On the other hand, Data mining helps discover hidden pattern or trend in data to support a conclusion. As the name suggests, unlike OLAP that operates at a summary view, Data mining operates at detail level. For Instance, If Walter would like to identify the trend of products sold during a holiday season, data mining would help them answer that question based on historic data. Although, OLAP and data mining operate on data to gain Intelligence, the main difference lies on how they operate on the data. OLAP tools provide multidimensional data analysis and summaries of the data.
On the contrary data mining focuses on ratios, patterns and influences in the set of data. OLAP and data mining can complement each other. OLAP might point out problems with sales of a specific product for Walter for this month in particular region. Data mining can be used to gain an insight about the behavior of customers in the region. Data mining can predict such as 5% increase in sale. Data running can be used to Identify the most Important attributes concerning sales and those attributes could be used to design the data model in OLAP. We can divide
IT systems into transactional (ALTO) and analytical (OLAP). ALTO OLAP ALTO stands for On-line Transaction Processing OLAP stands for On-line Analytical Processing ALTO tables are highly normalized OLAP tables are generally De-normalized with fewer tables ALTO comprises of Operational data. This Is the original source of the data OLAP contains consolidation data. The source for OLAP data is various ALTO databases Typically very fast processing speed Depends on the amount of data involved To control and run fundamental business tasks To help with planning, solving and decision support
Reveals a snapshot of ongoing business processes 1 OFF large number of short on-line transactions (INSERT, UPDATE, DELETE) Characterized by relatively low volume of transaction. Queries are often very complex and involve aggregation Relatively small space requirement if historical data is archived Large space requirement due to existence of aggregation structures and history data; requires more indexes than ALTO Consistency, reliability and accuracy of data in a relational database is achieved using relational integrity.
This defines a set of rules that enforce a basic fundamental incept of existence of data and a relationship between the data by use of primary and foreign keys. Relational integrity constraint can be mainly categorized into entity integrity and referential integrity. The basis of these integrates is primary keys for entity integrity and foreign key for referential integrity. The primary key in a table uniquely identifies the records in the table and the foreign key in one table references the primary key of another table. Entity Integrity: This is the mechanism the system provides to maintain primary keys.
Primary key is a unique identifier for tepees in an entity. It ensures two properties for primary keys, Primary key values for a table should be unique and it should not match the key value of another row in the table Primary key should not take in null values. No value of primary key should be set to null The entity integrity constraint ensures that each value in the primary key field uniquely identifies the row in that table and it is not assigned to null at any point of time. The system enforces entity integrity by restricting operations such as to create invalid primary keys that violate the constraint.
Referential Integrity: This is the mechanism that the system provides to ensure the relationship between tables remain consistent by the use of foreign keys. A foreign key in one table refers to a primary key in another table. It has one property for foreign key, A valid foreign key must always refer to a primary key of another table and it can also be a null value This implies that the foreign key referencing the primary key cannot be null as the primary key cannot take in null value. If the foreign key is null then it means that the primary key value does not exist in the referencing table.
Although referential integrity looks much simple than entity integrity, the consequences are far more complex, as it refers both primary and foreign keys No operation like INSERT, UPDATE reference table for primary key No operation like UPDATE, DELETE can change or remove a primary key while there is a referencing foreign key Foreign key constraint can be setup so that when DELETE operation is performed on a parent table, it can delete the values in the child table referencing the deleted value or can be set to null.