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What is OLAM?
OLAM stands for Online analytical mining. It is also known as OLAP Mining. It integrates online analytical processing with data mining and mining knowledge in multi-dimensional databases. There are several paradigms and structures of data mining systems.
Various data mining tools must work on integrated, consistent, and cleaned data. This requires costly pre-processing for data cleaning, data transformation, and data integration. Thus, a data warehouse constructed by such pre-processing is a valuable source of high-quality information for both OLAP and data mining. Data mining can serve as a valuable tool for data cleaning and data integration.
OLAM is particularly important for the following reasons which are as follows −
High quality of data in data warehouses − Most data mining tools are required to work on integrated, consistent, and cleaned information, which needs costly data cleaning, data integration, and data transformation as a pre-processing phase. A data warehouse constructed by such pre-processing serves as a valuable source of high-quality data for OLAP and data mining. Data mining can also serve as a valuable tool for data cleaning and data integration.
Available information processing infrastructure surrounding data warehouses − Comprehensive data processing and data analysis infrastructures have been or will be orderly constructed surrounding data warehouses, which contains accessing, integration, consolidation, and transformation of various heterogeneous databases, ODBC/OLE DB connections, Web-accessing and service facilities, and documenting and OLAP analysis tools. It is careful to create the best use of the available infrastructures instead of constructing everything from scratch.
OLAP-based exploratory data analysis − Effective data mining required exploratory data analysis. A user will be required to traverse through a database, select areas of relevant information, analyze them at multiple granularities, and display knowledge/results in multiple forms.
Online analytical mining supports facilities for data mining on multiple subsets of data and at several levels of abstraction, by drilling, pivoting, filtering, dicing, and slicing on a data cube and some intermediate data mining outcomes.
On-line selection of data mining functions − It supports a user who cannot understand what type of knowledge they would like to mine. By integrating OLAP with various data mining functions, online analytical mining provides users with the flexibility to choose desired data mining functions and swap data mining tasks dynamically.
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