High-performance OLAP and Data Mining Systems,Techniques and Applications

6 June, 2016

Título:High-performance OLAP and Data Mining Systems,Techniques and Applications
Prof. Alok Choudhary
Department of Electrical Computing Engineering
Nothwestern University
Chicago, EE.UU.
Fecha impartición: 20 y 21 de marzo de 2002.
Horario: de 16 a 21 horas.
Lugar: Escuela Politécnica Superior Universidad Carlos III. Leganés, Madrid
Edificio Torres Quevedo.
Aula: 40F16.

Material del seminario:


Short biography of the autor:

Prof. Alok Choudhary got his Ph.D. at the University of Illinois, Urbana-Champaign, in 989. He is currently a full Professor, ECE Department in the Northwestern University, Chicago, USA. He has got several awards, including the NSF Young Investigator Award, in 1993, the IBM Faculty Development Award, in 1994, the Intel Faculty Research Award, in 1993, and the IEEE Engineering Foundation Award, in 1990. His research interest includes compilers and Runtime Systems for High-Performance, embedded and adaptive computing systems and power-aware systems, High-Performance Databases, OLAP and datamining and Parallel and distributed Input-Output for Scientific and Information Processing Applications.

Abstract of the course:

OLAP and data mining are integral parts of any decision support process. Yet, to date, most OLAP systems have focused on providing “access” to multi-dimensional data, while data mining systems have dealt with “influence analysis” of data along a single dimension. In this article, I show that OLAP and data mining should not remain separate components of decision support, but should be fully merged – they are inherently related activities that re-inforce (and desperately need) each other. This course focuses on both topics and their relations, specifically to deal with Large-Scientific datasets.


1.Data Mining

2.On-Line-Analytical Processing

3.Data Warehousing (basic Design and Design for E-commerce Click stream)

4.Parallel OLAP

5.Parallel Data Mining

6.High-Performance Datawarehousing

7.Applications of the above technologies in various domain

8.New Approaches to dealing with Large-Scientific datasets

9.Large-scale Scientific data analysis