Q 7. Match the following

(i) OLAP------------------ (a) Regression
(ii) OLTP ---------------- (b) Data Warehouse
(iii) Decision Tree-------- (c) RDBMS
(iv) Neural Network-------- (d) Classification
(i) (ii) (iii) (iv)
(A) (b) (c) (a) (d)
(B) (b) (c) (d) (a)
(C) (c) (b) (a) (d)
(D) (c) (b) (d) (a)
Answer (B). Explanation. Although sometimes used interchangeably, the terms data warehousing and online analytical processing (OLAP) apply to different components of systems often referred to as decision support systems or business intelligence systems. Components of these types of systems include databases and applications that provide the tools analysts need to support organizational decision-making. Decision tree builds classification or regression models in the form of a tree structure. OLTP System deals with operational data. Operational data are those data involved in the operation of a particular system. Example: In a banking System, you withdraw amount from your account. Then Account Number, Withdrawal amount, Available Amount, Balance amount, Transaction Number etc are operational data elements. In an OLTP system data are frequently updated and queried. So quick response to a request is highly expected. Since the OLTP systems invlove large number of update quiries, the database tables are optimized for write operations. To prevent data redundancy and to prevent update anomalies the database tables are normalized.Set of tables that are normalized are fragmented.Normalization makes the write operation in the database tables more efficient. OLAP deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time. Data from OLTP are collected over a period of time and store it in a very large database called Data warehouse. The Data warehouses are highly optimized for read (SELECT) operation. Although neural network is used for classification but it can be used for the regression also.