© 2003-2014, Ludmila Zamiatina
(This is a two-sided iso-area mask, made out of single traditional 6” Origami square. One side is my portrait as a young man – the other side is my portrait now. Published with a kind permission of my wife Ludmila.)
I am passionate about all kinds of mathematical algorithms. My background is in pure mathematics, but I had been active in mathematical software development and publishing for more than 10 years before joining Microsoft.
After working for more than five years on the core Algebra and Calculus features of Mathematica® (http://www.wolfram.com) I went on to work as a principal contractor developing an algebraic application that has been subsequently published as firmware for Casio® Algebra FX scientific calculator and Casio® ClassPad 300.
The following paper on stability of time series forecasting has been presented at Data Mining and Information Engineering 2006 Conference and published in the Proceedings (“Data Mining VII”, WIT Press, 2006, pp.141 – 150)
In technical terms the cases of long range forecasting instability are characterized by rapid growth of mean absolute prediction error with time, which may or may not be accompanied by significant growth of predicted standard deviation. In practice the cases of instability where predict StDev stays tame are especially misleading, since they can furnish unreliable predictions with little or no visual cues that would characterize them as unreliable. The method described in the paper is designed to detect and control the long range forecasting instabilities and to cull the unreliable predictions.
The two main functions of data mining are classification and prediction (or forecasting). Data mining helps you make sense of those countless gigabytes of raw data stored in databases by finding important patterns and rules present in the data or derived from it. Analysts then use this knowledge to make predictions and recommendations about new or future data. The main business applications of data mining are learning who your customers are and what they need, understanding where the sales are coming from and what factors affect them, fashioning marketing strategies, and predicting future business indicators.
With the release of SQL Server 2000, Microsoft rebranded OLAP Services as Analysis Services to reflect the addition of newly developed data-mining capabilities. The data-mining toolset in SQL Server 2000 included only two classical analysis algorithms (Clustering and Decision Trees), a special-purpose data-mining management and query-expression language named DMX, and limited client-side controls, viewers, and development tools.
SQL Server 2005 Analysis Services comes with a greatly expanded set of data-mining methods and an array of completely new client-side analysis and development tools designed to cover most common business intelligence (BI) needs. The Business Intelligence Framework in SQL Server 2005 offers a new data-mining experience for analysts and developers alike.