What is the daily sales difference during a promotional period versus a normal One of the fundamental processes of data mining is to analyze correlations. Data mining may be used to automatically perform knowledge discovery by giving the mining algorithm loose cues about potential relationships and letting the algorithm OLAP is complimentary to data mining and is most likely the first, and most . Georgia. Impact. Sans Serif. Serif. Tahoma. Trebuchet MS. Verdana. 1. 2. 3. In this paper, we introduce the concept of OLAP mining and discuss how OLAP mining should be implemented in a data mining system. 1 INTRODUCTION.
Some of these can be applied to data mining. The most common ones are decision trees and neural networks. Other algorithms such as genetic algorithms and fuzzy logic are also included in some data mining packages.
For example, many popular association algorithms for analyzing product associations of large transaction tables were proposed by database researchers.
Understanding the Relationship Between OLAP and Data Mining Data Mining
OLAP can help data mining tasks with the data transformation step thanks to its data aggregation engine. In many cases, patterns can be found only in aggregated data. It is difficult to discover patterns directly from the fact table.
For example, analyzing the sales of snow tires at the city level can be challenging for many data mining algorithms, because there are too many cities.
OLAP (Online Analytical Processing) Definition
There are often millions of members in a dimension and tens of millions of aggregated values in a cube. Like any relational database, a cube contains hidden patterns such as sales trends, product associations, customer segments, and so on. An OLAP cube needs data mining techniques to discover the inside information. Market basket analysis about products: Market basket analysis of product associations is a frequent marketing problem. Store managers want to know which products sell together in order to do promotional cross-selling.
Store managers also like to group customers into segments using customer demographic information as well as aggregated measures, for example, monthly spending at the store.
Segmentation can be done on dimensions other than the customer. For example, the marketing department of a retail chain may want to cluster its stores based on store attributes and sales. Based on the customer attributes in the customer dimension and measures, it is possible to build a classification type mining model to analyze the customer information.
For example, a store manager might want to know the profile of customers who are interested in applying for a golden membership card. Based on historical product sales, a store manager might like to know projected future sales amounts. For example, what are the potential sales of all beverages in all stores in Washington state next month? Suppose that a store ordered a product — for example, a new kind of beer. The store manager wants to know which customers are most interested in buying this product.
He can apply data mining techniques to discover the profile of customers who are interested in buying beer and send mailings to those people with similar profiles. The OLAP mining model and relational mining model use the same set of data mining algorithms. Instead of being bound to table columns, the mining columns are bound to dimension attributes, measures, and measure groups. Because the OLAP cube contains precalculated aggregations, attributes bound to measures can be accessed very efficiently.
This information can be derived from relational tables as well; however, this requires extra data transformation steps. In addition to aggregated data, dimensions contain hierarchies in a cube, which define relationships among attributes. This hierarchy information can be used during data mining processing for attribute roll-up.
OLAP and Data Mining
In this chapter, we use OLAP mining model to refer those mining models built upon OLAP cubes and relational mining model to refer those mining models built upon relational data tables. The security features in Oracle have reached the highest levels of U. Oracle's fine grained access control feature, enables cell-level security for OLAP users.
Fine grained access control works with minimal burden on query processing, and it enables efficient centralized security management. The OLAP analytic engine, which supports the selection and rapid calculation of multidimensional data within the Oracle Database.
Analytic workspaces, which store data in a multidimensional format where it can be manipulated by the OLAP engine. Analytic Workspace Manager, a graphical user interface for creating and maintaining analytic workspaces. OLAP Catalog, the metadata repository which represents a star schema as a logical cube. OracleBI Discoverer Plus OLAP is a full featured tool for business analysis that provides a variety of presentation options including charts and graphs.
With Discoverer Plus OLAP you can create queries, drill, pivot, slice and dice data, add analytic calculations, chart the data, and export reports in various data formats. You can use the add-in to perform OLAP operations such as drilling, rotation, and data selection within a familiar spreadsheet environment. The Analytic Workspace API also supports XML representation of a logical multidimensional data model, which can be instantiated in the Database as an analytic workspace.
They include presentation beans, data beans, and persistence services. Tools for Administration A number of database administration tasks are involved in supporting the OLAP option in the database. One of the primary tasks is the management of analytic workspaces.
You can use either of the following tools: Analytic Workspace Manager provides a user interface for extracting data from a relational schema and creating an analytic workspace in database standard form. This form enables the analytic workspace to be used with various tools for modifying the logical model, loading new data, aggregating the data, and making the data accessible to OLAP applications. Oracle Warehouse Builder can extract data from many different sources, transform it into a relational schema, and create a standard form analytic workspace.
To further define the contents of the workspace, you can use Analytic Workspace Manager. Oracle Data Mining Overview Oracle Data Mining ODM uses data mining algorithms to sift through the large volumes of data generated by businesses to produce, evaluate, and deploy predictive and descriptive models. It enriches mission critical applications in CRM, manufacturing, inventory management, customer service and support, Web portals, wireless devices, and other fields with context-specific recommendations and predictive monitoring of critical processes.Date Warehousing and Data Mining
ODM finds valuable information that can help users better understand customers or clients and anticipate customer behavior. ODM insights can be revealing, significant, and valuable. For example, ODM can be used to Predict those customers likely to change service providers Discover the factors involved with a disease Identify fraudulent behavior Benefits of Data Mining in the Database Oracle Data Mining provides in-database mining that does not require data movement between the database and an external mining server, thereby eliminating redundancy, improving efficient data storage and processing, and maintaining data security.
Mining in the database makes it easier to mine up-to-date data.
There are several algorithms for each mining function. Oracle Data Mining supports the following data mining functions: Grouping items into discrete classes and predicting which class an item belongs to; classification algorithms are Decision Tree, Naive Bayes, Adaptive Bayes Network, and Support Vector Machine Regression: Approximating and forecasting continuous numerical values; the algorithm for regression is Support Vector Machine Attribute Importance: Identifying the attributes that are most important in predicting results; the algorithm for attribute importance models is Minimum Descriptor Length Unsupervised data mining: Finding natural groupings in the data; the algorithms for clustering are k-Means and O-Cluster Associations: Analyzing "market baskets", items that are likely to be purchased together; the algorithm for associations is A Priori Feature extraction: Creating new attributes features as a combination of the original attributes; the algorithm for Feature Extraction is Non-Negative Matrix Factorization In addition to mining structured data, ODM permits mining of one or more columns of text data.
Oracle Data Mining also supports specialized sequence search and alignment algorithms BLAST used to detect similarities between nucleotide and amino acid sequences. Oracle Data Mining Interfaces ODM provides extensive support for building applications that automate the extraction and dissemination of data mining insights.