We utilize Decision Support methodology to extract
information from data and use such information as a basis for consistent and
fact-based decision making.
We
analyze huge volumes of data with OLAP
and ETL
solutions to build, deploy, and manage a series of linked, dimensional data
marts to form an integrated
business intelligence
system in order to improve business
performance, maximize productivity and profitability, as well as making
better business decisions.
We create an
advanced data analysis environment that supports decision making,
business modeling, and operations research activities. We share four main
characteristics:
We add the following extensions to traditional RDBMS -
Relational Database Management System technology:
Multidimensional data schema support within the RDBMS
Data access language and query performance are optimized for
multidimensional data
Support for very large databases (VLDBs)
Conceptually, MDBMS end users visualize the stored data as a
three-dimensional cube known as a data cube. Data cubes are created by
extracting data from the operational databases or from the data warehouse. We
utilize ETL -
Extraction, Transformation and Load techniques for data acquisition.
ROLAP
MOLAP
OurROLAP provides OLAP
functionality by using relational databases and familiar relational query
tools to store and analyze multidimensional data. This approach builds on
existing relational technologies and represents a natural extension to all
our clients who already use relational database management systems within
their organizations.
Our MOLAP - Multidimensional Online Analytical Processing extends OLAP
functionality to MDBMSs - Multidimensional Database Management Systems.
(An MDBMS uses special proprietary techniques to store data in matrix like
n-dimensional arrays.) MOLAP's premise is that multidimensional databases are
best suited to manage, store, and analyze multidimensional data.
