Encyclopedia of GIS

2008 Edition
| Editors: Shashi Shekhar, Hui Xiong

Top-k OLAP Queries Using Augmented Spatial Access Methods

  • Nikos Mamoulis
  • Spiridon Bakiras
  • Panos Kalnis
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-35973-1_1391


Top-k OLAP queries using multi-resolution tree structures; OLAP query; Top-k query


A top-k OLAP query groups measurements with respect to some abstraction level of interesting dimensions and selects the k groups with the highest aggregate value. An example of such a query is “find the 10 combinations of product-type and month with the largest sum of sales”. Top‑k queries may also be applied in a spatial database context, where objects are augmented with some measures that must be aggregated according to a spatial division. For instance, consider a map of objects (e. g., restaurants) where each object carries some non-spatial measure (e. g., the number of customers served during the last month). Given a partitioning of the space into regions (e. g., city districts), the goal is to find the regions with the highest number of served customers. Formally, the top-k OLAP query can be defined as follows:

Let\( \mathcal{D} = \{d_1,\dots,d_m\} \)

This is a preview of subscription content, log in to check access

Recommended Reading

  1. 1.
    Beckmann, N., Kriegel, H., Schneider, R., Seeger, B.: The R*–tree: An efficient and robust access method for points and rectangles. In: Proc. of ACM SIGMOD, pp. 220–231 (1990)Google Scholar
  2. 2.
    Fang, M., Shivakumar, N., Garcia-Molina, H., Motwani, R., Ullman, J. D.: Computing iceberg queries efficiently. In: Proc. of VLDB (1998)Google Scholar
  3. 3.
    Han, J., Stefanovic, N., Koperski, K.: Selective materialization: An efficient method for spatial data cube construction. In: Second Pacific-Asia Conference, PAKDD-98, pp. 144–158, Melbourne, Australia, 15–17 Apr 1998Google Scholar
  4. 4.
    Harinarayan, V., Rajaraman, A., Ullman, J. D.: Implementing data cubes efficiently. In: Proc. of ACM SIGMOD, pp. 205–216 (1996)Google Scholar
  5. 5.
    Hjaltason, G. R., Samet, H.: Distance browsing in spatial databases. ACM TODS 24(2), 265–318 (1999)Google Scholar
  6. 6.
    Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data.In: Proceedings of the 9th International Symposium on Spatial and Temporal Databases (SSTD), pp. 364–381, Angra dos Reis, Brazil, August 2005Google Scholar
  7. 7.
    Kimball, R.: The Data Warehouse Toolkit. John Wiley, USA (1996)Google Scholar
  8. 8.
    Lazaridis, I., Mehrotra, S.: Progressive approximate aggregate queries with a multi-resolution tree structure. In: Proc. of ACM SIGMOD (2001)CrossRefGoogle Scholar
  9. 9.
    Papadias, D., Kalnis, P., Zhang, J., Tao, Y.: Efficient OLAP operations in spatial data warehouses. In: Proc. of SSTD (2001)Google Scholar
  10. 10.
    Pedersen, T. B., Tryfona, N.: Pre-aggregation in spatial data warehouses. In: Proc. of SSTD, pp. 460–480 (2001)Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Nikos Mamoulis
    • 1
  • Spiridon Bakiras
    • 1
  • Panos Kalnis
    • 2
  1. 1.Department of Computer ScienceUniversity of Hong KongHong KongChina
  2. 2.Department of Computer ScienceNational University of SingaporeSingaporeSingapore