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)


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.


  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