syst. 5, 161–174 (1991)

    Article  Google Scholar 

  5. Forlizzi, L., Gting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases. ACM SIGMOD 29(2), 319–330 (2000)

    Article  Google Scholar 

  6. Guting, R.H., Bhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Syst. (TODS) 25, 1–42 (2000)

    Article  Google Scholar 

  7. Guting, R.H., Ding, Z.: Modeling and querying moving objects in networks. VLDB J. 15(2), 165–190 (2006)

    Article  Google Scholar 

  8. Jensen, C.S., Lu, H., Yang, B.: Indoor - a new data management frontier. IEEE Data Eng. Bull 33, 12–17 (2010)

    Google Scholar 

  9. Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB Endowment, vol. 30, pp. 840–851 (2004)

    Google Scholar 

  10. Long, J.A., Nelson, T.A.: A review of quantitative methods for movement data. Int. J. Geogr. Inf. Sci. 27, 292–318 (2013)

    Article  Google Scholar 

  11. Mokbel, M.F., Sarwat, M.: Mobility and social networking: a data management perspective. Proc. VLDB Endow. 6, 1196–1197 (2013)

    Article  Google Scholar 

  12. Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Bogorny, V., Damiani, M.L., Macedo, J., Pelekis, N., Theoderidis, Y., Yan, Z.: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4), 39–76 (2013)

    Article  Google Scholar 

  13. Pelekis, N., Theodoridis, Y., Janssens, D.: On the management and analysis of our lifesteps. ACM SIGKDD Explor. Newsl. 15, 23–32 (2014)

    Article  Google Scholar 

  14. Sandu Popa, I.: Modeling. University of Versailles-Saint-Quentin, Querying and Indexing Moving Objects with Sensors on Road Networks (2010)

    Google Scholar 

  15. Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1046–1054 (2011)

    Google Scholar 

  16. Schneider, M.: Moving Objects in Databases and GIS: State-of-the-Art and Open Problems. In: Navratil, G. (ed.) Research Trends in Geographic Information Science, pp. 169–187. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  17. Sistla, A.P., Wolfson, O., Chamberlain, S., Dao, S.: Modeling and querying moving objects. In: International Conference on Data Engineering (ICDE), pp. 422–422 (1997)

    Google Scholar 

  18. Spaccapietra, S., Parent, C.: Adding meaning to your steps (keynote paper). In: Jeusfeld, M., Delcambre, L., Ling, T.-W. (eds.) ER 2011. LNCS, vol. 6998, pp. 13–31. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  19. Tang, W., Zhuang, H., Tang, J.: Learning to infer social ties in large networks. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 381–397. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  20. Wolfson, O., Chamberlain, S., Kalpakis, K., Yesha, Y.: Modeling moving objects for location based services. In: König-Ries, B., Makki, K., Makki, S.A.M., Pissinou, N., Scheuermann, P. (eds.) IMWS 2001. LNCS, vol. 2538, pp. 46–58. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 41401460, 41271408) and the Key Research Program of the Chinese Academy of Sciences (Grant No. ZDRW-ZS-2016-6-3). And we also thank the anonymous referees for their helpful comments and suggestions.

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Authors and Affiliations

  1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Hengcai Zhang, Feng Lu & Jie Chen

  2. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, 350003, China

    Hengcai Zhang, Feng Lu & Jie Chen

Authors
  1. Hengcai Zhang
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  2. Feng Lu
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  3. Jie Chen
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Corresponding author

Correspondence to Feng Lu .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

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© 2016 Springer International Publishing Switzerland

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Zhang, H., Lu, F., Chen, J. (2016). A Geo-Social Data Model for Moving Objects. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_11

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  • DOI: https://doi.org/10.1007/978-3-319-40973-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40972-6

  • Online ISBN: 978-3-319-40973-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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