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/*              INFO LIST CHUNKS (from the Multimedia Programmer's Reference
                                        plus new ones)
*/
#define RIFFINFO_IARL      mmioFOURCC ('I', 'A', 'R', 'L')     /*Archival location  */
#define RIFFINFO_IART      mmioFOURCC ('I', 'A', 'R', 'T')     /*Artist  */
#define RIFFINFO_ICMS      mmioFOURCC ('I', 'C', 'M', 'S')     /*Commissioned  */
#define RIFFINFO_ICMT      mmioFOURCC ('I', 'C', 'M', 'T')     /*Comments  */
#define RIFFINFO_ICOP      mmioFOURCC ('I', 'C', 'O', 'P')     /*Copyright  */
#define RIFFINFO_ICRD      mmioFOURCC ('I', 'C', 'R', 'D')     /*Creation date of subject  */
#define RIFFINFO_ICRP      mmioFOURCC ('I', 'C', 'R', 'P')     /*Cropped  */

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep