now well known that data warehousing/Online Analytical Processing offers an
approach that increases the level of productivity and improves the
decision-making process of knowledge workers within an organization.
ability to act quickly and decisively in today’s increasingly competitive
marketplace is critical to the success of organizations. The volume of
information that is available to corporations is rapidly increasing and
frequently overwhelming. Those organizations that will effectively and
efficiently manage these tremendous volumes of data, and use the information to
make business decisions, will realize a significant competitive advantage in
enables analysts, business managers, and executives to gain insight into data
through fast, consistent, interactive access to a wide variety of possible
views of information. OLAP transforms raw data so that it reflects the real
dimensionality of the enterprise as understood by the user.
Technology itself is boundless. There are infinite possibilities in the
research and development industry for Analytical technology. We will just
describe a single implementation that shows how OLAP plays a vital role in
Query based interactive systems.
Data warehousing, the creation of
an enterprise-wide data store, is the first step towards managing these volumes
of data. The data warehouse is becoming an integral part of many information
delivery systems because it provides a single, central location where a
reconciled version of data extracted from a wide variety of operational systems
Building a data
warehouse has its own special challenges (common data model, common business
dictionary, etc.) and is a complex endeavor. However, just having a data
warehouse does not provide organizations with the often-heralded business
benefits of data warehousing. To complete the supply chain from transactional
system to decision maker, IT organizations need to deliver
systems that allow knowledge workers to make strategic and tactical decisions
based on the information stored in these data warehouses. These decision
support systems are referred to as On-Line Analytical Processing (OLAP)
OLAP presents a complex viewing system (an example might be a graph) to represent data. We can compare it with graphs, rather its more generalized form, extending to nth dimension. Graphs are the most favorite option to get better understanding of the problem and its domain.
can also be defined as:
Heterogeneous Data sources + Multidimensional Data storage + Querying Tool
(Data warehouse) (Cubes) (MDX)
now on I will discuss the technical side of OLAP. Here we must mention some
terms that are necessary to understand before we go deeper. A diagram will show
the architecture of OLAP and how it will integrate with other layers.
As we have defined
Data warehouse, Data Mart term left for some discussion. Data ware house and
the data marts are defined and used in distinct ways in different data
warehousing systems. The simplest definition of Data mart can be is a small
collection of data that is used for the business analysis queries of a single
department or work group. Basically it’s just a logical categorization of sub
systems data within a larger volume.
|Cubes are the main
objects in online analytic processing (OLAP), a technology that provides fast
access to data in a data warehouse. A cube is a set of data that is usually
constructed from a subset of a data warehouse and is organized and summarized
into a multidimensional structure defined by a set of dimensions and measures. Dimensions are structural attribute of a cube,
which is an organized hierarchy of categories (levels). These categories
typically describe |
a similar set of
members upon which the user wants to base an analysis. For example, a geography
dimension might include levels for Country, Region, State or Province, and
City. Measures are a set of values that are based on a column in the cube's
table and are usually numeric. Measures are the central values that are
aggregated and analyzed.
Since cubes are
data structure, therefore there is an obvious need of a standard way to access
the data resides in it. Just like the same way as SQL do. SQL is design for
two-dimensional structure (tables), and because of such SQL’s limitation it
cannot be used in Accessing cubes data. To overcome such limitation a new
language has been developed which is similar in many ways to the SQL syntax
called MDX stands for Multidimensional expression. But is not an extension of
the SQL language.
We have used OLAP
in an interactive system; objective of this project is to provide performance
enhancement and analytical processing capabilities in the ABC Online System. ABC
system is an online query system that asks questions in certain English language
and gives results instantly.
We initiated the
project with following objectives:
should be simple, intuitive
with WCL queries
dynamic updates in the core logic
implemented WCL, English like querying language explicitly designed for this
project. The users to interact with the system will finally use this language.
The sample grammar is
goal has successfully achieved. We have implemented a unique strategy that is
simpler, both in terms of design and implementation
It has been
noticed by deep study that WCL grammar has a specific pattern in their design,
and therefore can be utilize to generate such SQL templates that accommodate
almost every part of WCL.
table we have successfully developed a generic SQL+MDX template that will be
filled dynamically during MDX generation process.
engine parse tokens and gather information, writing it to MDX array. After the
completion of this parsing process, we finally have a complete MDX that when
executed returns information set required by the user. Since the logic has been
broken into pieces and stored in the database, we also have the opportunity of
even manipulating this logic at execution time.
still so much to explore in analytical processing. Technology is far more
complex and need serious research work for maximum in the field of science and
technology. We have shown just a single implementation that OLAP can also be
use on non-transactional systems.