Answer:
(i) OLAP (Online Analytical Processing) is a technology that allows users to quickly and easily analyze large amounts of data from multiple dimensions. It is typically used in business settings to support decision making and data exploration. There are several different implementations of OLAP, including SQL-based OLAP and multi-dimensional OLAP. One key concept in OLAP is aggregation, which refers to the process of combining data from multiple sources into a single, more comprehensive view.
(ii) Multi-dimensional data refers to data that can be analyzed and understood from multiple perspectives or dimensions. For example, a company's sales data might be analyzed by product, region, and time period. In this case, the three dimensions would be product, region, and time period. Roll-up is a common operation in multi-dimensional data analysis, which involves aggregating data from multiple lower-level dimensions into a higher-level one. Pivoting is another common operation, which involves rotating the data so that different dimensions are displayed as rows or columns, making it easier to compare and analyze.
(iii) Data mining is the process of discovering patterns and trends in large datasets. It involves applying various techniques and tools to identify patterns and relationships in data, and can be used to make predictions about future trends or outcomes. Data preparation is an important step in data mining, which involves cleaning and formatting the data to make it ready for analysis. Some common techniques used in data mining include clustering, classification, and association rule mining. Tools that are commonly used in data mining include decision trees, neural networks, and support vector machines.