Sunday, December 31, 2006

Too much data and not enough information, ORACLE Data Mining

Business Intillegence (BI) is now one of the important direction that our company REALSOFT is working in for both local and regional markets. Data warehousing, data mining, OLAP and visualization of spatial information are concept that lie beneath the umbrella of data warehousing. The ultimate goal is : Discover the knowledge hidden in your data to prepare for a more informed decision making

Data mining finds hidden patterns in large, complex collections of data, patterns that elude traditional statistical approaches to analysis.

I was following lately the advent Oracle products in the areana of data mining.
I felt that it has gone a long way.

Oracle Data Mining (ODM) embeds data mining within the Oracle database. There is no need to move data out of the database into files for analysis and then back from files into the database for storing. The data never leaves the database -- the data, data preparation, model building, and model scoring results all remain in the database. This enables Oracle to provide an infrastructure for application developers to integrate data mining seamlessly with database applications.

Data mining functions are based on two kinds of learning: supervised (directed) and unsupervised (undirected).
Supervised learning functions are typically used to predict a value, and are sometimes referred to as predictive models. Unsupervised learning functions are typically used to find the intrinsic structure, relations, or affinities in data but no classes or labels are assigned aprioi. These are sometimes referred to as descriptive models.

Oracle Data Mining supports the following data mining functions:

Predictive models (supervised learning):
Classification: grouping items into discrete classes and predicting which class an item belongs to
Regression: function approximation and forecast of continuous values
Attribute importance: identifying the attributes that are most important in predicting results (Java interface only)

Descriptive models (unsupervised learning):
Clustering: finding natural groupings in the data
Association models: "market basket" analysis
Feature extraction: create new attributes (features) as a combination of the original attributes
Multimedia (TEXT)
Bioinformatics (BLAST)

ODM provides single-user milt-session access to models. Model building is either synchronous in the PL/SQL interface or asynchronous in the Java interface.

Oracle Data Miner Release 10gR2 introduces the popular Decision Tree algorithm for classification problems that can provide human readable "IF...THEN..." rules that communicate the patterns discovered by ODM. The new Anomaly Detection algorithm flags rare events and supports fraud and compliance monitoring. Oracle Data Miner now supports mining multiple tables at once (e.g., star schema) and supports mining unstructured "text" data. Oracle Data Miner also supports PREDICT and EXPLAIN "one-click data mining" predictive analytics. Oracle Data Miner Release 2 adds Receiver Operating Characteristics support for model evaluation and tuning. Oracle Data Miner can automatically generate the Java and SQL components needed to transform the data mining steps into an integrated data mining/BI enterprise application. Lastly, a new Gateway to Oracle Discoverer enables data analysts to publish their results for viewing through Oracle Discoverer. With Oracle Data Miner and Oracle Data Mining, the data never leaves the database: all data movement is eliminated. In addition, Oracle Data Miner and Oracle Data Mining provide the security of the Oracle database. Click here for details. Click here for an image showing key Data Miner windows. Oracle Data Miner Release 10gR2 requires Oracle Data Mining (ODM) 10.2.
Aside of its parctical applications, Data Mining requires both mathematical models to be implemented by software algorithms, this is an attractive combination for a scientifically inclined person.

There are a some application for data mining in our area like

Retail business
Fraud (Credit Card)
Money laundrying

in addition to many scientific and engineering application

REALSOFT has delivered many projects and services in this field like

1) Jordan Housing and Urben Development
2) Jordan National Human Resrouce Develpment Labour Market
3) Mobilecom Diwan (Customer service management system)
4) Saudi Telcom (STC) Telemetrics and Teletraffic Switch Analysis
5) Al- Balaqa Governerate Water Information Management System (CARE International)
6) Oman Social and Economic Indicators Database (SED)
7) Arab Bank Risk Managment Operational Data Store (ODS)
8) Oman E-Census (Oman Dept of national Economy)
9) Libya NIDC (National Information and Documentation Center) project
10) Libya Census project (OLAP and GIS enabled)
11) Kuwait Census Project (OLAP and GIS)
12) Currently, Bahrain Ministry of Social Development (MOSD) (OLTP,OLAP and GIS)
13) others

We have a group of the finest Software Engineers, Architects, Designers, Developers, QA team around the area who was able to deliver all of these projects successfully.

Ammar Sajdi


ahmad saeed said...

I think Microsoft have also good data mining technology

Ammar said...


I also know the founder of the data mining techniques within SQL Server. His name VICE President at YAHOO. School mate of mine

Anonymous said...

who is my boyfriend cheating on me with

Ammar said...

data mining depends on profiling data, get me data about your boy friend and i will tell you more info than you would expect

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