OLAP

Introduction

As previously established, RDBMS provide a necessary foundation for an enterprise IS.  An important function of an organization’s IS is to provide business intelligence (BI) to guide an organization’s activities and strategic planning.  BI includes market research and environmental scanning that are both based on evaluating database information to identify past, present and future patterns and trends (Garcia-Molina, Ullman & Widom, 2009; Fayyad, Piatetsky-Shapiro & Smyth, 1996; Kotler & Keller, 2007).  Recall that Codd developed the RDBMS model in the 1970’s to overcome deficiencies of existing database systems (Codd, 1990).  In the 1990’s, industry observed the rapid development of OLAP that sought to add analytical functionality to the large amounts of data being collected by organizations and stored in RDBMS (Codd, Codd & Salley, 1993).  As a result, today’s mature OLAP serve as an integral component of today’s organizational data mining and decision support systems (DSS).

Online Analytic Processing (OLAP)

OLAP typically consists of information derived from complex queries and aggregations posed against a large dataset (Garcia-Molina, Ullman & Widom, 2009).  This allows different perspectives or slices of information to be readily and more quickly retrieved and assessed than if the complex queries were run against the database in real time on an ad hoc basis (Codd, Codd & Salley, 1993; Garcia-Molina, Ullman & Widom, 2009).    OLAP’s augmented data can be comprised of many dimensions and data sources however it is typical for today’s OLAP data to add Web statistics, time stamped temporal and geographical spatial dimensions to the RDBMS information (Garcia-Molina, Ullman & Widom, 2009; Raab, 2009; Rivest, Bedard & Marchand, 2001).  This creates a data space often referred to as a fact table or data cube. With this basis, a data cube is a collection of a RDBMS information organized around objects of interest and their relevant dimensions (e.g. temporal and spatial components).

Data cubes can be further discriminated by their structure.  Multidimensional OLAP (MOLAP) implementations store information in a large multidimensional array (Garcia-Molina, Ullman & Widom, 2009).    It is well known that multidimensional arrays provide quick access to information however dependent on their implementation, they may not scale well.  In contrast to MOLAP, Relational OLAP (ROLAP) is structured as a star (Garcia-Molina, Ullman & Widom, 2009).    ROLAP allows the relational elements or objects of interest to maintain pointers from the elements to the element’s dimensions (Garcia-Molina, Ullman & Widom, 2009).  ROLAP’s pointer based structure facilitates scalability is pointers can be added as necessary to map to new dimensions.   In both models, it is possible to tailor the OLAP’s index structures and capabilities to meet the desired OLAP functionality (Garcia-Molina, Ullman & Widom, 2009).

As introduced above, OLAP works on large data sets and therefore differs from typical routine queries that access only a small subset of the database’s relations (Garcia-Molina, Ullman & Widom, 2009).    As a result, OLAP can tie up (e.g. lock) large portions of the database inhibiting transactions and impacting system efficiency (Garcia-Molina, Ullman & Widom, 2009).   This can be severely detrimental to e-commerce and transactional RDBMS.   To mitigate OLAP’s impact to online transaction processing system (OLTP) systems, OLAP is typically performed on snapshots of the database or on data warehouse data (Garcia-Molina, Ullman & Widom, 2009).    Both snapshots and data warehouses represent a historical time stamped instance of the DBMS however while the data may be out of date, it may suit its intended purpose and satisfy its fitness for use. Data warehouses provide additional benefits to OLAP as their time stamped snapshot basis probably already supports the OLAP’s temporal requirements.

In summary, information in an organization’s database serves as a foundation for forming an organization’s strategic decisions (Rivest, Bedard & Marchand, 2001).  OLAP can quickly assess large data sets and provide decision makers with the necessary basis to guide their strategic decisions (Codd, Codd & Salley, 1993;  Rivest, Bedard & Marchand, 2001).  OLAP functionality that includes geographical information is particularly useful today citing the move towards mobile and location based computing (Wolfe, 2008).

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