EFFICIENT JOIN PROCESSING FOR COMPLEX RASTERIZED OBJECTS

Hans-Peter Kriegel, Peter Kunath, Martin Pfeifle, Matthias Renz

2005

Abstract

One of the most common query types in spatial database management systems is the spatial intersection join. Many state-of-the-art join algorithms use minimal bounding rectangles to determine join candidates in a first filter step. In the case of very complex spatial objects, as used in novel database applications including computer-aided design and geographical information systems, these one-value approximations are far too coarse leading to high refinement cost. These expensive refinement cost can considerably be reduced by applying adequate compression techniques. In this paper, we introduce an efficient spatial join suitable for joining sets of complex rasterized objects. Our join is based on a cost-based decompositioning algorithm which generates replicating compressed object approximations taking the actual data distribution and the used packer characteristics into account. The experimental evaluation on complex rasterized real-world test data shows that our new concept accelerates the spatial intersection join considerably.

References

  1. Arge L., Procopiuc O., Ramaswamy S., Suel T., Vitter J.S.: Scalable Sweeping-Based Spatial Join, In Proc. of the VLDB Conference, 1998, 570-581.
  2. Burrows M., Wheeler D. J.: A Block-sorting Lossless Data Compression Algorithm, Digital Systems Research Center Research Report 124, 1994.
  3. Faloutsos C., Roseman S.: Fractals for Secondary Key Retrieval. In Proc. ACM PODS, 1989, 247-252.
  4. Gaede V.: Optimal Redundancy in Spatial Database Systems, In Proc. 4th Int. Symp. on Large Spatial Databases, 1995, 96-116.
  5. Jagadish H. V.: Linear Clustering of Objects with Multiple Attributes. In Proc. ACM SIGMOD, 1990, 332-342.
  6. Kriegel H.-P., Pfeifle M., Pötke M., Seidl T.: Spatial Query Processing for High Resolutions. Database Systems for Advanced Applications (DASFAA), 2003.
  7. Kunath P.: Compression of CAD-data, Diploma thesis, University of Munich, 2002.
  8. Lempel A., Ziv J.: A Universal Algorithm for Sequential Data Compression. IEEE Transactions on Information Theory, Vol. IT-23, No. 3, 1977, 337-343.
  9. Orenstein J. A.: Spatial Query Processing in an ObjectOriented Database System, In Proc. of the ACM SIGMOD Conference, 1986, 326-336.
  10. Seward J.: The bzip2 and libbzip2 official home page. http://sources.redhat.com/bzip2.
  11. Stonebraker M., Frew J., Gardels K., Meredith J.: The SEQUOIA 2000 Sorage Benchmark. In Proc. ACM SIGMOD Int. Conf. on Management of Data: 1993
Download


Paper Citation


in Harvard Style

Kriegel H., Kunath P., Pfeifle M. and Renz M. (2005). EFFICIENT JOIN PROCESSING FOR COMPLEX RASTERIZED OBJECTS . In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 5: ICEIS, ISBN 972-8865-19-8, pages 20-30. DOI: 10.5220/0002512200200030


in Bibtex Style

@conference{iceis05,
author={Hans-Peter Kriegel and Peter Kunath and Martin Pfeifle and Matthias Renz},
title={EFFICIENT JOIN PROCESSING FOR COMPLEX RASTERIZED OBJECTS},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 5: ICEIS,},
year={2005},
pages={20-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002512200200030},
isbn={972-8865-19-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 5: ICEIS,
TI - EFFICIENT JOIN PROCESSING FOR COMPLEX RASTERIZED OBJECTS
SN - 972-8865-19-8
AU - Kriegel H.
AU - Kunath P.
AU - Pfeifle M.
AU - Renz M.
PY - 2005
SP - 20
EP - 30
DO - 10.5220/0002512200200030