Topic  Paper(s) 
Basic histogram building, aggregate model (including
orthogonality and Haar wavelets and onedimensional range
queries) 
Approximate Histogram and
Wavelet Summaries of Streaming Data, draft book chapter,
S. Muthukrishnan and M. Strauss. 
Histograms under Nonuniform workload 
WorkloadOptimal Histograms on Streams,
S. Muthukrishnan, M. Strauss, and X. Zheng. 
Basic sketches (randomized linear projections) and their use
in histograms for dynamic data  Class notes 
Countmin sketch  An Improved Data
Stream Summary: The CountMin Sketch and its Applications,
G. Cormode and S. Muthukrishnan. 
Quantiles 
 M. Greenwald, S. Khanna. ``SpaceEfficient Online Computation of
Quantile Summaries'', Proceedings of the 2001 ACM SIGMOD
Intl. Conference on Management of Data, pp. 5866, Santa Barbara, CA,
May 2124, 2001.
See here.

How to Summarize
the Universe: Dynamic Maintenance of Quantiles, A. Gilbert,
Y. Kotidis, S. Muthukrishnan and M. Strauss.

Clustering 
 Clustering Data Streams: Theory and Practice
(S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O'callaghan)
See here.
 Moses Charikar, Liadan O'Callaghan, Rina Panigrahy: Better
streaming algorithms for clustering problems. STOC 2003:
3039. See here.

Sliding windows and decay 
 Maintaining Stream Statistics over Sliding Windows,
M. Data, A. Gionis, P. Indyk, and R. Motwani.
See here.
 Maintaining timedecaying stream
aggregates, E. Cohen and M. Strauss, PODS,
2003.

Sensor networks 
 Samuel Madden, Michael J. Franklin, and Joseph M. Hellerstein, Wei
Hong:
TAG: A
Tiny AGgregation Service for AdHoc Sensor Networks
 Suman Nath, Phillip B. Gibbons, Srinivasan Seshan, Zachary
R. Anderson: Synopsis Diffusion
for Robust
Aggregation in Sensor Networks

Edith Cohen, Haim Kaplan: Spatiallydecaying aggregation over a
network: model and algorithms. SIGMOD Conference 2004: 707718.
See here.
 More to be determined; see below.
