(Enter summary)
Abstract: On-line analytical processing (OLAP) has gained its popu-
larity in database industry. With a huge amount of data stored in spatial
databases and the introduction of spatial components to many relational
or object-relational databases, it is important to study the methods for
spatial data warehousing and on-line analytical processing of spatial
data. In this paper, we study methods for spatial OLAP, by integra-
tion of nonspatial on-line analytical processing (OLAP) methods with
spatial... (Update)
Context of citations to this paper: More
.... and similarity in time series data [tt, 30] multi level classification and prediction [23, 6] spatial data cube construction [21], spatial association rule mining [24] OLAP mining [12] Weblog mining [31] etc. A data mining system, DBliner [16, 14] has been...
.... and similarity in time series data [11, 30] multi level classification and prediction [23, 6] spatial data cube construction [21], spatial association rule mining [24] OLAP mining [12] Weblog mining [31] etc. A data mining system, DBMiner [16, 14] has been...
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BibTeX entry: (Update)
J. Han, N. Stefanovic, and K. Koperski. Selective materialization: An efficient method for spatial data cube construction. In Proc. 1998 Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD'98), Melbourne, Australia, April 1998. http://citeseer.ist.psu.edu/han98selective.html More
@inproceedings{ han98selective,
author = "J. Han and N. Stefanovic and K. Koperski",
title = "Selective Materialization: An Efficient Method for Spatial Data Cube Construction",
booktitle = "Research and Development in Knowledge Discovery and Data Mining, Second Pacific-Asia Conference, {PAKDD}'98",
address = "Melbourne, Australia",
editor = "X. Wu and K. Ramamohanarao and K. Korb",
pages = "144--158",
year = "1998",
url = "citeseer.ist.psu.edu/han98selective.html" }
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