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Abstract
Data warehouse and On-Line Analytical Processing (OLAP) are important
components of Decision Support Systems (DSS). They are noticed in database
systems more than every time. Now, there are many products and services for
managing data warehousee and processing on-line analytical queries. On-line
Analytical Processing (OLAP) systems have different requirements than On-Line
Transactinal Processing (OLTP) systems.
In this thesis, we first represent a comprehensive overview of data warehouse and
OLAP. We describe back-end tools those extract data from different (and possibly
herogeneus) databases, transform extracted data, and load transformed data in data
warehouse. We also explain about front-end tools use for reporting, querying and
analysing data. After that, we describe algorithms are represented for efficient query
processing in data warehouses and specify advantages and drabacks of each
algorithm.
We represent a new approach for efficient cube computation by extending two best
existing methodes: BUC and CCUBE. Results of implementation of this new method
(Ex-CUBE), show this algorithm has the best efficiency between all previous
algorithms. When we use Ex-Cube, processing time for monotonic queries in large
amount of data, is very better than other algorithms. Moreover, Ex-Cube use a hypergraph structure called Ex-Graph for storing data. This graph causes we have a 2dimension view of cube. Thus Ex-cube’s space need, is less than other methods when
we agreggate a subset of dimensions set.
Keywords: Data warehouse, On-Line Analytical Processing (OLAP), Decision
support systems, Multi-dimensional Data model, Data Cube,