Scalable Database Systems :  Research Projects

MultiDimensional Clustering

The goal of this project is to provide an elegant relational database
solution for clustering tables using multiple dimensions. This is very
important in the Data Warehousing and Data Mart applications since most of
the analysis tends to be dimensional. For example, a Retail data
warehouse is organized along the key dimensions of Time, Products,
Geography (Regions), Suppliers, and Customers. Analysis is performed
using some or all of these dimensions.  A number of OLAP and
Multidimensional products have been implemented in recent years
targeted at this growing market. They claim that relational databases
are not naturally suited for such work.  With this project, we
are aiming to enhance the relational database technology so that it
will perform efficiently and without requiring complex indexes
for multidimensional analysis.

Consider the TPC-D benchmark. The significant dimensions of this
benchmark are:

• Time: Dates when orders are received, committed, and shipped
• Geography: Nations and Regions of Customers and Suppliers
• Parts description: Brand, type, size of parts

 

 

Already, several benchmark implementations use the Time dimension to
Range-Partition the important tables in TPC-D.  In our project, we are
exploring the ability to cluster and partition the tables using more than one
dimension. The expected advantages are better isolation of the data
to answer the multidiemsnional queries efficiently and better manageablilty of the
database. The following diagram provides a view of how such a multidimensional
clustering of the data will help to support the complex queries of TPC-D.



In this example, we are using a 2-D clustering scheme using months of Dates and Nations.
Given a query that selects a range of dates and some nations (in a region), the processing
strategy is to select the subset of the cells that belong in the intersection of  the range of
dates and specific  nations  using compact indexes.
 

[Projects:  Multidimensional Clustering |  XML Data Access  |   Tertiary Storage  |   Database Processing  ]
 
 

 
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