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Murad Akhrarov

from Clayton, NC
Age ~69
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Also known as:
Murad V
Connected to:
Eyad Akhras, 46Almira A Akhremenko, 64Nazila Akhshangfard, 59Fatimah Akhtab, 40Abbas Akhtar, 75Adeela Akhtar, 41Afaque E Akhtar, 58Ayaz Akhtar, 64Aysha Akhtar, 56Aziz N Akhtar, 39

Murad Akhrarov Phones & Addresses

  • Clayton, NC
  • Kirkland, WA
  • Santa Clara, CA
  • Bellevue, WA
  • San Mateo, CA
  • Burlingame, CA
  • 6504 130Th Ave NE APT L204, Kirkland, WA 98033 (650) 627-8067

Work

Company: Microsoft Apr 2008 Position: Software engineer

Education

Degree: PhD School / High School: Lebedev Institute of Physics 1980 to 1986 Specialities: Laser Physics

Languages

English • Russian

Industries

Information Technology and Services

Resumes

Resumes

Murad Akhrarov Photo 1

Software Developer @ Microsoft

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Position:
Software Engineer at Microsoft
Location:
Greater Seattle Area
Industry:
Information Technology and Services
Work:
Microsoft since Apr 2008
Software Engineer
iSagacity, Inc Apr 2006 - Apr 2008
Software Manager
Education:
Lebedev Institute of Physics 1980 - 1986
PhD, Laser Physics Samarkand Aliser Navoi Nomidagi Universiteti 1970 - 1975
Languages:
English
Russian

Publications

Us Patents

Method For Analyzing And Classifying Process Data

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US Patent:
20100030521, Feb 4, 2010
Filed:
Feb 13, 2008
Appl. No.:
12/030437
Inventors:
Murad Akhrarov - Santa Clara CA,
Joe Bates - Joliet IL,
Peter Millett - Bremen ME,
Chanchal Sharma - Lee NH,
International Classification:
G06F 15/00
US Classification:
702182, 702188
Abstract:
Process data mining system and method. The system analyzes data from complex process plants or systems and operates in open-book and closed-book modes. In closed-book mode, the system monitors incoming data sets against pre-defined clusters of data values and generates reports, indicating whether incoming data is a match or a no-match with the pre-defined clusters. In open-book mode, the system generates initial clusters, without having a-priori knowledge of the component or process, and also creates clusters “on the fly”, thereby fine-tuning the analysis. A knowledge base encompasses a combination of parameters for a particular component. Clusters are defined within the knowledge base, each cluster representing a particular operating condition. The system expands clusters, within pre-defined limits, or creates new clusters, as needed, in order to accommodate incoming data values. Newly created clusters are then named, so as to indicate the particular operating conditions.
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Murad Akhrarov from Clayton, NC, age ~69 Get Report