Database Information Processing (OLAP)

Batch Processing

Batch processing is another form of data processing that is still used by many businesses today. When a company has large amounts of data that do not require any user interaction, they can prepare the data in advance, and then when the computer system is not busy (like during non-work hours) the information can be processed all in one batch. For example, large companies may use batch processing to run payroll. Most employees have similar tax obligations (social security, medicare, federal withholding) so the data can be prepared in advance, and many hundreds or thousands of employee payroll calculations can be completed in one batch. Another example is credit card bills. Credit card charges are placed on your account shortly after each transaction. The credit card company, however, does not send out a bill for each individual charge. Instead, it waits until the end of your billing cycle and processes each credit card customer's bill for the month in one batch.

Dicing

Dicing is the same as slicing except that the result can be in multiple dimensions, but still obtaining a subset of the data. For instance, a dice might limit a three-dimensional collection of data into a smaller three-dimensional collection of data by discarding certain records and fields.

Drilling Up and Down

Drilling up and down merely shifts the view of the data. Drilling down provides more detail and drilling up provides summarized data.

Event-Driven Processing (EDA)

Event-driven processing uses business events, such as submitting an order to a vendor, receiving goods, or creating new employee records to trigger messages to be sent by middleware between software modules that are completely independent of one another. In an EDA, each event is handled individually as it appears; the business unit experiencing the event "pushes" the event to the recipient, providing the recipient with immediate relevant business events; these events are "pushed" simultaneously to all interested parties (i.e., purchasing, receiving, manufacturing, sales and the customer).

Data Warehouse Processes.

Extract data: This is how the data is gathered for the warehouse. Transform data: This step is where the data is reconfigured to fit the setup of the data warehouse. Load data: The Data is appropriately formatted to make it easier to recall and use in this step.

Examples of OLDP: Forecasting sales trends based on historic patterns.

Historical patterns don't use real-time events as part of their data. Real Time Data

OLAP

In OLAP (Online Analytical Processing), data are processed through a suite of analysis software tools. You might think of OLAP as sitting on top of the data warehouse so that the user can retrieve data from the data warehouse and analyze the data without having to separate the database management system (DBMS) operations from the more advanced analysis operations.

OLTP (On-line Transaction Processing)

Is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).

OLAP (On-line Analytical Processing)

Is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).

An Example of OLAP: The combination of analyzed data from various databases into a meaningful report.

OLAP is used to find the relevant information when looking at a database.

OLAP Operations

OLAP operations include slicing, dicing, drilling down (or up), rolling-up, and pivoting the data.

An Example of OLAP: The transformation of raw data so it can be stored in the data warehouse.

OLAP takes the information from a data warehouse and makes it easier to sift through.

An Example of OLAP: Looking at data in a more granular fashion; examining the details of the data.

OLAP uses the details of the data to find what information the user wants.

OLDP

OLDP is a "live" database, Online Data Processing (OLDP) A number of systems require that data be processed immediately in real-time. Sometimes these are called Online Real-time Systems. When you book an airline ticket online, for example, that information is processed immediately and then stored in the airline company's database. That happens in real time.

Examples of OLDP: Purchasing an item through Amazon.

OLDP is used to update information like how much is left in stock after you purchase an item.

Examples of OLDP: Data are processed in real-time.

OLDP processes data that is actively being collected.

Pivot

Pivoting rotates data to view the data from a different perspective. Consider a database that contains product sales information by year and by country. If the typical view of the data is by product, we might instead want to pivot all of the data so that our view first shows us each year. The pivot then reorganizes the data from a new perspective. We could also pivot these data by country of sale instead.

Rolling Up

Rolling up is similar to drilling up in that it summarizes data, but in doing so, it collapses the data from multiple items (possible over more than one dimension) into a single value. As an example, all HR records might be collapsed into a single datum such as the number of current employees, or a single vector that represents the number of employees in each position (e.g., management, technical, support).

Slicing

Slicing creates a subset of the data by reducing the data from multiple dimensions to one dimension. For instance, if we think of the data in our data warehouse as being in three dimensions, slicing would create a one-dimensional view of the data.

Online Transaction Processing (OLTP)

This type of information is output from what is referred to as an Online Transaction Processing (OLTP) system. It uses data that has been generated internally and stored in databases.