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By Guido Deutsch
 Cloud Mining für CRM Cloud Mining is a new approach to apply Data Mining to customer data. This article introduces Cloud Mining in a quick overview.
Data Mining is a determined technique to analyse Data in CRM, Marketing and Distribution. For example it helps optimizing customer interaction, shows buying potentials of customers and the churn probability by the use of statistical-mathematical methods on big amounts of data. Thereby companies can make marketing efforts more precise – they spendings less and achieve better effects.
Continue reading Cloud Mining – CRM Data Mining in the Cloud
By Guido Deutsch
 Diligent Data Mining Bot! Applying Data Mining with only SQL is considered very rare, but it is actually possible to solve some problems. As you can read in the article A Genetic Algorithm Sample in T-SQL by William Talada, it can solve a quite interessting problem.
A field with 10×10 squares is covered to the half with empty cans, it is bordered by a impassable wall. Now a poor tiny robot is send to clear the whole field. He can walk in any direction or pick up a can. His view is reduced to 5 squares, the one he stands on am the four fields that adjoin.
Continue reading Genetic Algorithms with T-SQL
By Guido Deutsch
In 1996 Osama Fayyad proposed a very popular process how to make a companies data useful for business needs. Data Mining is described to be a part of the KDD Process, actually quite small in this definition, but very important. After reading this article, you will understand why Data Mining needs a pre- and post-processing and just can’t stand alone against all the Data.
The approach to gain knowledge out of a set of data was separated by Fayyad into individual steps. The individuality results out of different tools you use, and different outcomes that are needed.
 KDD process by Fayyad 1996 The KDD Process stands for the Knowledge Discovery in Databases. According to Fayyad there are five steps: Selection, Pre-processing, Transformation, Data Mining and Interpretation. These five steps are passed through iteratively. Every step can be seen as a work-through phase. Such a phase requires the supervision of a user and can lead to multiple results. The best of these results is used for the next iteration, the others should be documented. In the following, the steps will be briefly described.
Continue reading Data Mining in the KDD Environment
By Guido Deutsch
 Screenshot MASSIVE Software MASSIVE (short for Multiple Agent Simulation System in Virtual Environment) should be quite well known to all the cinema-enthusiastics and Lord of the Rings-Fans – at least the stunning result when it comes to huge crowded war mass scenes. But not necessarily to the Data Mining friend. New Zealands’ special effects company WETA developed this nice piece of software and left an impressing first fingerprint in the world of cinema.
The software is exciting, because every single simulated agent is driven by his own Fuzzy Logic individually, as if he would make real decisions. The agents decides out of a given set of information, if he runs away or fights. Running away can be an option even for a mighty uruk hai-orc, especially if everyone else does it …
Continue reading Massive Prospects
By Guido Deutsch
 Typical Data Mining task: Looking for a needle in a haystack! To improve your Data Mining result when only having a small amount of target variables, it is useful to oversample the target variable. It is shown here how this works – and how to undo it when dealing with the result.
In cases where the target variable appears in a fraction of less then 10%, it is common to stratify the occurrence of the target variable. That should improve the result of your Data Mining challange. The term “oversampling” is used by SAS in their Enterprise Miner Software, to higher the relative occurence of the target variable without using copies – but by reducing the occurence of the non-target variable. Be advised that “oversampling” is also called to duplicate the content – you should check that out at zyxos Blog. We will stick to the quite simple view of SAS.
Continue reading Overrepresentation – “SAS”-Oversampling
By Guido Deutsch
 Location: Beijing, P.R. China
Date: October 14-17, 2010
to the Website
Topics: (but not limited to)
- Emerging Technology for Database
Systems
- Data Management on Cloud
Mining Infrastructures
- Web Data Management
- Query Processing and Optimization
- Stream Data Management
- XML and Semi-Structured Data Mining
- Data Warehouse and On-Line
Analytic Processing
- Approximate and Uncertain Data Management
- Content and Knowledge Management
- Data Mining and Knowledge
Discovery
- Meta-data Management
- Data Integration and Migration
- Embedded Database Systems and Mobile Data Management
- Parallel and Distributed Database
Systems
- Domain-Specific Database Systems
- Self-Managing Technology in DBMS
- Intelligent User Interface Technology
- Spatial and Temporal Database
Systems
- Multimedia Data Management
- Data Privacy and Security
- Information Retrieval and
Databases
By Guido Deutsch
 As reported by Katie Brooks from cloudcomputing.sys-con.com, there will be a new Virtual Private Data Center (VPDC) platform from Layered Technologies. The provider of on-demand IT infrastructure tries to overcome users concerns if their data is safe by having a enterprise level security standart. It will be possible to “design, order and deploy a secure virtualized environment within an hour”. Customers can choose whatever plattform they need, including 3Tera’s AppLogic, VMware and Microsoft Hyper-V. This might be a good SaaS Provider for cloud mining, the Data Mining of the future.
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Data Mining – Blog.de