DBMS vs Data Mining
A DBMS (Database Management System) is a complete system used for managing digital databases that allows storage of database content, creation/maintenance of data, search and other functionalities. On the other hand, Data Mining is a field in computer science, which deals with the extraction of previously unknown and interesting information from raw data. Usually, the data used as the input for the Data mining process is stored in databases. Users who are inclined toward statistics use Data Mining. They utilize statistical models to look for hidden patterns in data. Data miners are interested in finding useful relationships between different data elements, which is ultimately profitable for businesses.
DBMS, sometimes just called a database manager, is a collection of computer programs that is dedicated for the management (i.e. organization, storage and retrieval) of all databases that are installed in a system (i.e. hard drive or network). There are different types of Database Management Systems existing in the world, and some of them are designed for the proper management of databases configured for specific purposes. Most popular commercial Database Management Systems are Oracle, DB2 and Microsoft Access. All these products provide means of allocation of different levels of privileges for different users, making it possible for a DBMS to be controlled centrally by a single administrator or to be allocated to several different people. There are four important elements in any Database Management System. They are the modeling language, data structures, query language and mechanism for transactions. The modeling language defines the language of each database hosted in the DBMS. Currently several popular approaches like hierarchal, network, relational and object are in practice. Data structures help organize the data such as individual records, files, fields and their definitions and objects such as visual media. Data query language maintains the security of the database by monitoring login data, access rights to different users, and protocols to add data to the system. SQL is a popular query language that is used in Relational Database Management Systems. Finally, the mechanism that allows for transactions help concurrency and multiplicity. That mechanism will make sure that the same record will not be modified by multiple users at the same time, thus keeping the data integrity in tact. Additionally, DBMS provide backup and other facilities as well.
Data mining is also known as Knowledge Discovery in Data (KDD). As mentioned above, it is a felid of computer science, which deals with the extraction of previously unknown and interesting information from raw data. Due to the exponential growth of data, especially in areas such as business, data mining has become very important tool to convert this large wealth of data in to business intelligence, as manual extraction of patterns has become seemingly impossible in the past few decades. For example, it is currently been used for various applications such as social network analysis, fraud detection and marketing. 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/feature selection and Evaluation/validation. Regression is finding functions with minimal error to model data. And association is looking for relationships between variables. Data mining is usually used to answer questions like what are the main products that might help to obtain high profit next year in Wal-Mart?
What is the difference between DBMS and Data mining?
DBMS is a full-fledged system for housing and managing a set of digital databases. However Data Mining is a technique or a concept in computer science, which deals with extracting useful and previously unknown information from raw data. Most of the times, these raw data are stored in very large databases. Therefore Data miners use the existing functionalities of DBMS to handle, manage and even preprocess raw data before and during the Data mining process. However, a DBMS system alone cannot be used to analyze data. But, some DBMS at present have inbuilt data analyzing tools or capabilities.