Analysis of Mining, Visual Analytics Tools and Techniques in Space and Time

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

All living things are connected to the space and time, which really shows a necessity to improve their sophistication for leading a better life. Exploration and prediction in space and time has been the tough chore to the researchers and developers. Development in the technology helps to elevate the persevering difficulties. Two interdisciplinary approaches in the computer science that has become pre-eminent in the effective analysis of space and time are data mining and visual analytics. Visual analytics is one interactive user interface where we can explore and visualize the data using visual analytic tools. So, visual analytics with the complex data requires a competent approach for accuracy which is nevertheless a data mining process. But the real scenario is, techniques and tools are more developed but may not nail in terms of accuracy and speed for handling complex-and time-oriented data. The main cause of the dearth may be more new tools and techniques developed by more researchers are not deliberated. The mission of the research paper is to study the techniques and tools of data mining and visual analytic in space and time.

Keywords

Data mining No-SQL databases Space and time Spatial data Spatio-temporal Temporal data Visual analytics Geographical analysis 

References

  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kauffman, San Francisco (2006)Google Scholar
  2. 2.
    Mohammed, A., et al.: A unified approach for spatial data query. Int. J. Data Mining Knowl. Manag. Process 3(6), 55–71 (2013)CrossRefGoogle Scholar
  3. 3.
    Sun, G.D., Wu, Y.C., Liang, R.H., et al.: A survey of visual analytics techniques and applications: state-of-the-art research and future challenges. J. Comput. Sci. Technol. 28(5), 852–867 (2013)CrossRefGoogle Scholar
  4. 4.
    Fotheringham, S.A., Rogerson, P.A.: The SAGE Handbook of Spatial Analysis. SAGE Publications, London (2008)Google Scholar
  5. 5.
  6. 6.
    Gennady, A., et al.: Space, time and visual analytics. Int. J. Geogr. Inf. Sci. 24(10), 1577–1600 (2010)CrossRefGoogle Scholar
  7. 7.
    Angela, L., Schmidt, A., Tischendorf, L.: Data mining and linked open data–new perspectives for data analysis in environmental research. Ecol. Model. 295, 5–17 (2015)CrossRefGoogle Scholar
  8. 8.
    Yoo, J.S., Boulware, D., Kimmey, D.: Incremental and parallel association mining for evolving spatial data: a less iterative approach on map reduce (2015)Google Scholar
  9. 9.
    Jeremy, M., Guo, D.: Spatial data mining and geographic knowledge discovery—an introduction. Comput. Environ. Urban Syst. 33(6), 403–408 (2009)CrossRefGoogle Scholar
  10. 10.
    Yuzuru, Tanaka, et al.: Geospatial visual analytics of traffic and weather data for better winter road management. data mining for geoinformatics, pp. 105–126. Springer, New York (2014)Google Scholar
  11. 11.
    Petelin, B., et al.: Multi-level association rules and directed graphs for spatial data analysis. Expert Syst. Appl. 40(12), 4957–4970 (2013)CrossRefGoogle Scholar
  12. 12.
    Xie, Y., et.al.: Silverback: Scalable association mining for temporal data in columnar probabilistic databases. Data Engineering (ICDE), IEEE, pp. 1072–1083 (2014)Google Scholar
  13. 13.
    Wei, Tian, et al.: A survey on clustering based meteorological data mining. Int. J. Grid Distrib. Comput. 7(6), 229–240 (2014)Google Scholar
  14. 14.
    Antunes, C.M., Oliveira, A.L.: Temporal data mining: an overview. KDD workshop on temporal data mining, pp. 1–13 (2001)Google Scholar
  15. 15.
    Twitter topic explorer, www.datainterfaces.org
  16. 16.
    Perer, A., Sun, J.: MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression. In: AMIA Annual Symposium Proceedings, vol. 2012, pp. 716–725 (2012)Google Scholar
  17. 17.
    Quantifying protests around the world, www.recordedfuture.com
