Data Mining, Database and Temporal Database

Data Mining, Database and Temporal Database

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of  machine learning, statistics, and database systems. OR it can be defined as Data Mining is the process of analyzing data from different perspectives and summarizing the results as useful information.

Explain some important terms, which are used in data mining:

A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. Data sets can also consist of a collection of documents or files.

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

DBMS

The DBMS is the software that interacts with end-users, applications, and the database itself to capture and analyze the data. The DBMS software additionally encompasses the core facilities provided to administer the database. The sum total of the database, the DBMS and the associated applications can be referred to as a "database system". Often the term "database" is also used to loosely refer to any of the DBMS, the database system or an application associated with the database. A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modelling techniques.

Temporal database stores data relating to time instances. It offers temporal data types and stores information relating to past, present and future time. Temporal databases could be uni-temporal, bi-temporal or tri-temporal.

More specifically the temporal aspects usually include valid time, transaction time or decision time.

• Valid time is the time period during which a fact is true in the real world.
• Transaction time is the time period during which a fact stored in the database was known.
• Decision time is the time period during which a fact stored in the database was decided to be valid.

Temporal Data Mining is a rapidly evolving area of research that is at the intersection of several disciplines, including statistics (e.g., time series analysis), temporal pattern recognition, temporal databases, optimisation, visualisation, high-performance computing, and parallel computing. 

The research area of temporal databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query temporal data.