Who invented OLAP Cube?

OLAP (online analytical processing) Cube

An olap cube, or “online analytical processing” (OLAP) is a specially designed computer database which is designed to contain a large amount of easily accessible data. Data is stored in a virtual structure called a “cube” because it allows for calculation of data with multiple types of factors. Individual cubes are grouped together to form a larger cube type structure. This allows the user to perform several types of actions to include the types of data included in the other cubes to come up with the desired result of information required. OLAP cubes are particularly useful when there are multiple factors that need to be considered at the same time. Data from very large OLAP cubes is able to be accessed in a very short amount of time, for example in as little as 1.2 seconds.

The types of systems that were predecessors to the OLAP cube required that the operator submit what was known as a “query” or request for a specific combination of data factors, such as type of product, size of container of product, store location, and specific information about the customer who purchased the product, including age, sex and residence location. After the operator submitted the query, it could take anywhere from a few minutes to a few hours for the particular query to come back with the desired information. With the advent of the OLAP cube, this amount of time has been dramatically reduced.

The names of the types of data in an OLAP cube are typically arranged in a “star” configuration, or a set of information boxes arranged in the shape of a five-pointed star connected by lines to show their interrelationship. OLAP systems are generally classified into three types of systems; multidimensional ( the classic OLAP, also referred to as MOLAP), relational ( referred to as ROLAP) and a hybrid system (HOLAP). MOLAP systems are occasionally subject to what is called “database explosion” or when the OLAP system uses up too much memory space due to the way it has been configured. In spite of this drawback, MOLAP systems usually perform better than ROLAP systems when they are properly managed because the data can be more easily compressed. The HOLAP version combines the best aspects of the other two systems in one package.

Data can be retrieved from an OLAP cube in five ways: slice, dice, drill down/up, roll up or pivot. Data ideally should be arranged so that each cube is full to its capacity, not leaving blank areas that the computer recognizes as zeroes. This is referred to as overcoming “sparsity”. Once the cubes are loaded as desired, a company using an OLAP system can perform a query that can provide valuable insight to running the business. This is particularly useful to warehousing concerns for corporations. For example, a grocery store chain that wanted to know how many bags of potato chips it sold of a certain size of a specific brand in all of its stores in a particular city, region or state in the previous quarter of the current calendar year, could retrieve this information. This would be valuable to the company to forecast budgets if it wanted to establish a new store in a location with similar demographics.

Who created OLAP Cube

The first type of program that produced OLAP cube queries was created in 1970 and was marketed by the name Express. The term OLAP was not created until 1993, when Edgar Codd developed an essay describing what he called the “twelve laws of online analytical processing”. OLAP type products saw initial growth in the market in the late 90s until 1998, when Microsoft Corporation developed its own server based program. After this period the OLAP cube became much more used and recognized in the marketplace. By the year 2006, there were ten major vendors of OLAP programs and a few additional minor ones recognizing a total market value of 5.7 billion dollars.

OLAP Cube Operations

Slice: A slice is a subset of a multi-dimensional array corresponding to a single value for one or more members of the dimensions not in the subset.

Dice: The dice operation is a slice on more than two dimensions of a data cube.

Drill Down/Up: Drilling down or up is a specific analytical technique whereby the user navigates among levels of data ranging from the most summarized to the most detailed.

Roll-up: A roll-up involves computing all of the data relationships for one or more dimensions.

Pivot: This operation is also called rotate operation. It rotates the data in order to provide an alternative presentation of data.

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