Carnegie Mellon University
15-826 Multimedia Databases and Data Mining
Spring 2007 - C. Faloutsos
Final exam study guide

Reminders:

1. Exam duration: 3 hours, on Friday May 11, 8:30am-11:30, PH A18A (but double-check room)
2. All aids allowed, EXCEPT laptop (due to its wireless connection)
3. The exam will be comprehensive, with more emphasis on the material after the midterm
4. Several of the links are internal to CMU.
5. The reading list below is a slightly modified version of the original reading list. Namely, we added the paper on ICA, and we deleted the papers on approximate counting.

Required text

• [Textbook] C. Faloutsos Searching Multimedia Databases by Content, Kluwer Academic Press, 1996. Evaluation draft ( internal to CMU - gzipped postscript).

Recommended text

• [HK] Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.
• [PTVF] William H. Press Saul A. Teukolsky William T. Vetterling Brian P. Flannery Numerical Recipes in C Cambridge University Press, 1992, 2nd Edition. On-line evaluation copy
• Undergraduate DB textbook, for those who took a db class too long ago:
• • Raghu Ramakrishnan, Johannes Gehrke, "Database Management Systems," McGraw-Hill 2002 (3rd ed).

Foils:

In pdf, from the course schedule page.

A. Multimedia Indexing

• Primary key access methods
  • Litwin, W., (1980), Linear Hashing: A New Tool for File and Table Addressing, VLDB, Montreal, Canada, 1980.
  • Textbook, chapters 2-3
• Secondary key and spatial access methods
  • A. Guttman R-Trees: a Dynamic Index Structure for Spatial Searching, Proc. ACM SIGMOD, June 1984, pp. 47-57, Boston, Mass.
  • J. Orenstein, Spatial Query Processing in an Object-Oriented Database System, Proc. ACM SIGMOD, May, 1986, pp. 326-336, Washington D.C..
  • Textbook, chapters 4 and 5.
• Fractals
  • Ibrahim Kamel and Christos Faloutsos, Hilbert R-tree: An improved R-tree using fractals Proc. of VLDB Conference, Santiago, Chile, Sept. 12-15, 1994, pp. 500-509.
  • Christos Faloutsos and Ibrahim Kamel, Beyond Uniformity and Independence: Analysis of R-trees Using the Concept of Fractal Dimension, Proc. ACM SIGACT-SIGMOD-SIGART PODS, May 1994, pp. 4-13, Minneapolis, MN.
• Text and LSI
  • Textbook, chapter 6
  • Peter W. Foltz and Susan T. Dumais, Personalized Information Delivery: an Analysis of Information Filtering Methods, Comm. of ACM (CACM), 35, 12, Dec. 1992, pp. 51-60.
  • SVD: In PTVF ch. 2.6; Textbook Appendix D
• Time sequences
  • Textbook, chapters 7, 8 and 10.
• DSP and image databases
  • Myron Flickner, Harpreet Sawhney, Wayne Niblack, Jon Ashley, Qian Huang, Byron Dom, Monika Gorkani, Jim Hafner, Denis Lee, Dragutin Petkovic, David Steele and Peter Yanker Query by Image and Video Content: the QBIC System IEEE Computer 28, 9, Sep. 1995, pp. 23-32. (hard copy - on reserve)
  • Journal of Intelligent Inf. Systems, 3, 3/4, pp. 231-262, 1994 An earlier, more technical version of the IEEE Computer '95 paper.
  • FastMap: Textbook chapter 11; Also in: C. Faloutsos and K.I. Lin FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets ACM SIGMOD 95, pp. 163-174.
  • DFT/DCT: In PTVF ch. 12.1, 12.3, 12.4; in Textbook Appendix B.
  • Wavelets: In PTVF ch. 13.10; in Textbook Appendix C
  • Karhunen-Loeve: in Textbook Appendix D.
  • JPEG: Gregory K. Wallace, The JPEG Still Picture Compression Standard, CACM, 34, 4, April 1991, pp. 31-44
  • MPEG: D. Le Gall, MPEG: a Video Compression Standard for Multimedia Applications CACM, 34, 4, April 1991, pp. 46-58
  • Fractal compression: M.F. Barnsley and A.D. Sloan, A Better Way to Compress Images, BYTE, Jan. 1988, pp. 215-223. (hard copy: on reserve)
  • Textbook, chapter 9

B. Data mining

• Graph mining and social networks:
  • Michalis Faloutsos, Petros Faloutsos and Christos Faloutsos, On Power-Law Relationships of the Internet Topology, SIGCOMM 1999.
  • R. Albert, H. Jeong, and A.-L. Barabási, Diameter of the World Wide Web, Nature, 401, 130-131 (1999).
  • Réka Albert and Albert-László Barabási Statistical mechanics of complex networks, Reviews of Modern Physics, 74, 47 (2002).
• Time series forecasting
  • Byong-Kee Yi, Nikolaos D. Sidiropoulos, Theodore Johnson, H.V. Jagadish, Christos Faloutsos and Alex Biliris, Online Data Mining for Co-Evolving Time Sequences, ICDE 2000,  Feb. 2000.
• Statistics background: In PTVF pp. 620-621 and ch. 14.4-14.5;
• AI background / Classification
  • [HK] chapter 7.3
  • Rakesh Agrawal, Sakti Ghosh, Tomasz Imielinski, Bala Iyer and Arun Swami An Interval Classifier for Database Mining Applications VLDB Conf. Proc. Vancouver, BC, Canada, Aug. 1992, pp. 560-573.
  • M. Mehta, R. Agrawal and J. Rissanen, `SLIQ: A Fast Scalable Classifier for Data Mining', Proc. of the Fifth Int'l Conference on Extending Database Technology, Avignon, France, March 1996.
• Data Mining in Databases:
  • Data warehouses, OLAP and DataCubes: [HK], ch. 2.
  • Data reduction: [HK] chapter 3.4
  • Association Rules:
  • • [HK] chapter 6
    • Rakesh Agrawal, Tomasz Imielinski and Arun Swami Mining Association Rules Between Sets of Items in Large Databases Proc. ACM SIGMOD, Washington, DC, May 1993, pp. 207-216
  • Cluster analysis: [HK] chapter 8.
  • ***ADDED***: ICA: Jia-Yu Pan, Christos Faloutsos, Masafumi Hamamoto and Hiroyuki Kitagawa: AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases, PAKDD, Sydney, Australia, May 2004.

• Miscellaneous (approximate counting)
  • Christopher Palmer, Phillip Gibbons and Christos Faloutsos, ANF: A Fast and Scalable Tool for Data Mining in Massive Graphs, KDD 2002, Edmonton, Alberta, Canada, July 2002
  • Efficient and Tunable Similar Set Retrieval, by  Aristides Gionis, Dimitrios Gunopulos and Nikos Koudas, ACM SIGMOD,  Santa Barbara, California, May 21-24, 2001.
  • New sampling-based summary statistics for improving approximate query answers, by Phillip B. Gibbons and Yossi Matias, ACM SIGMOD, pp 331 - 342, Seattle, Washington, 1998.

Last modified May 1, 2007, by Christos Faloutsos