What are the four requirements in transaction management?

To ensure the integrity of the data, the database system must maintain some desirable properties of the transaction. In particular, developed four rules, known as the requirements of ACID, they ensure the accuracy and reliability of the system:

Atomicity

All changes to data are performed as if they are a single operation. That is, all the changes are performed, or none of them are.

For example, in an application that transfers funds from one account to another, the atomicity property ensures that, if a debit is made successfully from one account, the corresponding credit is made to the other account.

Consistency

Data is in a consistent state when a transaction starts and when it ends.

For example, in an application that transfers funds from one account to another, the consistency property ensures that the total value of funds in both the accounts is the same at the start and end of each transaction.

Isolation

The intermediate state of a transaction is invisible to other transactions. As a result, transactions that run concurrently appear to be serialized.

For example, in an application that transfers funds from one account to another, the isolation property ensures that another transaction sees the transferred funds in one account or the other, but not in both, nor in neither.

Durability

After a transaction successfully completes, changes to data persist and are not undone, even in the event of a system failure.

For example, in an application that transfers funds from one account to another, the durability property ensures that the changes made to each account will not be reversed.

 

There are three teams that are used for transaction management:

 

COMMIT - to save the changes;

ROLLBACK - to undo the changes;

SAVEPOINT - to install special cusps.

 

 

What is data mining?

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

 

Data mining usually deals with following four tasks: clustering, classification, regression, and association. Clustering is identifying similar groups from unstructured data. Classification is learning rules that can be applied to new data and will typically include following steps: preprocessing of data, designing modeling, learning and validation. Regression is finding functions with minimal error to model data. And association is looking for relationships between variables.

 

The key properties of data mining are:

· Automatic discovery of patterns.

Data mining is accomplished by building models. A model uses an algorithm to act on a set of data. The notion of automatic discovery refers to the execution of data mining models.

· Prediction of likely outcomes.

Many forms of data mining are predictive.

· Creation of actionable information.

Other forms of data mining identify natural groupings in the data.

· Focus on large data sets and databases.

Data mining can derive actionable information from large volumes of data.