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Spatio-Temporal Evolution of Scientific Knowledge

  • Goce TrajcevskiEmail author
  • Xu Teng
  • Shailav Taneja
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 767)

Abstract

In this work we take a first step towards the problem of integrating the content and the spatio-temporal aspects of the evolution of the (published) scientific knowledge. A lot of research has been invested in developing tools and search engines that will enable more efficient querying of relevant medical (and broader scientific) data from various perspectives, spanning from retrieval of similar documents/images to HCI-based flexible query-answering systems. Variety of methodologies have been developed, founded on knowledge-bases, statistics, semantic similarity, etc. and quite a few systems are available (e.g., Medline). Parallel to this, another body of research works has emerged over the past couple of decades, targeting the efficient management of mobility and spatio-temporal data. What motivates this work is the observation that fusing the data (and corresponding techniques) developed in these two broad research fields could enable novel categories of queries that can be used to investigate various evolving spatio-temporal relationships between particular scientific topics.

We present a novel model and a formalization of this confluence, in what we call Knowledge-Evolution Trajectories (KET). We also provide a preliminary proof-of-concept implementation that enables answering novel categories of queries pertaining to KET data with a few initial observations regarding the impact of different data-representation approaches.

References

  1. 1.
    Bedard, Y., Merrett, T., Han, J.: Fundamentals of spatial data warehousing for geographic knowledge discovery. Geogr. Data Min. Knowl. Discov. 2, 53–73 (2001). Taylor and FrancisCrossRefGoogle Scholar
  2. 2.
    Bilgen, M., Abbe, R., Liu, S.J., Narayana, P.A.: Spatial and temporal evolution of hemorrhage in the hyperacute phase of experimental spinal cord injury: in vivo magnetic resonance imaging. Magn. Ressonance Med. 43(4), 594–600 (2000)CrossRefGoogle Scholar
  3. 3.
    Bogorny, V., Renso, C., de Aquino, A.R., de Lucca Siqueira, F.: Constant - A conceptual data model for semantic trajectories of moving objects. GIS 18(1), 66–88 (2014)CrossRefGoogle Scholar
  4. 4.
    Chu, W.W., Cardenas, A.F., Taira, R.T.: Kmed: A knowledge-based multimedia medical distributed database system. Inf. Sci. 20(2), 75–96 (1995)Google Scholar
  5. 5.
    Damiani, M.L., Güting, R.H.: Semantic trajectories and beyond. In: Proceedings of IEEE - MDM, pp. 1–3. Brisbane, Australia (2014)Google Scholar
  6. 6.
    Dee, C.R.: The development of the medical literature analysis and retrieval system (medlars). J. Med. Library Assoc. 94(5), 416–425 (2007)CrossRefGoogle Scholar
  7. 7.
    Ding, H., Trajcevski, G., Scheuermann, P.: Towards efficient maintenance of continuous queries for trajcectories. GeoInformatica 12(3), 255–288 (2008)CrossRefGoogle Scholar
  8. 8.
    Etzion, O., Jajodia, S., Sripada, S. (eds.): Temporal Databases: Research and Practice. LNCS, vol. 1399. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N., Schneider, M., Vazirgiannis, M.: A foundation for representing and queirying moving objects. ACM TODS 25, 1–42 (2000)CrossRefGoogle Scholar
  10. 10.
    Güting, R.H., Schneider, M.: Moving Objects Databases. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  11. 11.
    Güting, R.H., Valdés, F., Damiani, M.L.: Symbolic trajectories. ACM Trans. Spat. Algorithms Syst. 1(2), 7:1–7:51 (2015)Google Scholar
  12. 12.
    Hirano, Y., Stefanovic, B., Silva, A.C.: Spatiotemporal evolution of the fmri response to ultrashort stimuli. J. Neurosci. 31(4), 1440–1447 (2011)CrossRefGoogle Scholar
  13. 13.
    Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. ACM Trans. Database Syst. 24(2), 265–318 (1999)CrossRefGoogle Scholar
  14. 14.
    Hristovski, D., Kastrin, A., Dinevski, D., Rindflesch, T.C.: Constructing a graph database for semantic literature-based discovery. In: MEDINFO 2015: eHealth-enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics, p. 1094 (2015)Google Scholar
  15. 15.
    Issa, H.: Spatio-textual trajectories: models and applications. PhD thesis, Universita degli studi di Milano (2017)Google Scholar
  16. 16.
    Korfhage, R.: The impact of personal computers on library-based information systems. SIGIR Forum 12(4), 10–13 (1978)CrossRefGoogle Scholar
  17. 17.
    Lowe, H.J., Barnett, G.O.: Understanding and using the medical subject heading (mesh) vocabulary to perform literature searches. J. Am. Med. Assoc. 271(14), 1103–1108 (1994)CrossRefGoogle Scholar
  18. 18.
    Mokbel, M.F., Aref, W.G.: SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams. VLDB J. 17(5), 971–995 (2008)CrossRefGoogle Scholar
  19. 19.
    Parent, C., Spaccapietra, S., Renso, C., Andrienko, G.L., Andrienko, N.V., Bogorny, V., Damiani, M.L., Gkoulalas-Divanis, A., de Macêdo, J., Pelekis, N., Theodoridis, Y., Yan, Z.: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4), 42 (2013)CrossRefGoogle Scholar
  20. 20.
    Pelanis, M., Saltenis, S., Jensen, C.S.: Indexing the past, present, and anticipated future positions of moving objects. ACM Trans. Database Syst. 31(1), 255–298 (2006)CrossRefGoogle Scholar
  21. 21.
    Salton, G.: Automatic Text Processing. Addison Wesley, Massachusetts (1989)Google Scholar
  22. 22.
    Schiller, J.H., Voisard, A. (eds.): Location-Based Services. Morgan Kaufmann, San Francisco (2004)Google Scholar
  23. 23.
    Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice Hall, New Jersy (2003)Google Scholar
  24. 24.
    Taine, S.I.: New program for indexing at the national library of medicine. Bull. Med. Libr. Assoc. 47(2), 117 (1959)Google Scholar
  25. 25.
    Trajcevski, G., Donevska, I., Vaisman, A.A., Avci, B., Zhang, T., Tian, D.: Semantics-aware warehousing of symbolic trajectories. In: Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2015, pp. 1–8, 3–6 November 2015, Bellevue, WA, USA (2015)Google Scholar
  26. 26.
    Trajcevski, G., Tamassia, R., Cruz, I., Scheuermann, P., Hartglass, D., Zamierowski, C.: Ranking continuous nearest neighbors for uncertain trajectories. VLDB J. 20(5), 767–791 (2011)CrossRefGoogle Scholar
  27. 27.
    Vaisman, A.A., Zimányi, E.: Data Warehouse Systems: Design and Implementation. Data-Centric Systems and Applications. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  28. 28.
    Xing, X., Mokbel, M.F., Aref, W.G., Hambrusch, S.E., Prabhakar, S.: Scalable spatio-temporal continuous query processing for location-aware services. In: International Conference on Scientific and Statistical Database Management (SSDBM) (2004)Google Scholar
  29. 29.
    Xiong, X., Mokbel, M.F., Aref, W.G.: Sea-cnn: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE, pp. 643–654 (2005)Google Scholar
  30. 30.
    Yu, X., Pu, K.Q., Koudas, N.: Monitoring k-nearest neighbor queries over moving objects. In: ICDE, pp. 631–642 (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceNorthwestern UniversityEvanstonUSA

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