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What is the structure for On-Line Analytical Mining?
An OLAM server performs analytical mining in data cubes similarly to an OLAP server performs online analytical processing. An integrated OLAM and OLAP mechanism,where the OLAM and OLAP servers both accept user online queries (or commands) via a graphical user interface API and operate with the data cube in the data analysis via a cube API.
A metadata directory can be used to instruct the access of the data cube. The data cube can be created by accessing and integrating multiple databases via an MDDB API and by filtering a data warehouse via a database API that can provide OLE DB or ODBC connections.
Because an OLAM server can implement several data mining tasks, including concept description, association, classification, prediction, clustering, time-series analysis, etc. It generally includes multiple integrated data mining modules and is higher sophisticated than an OLAP server.
An OLAM engine can perform multiple data mining tasks, such as concept description, association, classification, prediction, clustering, and time-series analysis. Therefore, it usually consists of multiple, integrated data mining modules, making it more sophisticated than an OLAP engine. There is no fundamental difference between the data cube required for OLAP and that for OLAM, although OLAM analysis might require more powerful data cube construction and accessing tools.
This is the case when OLAM involves more dimensions with finer granularities or involves the discovery-driven exploration of multi-feature aggregations on the data cube, thereby requiring more than OLAP analysis. Moreover, when exploratory data mining identifies interesting spots, an OLAM engine might need to drill through from the data cube into the corresponding relational databases for detailed analysis of specific portions of data.
This is the case when OLAM involves more dimensions with finer granularities or involves the discovery-driven exploration of multi-feature aggregations on the data cube, thereby requiring more than OLAP analysis. Moreover, when exploratory data mining identifies interesting spots, an OLAM engine might need to drill through from the data cube into the corresponding relational databases for detailed analysis of specific portions of data.
Moreover, a data mining process can disclose that the dimensions or measures of a constructed cube are not suitable for data analysis. Here, a refined data cube design could improve the quality of data warehouse construction.
An effective data mining requires exploratory data analysis. Users often want to traverse through a database, select portions of relevant data, analyze them at different granularities, and present knowledge/results in different forms.
Online analytical mining provides facilities for data mining on different subsets of data and at different levels of abstraction. It can achieve this by drilling, pivoting, filtering, dicing, and slicing on a data cube and intermediate data mining outcomes. This, together with data/knowledge visualization tools, can highly increase the power and adaptability of exploratory data mining.
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