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


Data Warehouse Frequent Itemset High Aggregate Star Schema Document Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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