02046nas a2200205   4500008004100000245009900041210006900140260000900209300001400218490000600232520135600238653002101594653002501615653001701640653001801657100001601675700002001691700001201711856011701723       2007                        eng d00aStorage and indexing of relational OLAP views with mixed categorical and continuous dimensions0 aStorage and indexing of relational OLAP views with mixed categor  c2007  a180 - 1900 v53 aDue to the widespread adoption of location-based services and other spatial applications, data warehouses that store spatial information are becoming increasingly prevalent. Consequently, it is becoming important to extend the standard OLAP paradigm with features that support spatial analysis and aggregation. While traditional OLAP systems are limited to data characterized by strictly categorical feature dimensions, Spatial OLAP systems must provide support for both categorical and spatial feature dimensions. Such spatial feature dimensions are typically represented by continuous data values. In this paper we propose a technique for representing and indexing relational OLAP views with mixed categorical and continuous data. Our method builds on top of an established mechanism for standard OLAP and exploits characteristic properties of space-filling curves. It allows us to effectively represent and index mixed categorical and continuous data, while dynamically adapting to changes in dimension cardinality during updates. We have implemented the proposed storage and indexing methods and evaluated their build, update, and query times using both synthetic and real datasets. Our experiments show that the proposed methods based on Hilbert curves of dynamic resolutions offers significant performance advantages especially for view updates.10aContinuous space10aSpace-filling curves10aSpatial OLAP10aView indexing1 aBaltzer, O.1 aRau-Chaplin, A.1 aZeh, N.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-44949248100&partnerID=40&md5=a63605fb283d6f4f67f8f3aaa7a854be