Carnegie Mellon University
15-826 Multimedia Databases and Data Mining
Spring 2006 - C. Faloutsos

Lecture foils




Lecture date Foils Topic
1/17
010_intro-db.pdf overview of relational and OR-DBMS
1/17
020_b-trees.pdf primary key indexing: b-trees
1/19
030_hashing.pdf                                  :hashing (static and extendible)
1/24
110_SAMs1.pdf multi-key and spatial access methods - part I
1/24
120_SAMs2.pdf spatial access methods: z-ordering 
1/31
130_SAMs3.pdf                                    : R-trees 
2/2, 2/7
140_SAMs4.pdf                                     : grid files, dim. curse
2/7
150_SAMs5.pdf                                     : metric trees, nn search algorithms
2/9-14
160_fractals1.pdf  fractals: introduction (fd, multifractals, fat fractals, power laws)
2/16
170_fractals2.pdf            : case studies - part-I (r-tree performance; dim. reduction)
2/21
180_fractals3.pdf            : case studies - part-II (dim. curse, M-trees analysis)
2/23
190_fractals4.pdf            : case studies - part III (regions, quadtrees)
3/2
210_text1.pdf text    : full text scanning
3/2
220_text2.pdf text    : part-II: inversion; signature files
3/7
230_text3.pdf text    : part-III: clustering
3/9
240_text4.pdf text    : part-IV: LSI
3/9
250_SVD1.pdf SVD  : part-I: definitions
3/9, 3/21
260_SVD2.pdf           : part-II: case studies
3/21, 3/23
270_SVD3.pdf, spirit.pdf           : part-III: properties and case studies
3/28
310_multimediaDB.pdf multimedia indexing (time series, images, FastMap)
3/30
320_DSP.pdf DSP tools: Fourier and wavelets
4/4
330_JPEG.pdf compression: JPEG, MPEG and fractal
4/6 410_TimeSeriesForecasting.pdf Time Series: sensor mining and forecasting
4/11
420_GraphMining1.pdf Graph Mining - part1: Laws, generators
4/13 430_GraphMining2.pdf Graph Mining - part2: HITS, PageRank, virus propagation
4/18
510_DM-stat.pdf Data Mining: Statistics basics
4/18
520_DM-AI.pdf Data Mining: AI basics 
4/25
530_DM-OLAP.pdf Data Mining: OLAP and Data Warehousing
4/25
540_DM-Trees+Rules.pdf Data Mining: DB concepts - trees and association rules
5/2
550_DM-clustering.pdf Data Mining: DB concepts - clustering
5/2
610_appxCountingPalmer.pdf
Approximate Counting
5/4
700_Conclusions.pdf
Overall conclusions


Last modified 5/1/2006 by Christos Faloutsos