Parallel OLAP for Relational Database Environment

Parallel OLAP for Relational Database Environment : 

Parallel OLAP for Relational Database Environment By Manirul Hak Munsi (IT/2007/015) Souvik Kolay (IT/2007/016)

CONTENTS : 

CONTENTS Introduction Review Of Works Technical Details Types of OLAP Dimensional Analysis of OLAP Databases Technical details of OLAP cube OLAP For Relational Database Environment Application of OLAP Future Scope References

INTRODUCTION : 

INTRODUCTION OLAP stands for On Line Analytical Processing, a series of protocols used mainly for business reporting. Using OLAP, businesses can analyse data in all manner of different ways, including budgeting, planning, simulation, data warehouse reporting, and trend analysis. A main component of OLAP is its ability to make multidimensional calculations, allowing a wide and lightning-fast array of possibilities. In addition, the bigger the business, the bigger its business reporting needs. Multidimensional calculations enable a large business to complete in seconds what it otherwise would have waited a handful of minutes to receive.

REVIEW OF WORKS : 

REVIEW OF WORKS OLAP is not a new concept and has persisted through the decades. As a matter of fact, the origin of OLAP technology can be traced way back in 1962. It was not until 1993 that the term OLAP was coined in the Codd white paper authored by the highly esteemed database researcher Ted Codd, who also established the 12 rules for an OLAP product It was Kenneth Iverson who first introduced the base foundation of OLAP through his book “A Programming Language”, which defined a mathematical language with processing operators and multidimensional variables OLAP market experienced strong growth in late 90s with dozens of commercial products going into market. In 1998, Microsoft released its first OLAP Server - Microsoft Analysis Services, which drove wide adoption of OLAP technology and moved it into mainstream In 2005, Microsoft released the next generation of OLAP and Data Mining technology as Analysis Services 2005

REVIEW OF WORKS : 

REVIEW OF WORKS Market Structure Below is a list of top OLAP vendors in 2006, with figures in millions of US Dollars,

Technical Details : 

Technical Details Concept : At the core of any OLAP system is the concept of an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures which are categorized by dimensions. Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure. A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale. Any number of dimensions can be added to the structure such as Store, Cashier, or Customer by adding a column to the fact table. This allows an analyst to view the measures along any combination of the dimensions.

Technical Details(Contd) : 

Technical Details(Contd) For Example : Sales Fact Table TimeDimension

Types of OLAP : 

Types of OLAP OLAP systems have been traditionally categorized using the following taxonomy:- Multidimensional OLAP Relational OLAP Hybrid OLAP Other types The following acronyms are also sometimes used, although they are not as widespread as the ones above:- WOLAP - Web-based OLAP DOLAP - Desktop OLAP RTOLAP - Real-Time OLAP

Dimensional Analysis of OLAP Databases : 

Dimensional Analysis of OLAP Databases Levels of detail  OLAP databases organize data by level of detail, using the same categories you use to analyze the data. Levels in a geography dimension

Dimensional Analysis of OLAP Databases : 

Dimensional Analysis of OLAP Databases Dimensions and cubes :- A set of levels that encompass one aspect of the data, such as geographic locations, is called a dimension. Similarly, information about when sales were made could be organized in a time dimension with levels for year, quarter, month, and day.

Technical details of OLAP cube : 

Technical details of OLAP cube An OLAP (Online analytical processing) cube is a data structure that allows fast analysis of data. Technical definition In database theory, an OLAP cube is an abstract representation of a projection of an RDBMS relation. W : (X,Y,Z) → W

Technical details of OLAP cube(Contd) : 

Technical details of OLAP cube(Contd) Functionality The OLAP cube consists of numeric facts called measures which are categorized by dimensions. Pivot A financial analyst might want to view or "pivot" the data in various ways, such as displaying all the cities down the page and all the products across a page.

OLAP for Relational Database Environment : 

OLAP for Relational Database Environment Relational databases are not well suited for near instantaneous analysis and display of large amounts of data. OLAP provide a much better representation for Relational Database. OLAP (online analytical processing) cubes can be thought of as extensions to the two-dimensional array of a spread sheet.

Application of OLAP : 

Application of OLAP On-Line Analytical Processing (OLAP) provides customers with tools that can be used to perform "multidimensional analysis" on data to discover hidden information within the database OLAP operations Slice Dice Drill Down/Up Roll-up Pivot Linking cubes and sparsity Uses in business

Future Scope : 

Future Scope The future of OLAP is very much dependent on it’s vendors specially Microsoft and Oracle.

Future Scope : 

Future Scope ORACLE will launch it’s next OLAP compatible Database Software OLAP on the field of Relational Database depend on the database software vendor, we may expect the further research will help to provide the software vendor new features in their product

Conclusion : 

Conclusion OLAP has very much come on the field of online analysis of the database of those industry who deals with huge amount of data. The database server provider also providing the OLAP facility with their tool. Unfortunately Parallel OLAP has not yet come so loudly for the relational database. But as Microsoft one of the main vendor of database server has launch it’s own Parallel OLAP, we are sure that other will also follow them.

References : 

References Document Reference: Christopher Adamson and Michael Venerable. The Data Warehouse Design solutions. John Wiley & Sons, Inc, 1998. Todd Eavis. Parallel OLAP Computing. PhD thesis, Dalhousie University, 2004. A. Gupta and I. S. Mumick. Materialized Views: Techniques, Implementations, and Applications. MIT Press, 1999. Online Reference: www.google.com Google definition. www.wikipedia.org www.oracle.com/technetwork/database/options/olap/index.htm msdn.microsoft.com/enus/library/ms175367(SQL.90).aspx www.oracle.com/ip/analyze/warehouse/bus_intell