Advertisement

On Designing a GeoViz-Aware Database System - Challenges and Opportunities

  • Mohamed SarwatEmail author
  • Arnab Nandi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10411)

Abstract

The human’s ability to perceive, consume, and interact with the data is in-fact limited. One key observation is worth considering – that Geospatial data is typically consumed as aggregate visualizations.

References

  1. 1.
    Battle, L., Chang, R., Stonebraker, M.: Dynamic prefetching of data tiles for interactive visualization. In: SIGMOD. ACM (2016)Google Scholar
  2. 2.
    Battle, L., Stonebraker, M., Chang, R.: Dynamic reduction of query result sets for interactive visualizaton. In: Big Data, pp. 1–8. IEEE (2013)Google Scholar
  3. 3.
    Cetintemel, U., Cherniack, M., DeBrabant, J., Diao, Y., Dimitriadou, K., Kalinin, A., Papaemmanouil, O., Zdonik, S.B.: Query steering for interactive data exploration. In: CIDR (2013)Google Scholar
  4. 4.
    Chan, S.-M., Xiao, L., Gerth, J., Hanrahan, P.: Maintaining interactivity while exploring massive time series. In: Symposium on Visual Analytics Science and Technology, pp. 59–66. IEEE (2008)Google Scholar
  5. 5.
    Kamat, N., Jayachandran, P., Tunga, K., Nandi, A.: Distributed interactive cube exploration. In: ICDE (2014)Google Scholar
  6. 6.
    Krumm, J.: A Markov model for driver turn prediction. In SAE (2008)Google Scholar
  7. 7.
    Lins, L., Klosowski, J.T., Scheidegger, C.: Nanocubes for real-time exploration of spatiotemporal datasets. TVCG 19(12), 2456–2465 (2013)Google Scholar
  8. 8.
    Liu, Z., Jiang, B., Heer, J.: imMens: real-time visual querying of big data. In: Computer Graphics Forum, vol. 32, pp. 421–430. Wiley Online Library (2013)Google Scholar
  9. 9.
    Nandi, A., Yu, C., Bohannon, P., Ramakrishnan, R.: Data cube materialization and mining over mapreduce. IEEE TKDE 24(10), 1747–1759 (2012)Google Scholar
  10. 10.
    Omidvar-Tehrani, B., Amer-Yahia, S., Termier, A.: Interactive user group analysis. In: CIKM, pp. 403–412. ACM (2015)Google Scholar
  11. 11.
    Pahins, C., Stephens, S., Scheidegger, C., Comba, J.: Hashedcubes: simple, low memory, real-time visual exploration of big data. TVCG 23, 671–680 (2016)Google Scholar
  12. 12.
    Park, Y., Cafarella, M., Mozafari, B.: Visualization-aware sampling for very large databases. In: ICDE. IEEE (2016)Google Scholar
  13. 13.
    Wang, L., Christensen, R., Li, F., Yi, K.: Spatial online sampling and aggregation. In: VLDB, vol. 9, pp. 84–95. VLDB Endowment (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Arizona State UniversityTempeUSA
  2. 2.Ohio State UniversityColumbusUSA

Personalised recommendations