  18. 18.
    Xue, C. J., Q. Dong., W. X. Ma.: Object-oriented spatial-temporal association rules mining on ocean remote sensing imagery. In: IOP Conference Series: Earth and Environmental Science, vol. 17, no. 1, IOP Publishing, (2014)Google Scholar
  19. 19.
    Freddy, L., et al.: Smart traffic analytics in the semantic web with STAR-CITY: scenarios, system and lessons learned in Dublin City. Web Semantics: Science, Services and Agents on the World Wide Web 27, 26–33 (2014)Google Scholar
  20. 20.
    Ma, W., Xue, C., Zhou, J.: Mining time-series association rules from Western Pacific spatial-temporal data. In: IOP Conference Series: Earth and Environmental Science. vol. 17. no. 1. IOP Publishing (2014)Google Scholar
  21. 21.
    Musdholifah, A., Hashim, S.Z.M.: Triangular kernel nearest neighbor based clustering for pattern extraction in spatio-temporal database. In: Intelligent Systems Design and Applications (ISDA), pp. 67–73. IEEE (2010)Google Scholar
  22. 22.
    Musdholifah, A., Hashim, S.Z.M.: Scatter-PCA for visual clustering of spatio-temporal data. IJCSNS 14(1), 72–76 (2014)Google Scholar
  23. 23.
    Munson, Michael E., et al.: Data mining for identifying novel associations and temporal relationships with Charcot foot. Journal of diabetes research, Vol. 2014 (2014)Google Scholar
  24. 24.
    Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Comput. Environ. Urban Syst. 47, 16–27 (2014)CrossRefGoogle Scholar
  25. 25.
    Reddy, P.: Sequential spatio-temporal pattern mining with time lag. Dissertation. University of Illinois, Chicago (2014)Google Scholar
  26. 26.
    Tayyab Asif, M., et al.: Spatiotemporal patterns in large-scale traffic speed prediction. IEEE Trans. Intell. Transp. Syst. 15(2), 794–804 (2014)CrossRefGoogle Scholar
  27. 27.
    Mohan, A.: A new spatio-temporal data mining method and its application to reservoir system operation. Dissertation. University of Nebraska, Nebraska (2014)Google Scholar
  28. 28.
    Schubert, E., Zimek, A., Kriegel, H.P.: Generalized outlier detection with flexible kernel density estimates. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM), Philadelphia (2014)Google Scholar
  29. 29.
    Viswanath, B., et al. : Towards detecting anomalous user behavior in online social networks. In: Proceedings of the 23rd USENIX Security Symposium (USENIX Security), pp. 223–238. San Deigo (2014)Google Scholar
  30. 30.
    Lu, T., Wu, L., Ma, X., Shivakumara, P., Tan, C.L.: Anomaly detection through spatio-temporal context modeling in crowded scenes. In: 22nd International Conference Pattern Recognition (ICPR), pp. 2203–2208. IEEE (2014)Google Scholar
  31. 31.
    Olislagers, F., Worring, M.: The spatiotemporal multivariate hypercube for discovery of patterns in event data. In: IEEE Conference Visual Analytics Science and Technology (VAST), pp. 235–236. IEEE (2012)Google Scholar
  32. 32.
    Junghoon, C., et al.: Public behavior response analysis in disaster events utilizing visual analytics of microblog data. Comput. Graphics 38, 51–60 (2014)CrossRefGoogle Scholar
  33. 33.
    Markus, H., et al.: Uncertainty-aware video visual analytics of tracked moving objects. J. Spat. Inform. Sci. 2, 87–117 (2015)Google Scholar
  34. 34.
    Straumann, R.K., Çöltekin, A., Andrienko, G.: Towards (Re) constructing narratives from georeferenced photographs through visual analytics. Cartogr. J. 51(2), 152–165 (2014)CrossRefGoogle Scholar
  35. 35.
    Zhu, X., Guo, D.: Mapping large spatial flow data with hierarchical clustering. Trans. GIS 18(3), 421–435 (2014)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Leavitt, N.: Will NoSQL databases live up to their promise? Computer 43(2), 12–14 (2010)CrossRefGoogle Scholar
  37. 37.
    List of NoSQL databases. http://nosql-database.org/
  38. 38.
  39. 39.
  40. 40.

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for WomenDeemed UniversityCoimbatoreIndia

Personalised recommendations