Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
A guided tour of Chernoff bounds
Information Processing Letters
Pseudorandom generators for space-bounded computations
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Data streams: algorithms and applications
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Better algorithms for high-dimensional proximity problems via asymmetric embeddings
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Sublinear-time approximation of Euclidean minimum spanning tree
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Counting Distinct Elements in a Data Stream
RANDOM '02 Proceedings of the 6th International Workshop on Randomization and Approximation Techniques
Stable distributions, pseudorandom generators, embeddings and data stream computation
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Processing set expressions over continuous update streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Deterministic sampling and range counting in geometric data streams
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Faster core-set constructions and data stream algorithms in fixed dimensions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Range counting over multidimensional data streams
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Estimating the weight of metric minimum spanning trees in sublinear-time
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
On coresets for k-means and k-median clustering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Algorithms for dynamic geometric problems over data streams
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Comparing data streams using Hamming norms (how to zero in)
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Geometric optimization problems over sliding windows
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
Coresets in dynamic geometric data streams
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
A dip in the reservoir: maintaining sample synopses of evolving datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Random Sampling for Continuous Streams with Arbitrary Updates
IEEE Transactions on Knowledge and Data Engineering
A space-optimal data-stream algorithm for coresets in the plane
SCG '07 Proceedings of the twenty-third annual symposium on Computational geometry
Maintaining bernoulli samples over evolving multisets
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Shape sensitive geometric monitoring
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the twenty-fourth annual symposium on Computational geometry
Facility Location in Dynamic Geometric Data Streams
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Optimal sampling from sliding windows
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Streaming Embeddings with Slack
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
Estimating clustering indexes in data streams
ESA'07 Proceedings of the 15th annual European conference on Algorithms
1-pass relative-error Lp-sampling with applications
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Tight bounds for Lp samplers, finding duplicates in streams, and related problems
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Optimal sampling from sliding windows
Journal of Computer and System Sciences
Width of points in the streaming model
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Analyzing graph structure via linear measurements
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Continuous sampling from distributed streams
Journal of the ACM (JACM)
Graph sketches: sparsification, spanners, and subgraphs
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Don't let the negatives bring you down: sampling from streams of signed updates
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
SIAM Journal on Discrete Mathematics
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
Efficient sampling of non-strict turnstile data streams
FCT'13 Proceedings of the 19th international conference on Fundamentals of Computation Theory
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A dynamic geometric data stream is a sequence of m Add/Remove operations of points from a discrete geometric space (1,...,Δ)d [21]. Add(p) inserts a point p from (1,...,Δ)d into the current point set, Remove(p) deletes p from P. We develop low-storage data structures to (i) maintain ε-approximations of range spaces of P with constant VC-dimension and (ii) maintain an ε-approximation of the weight of the Euclidean minimum spanning tree of P. Our data structures use O(log3ε • log3(1/ε) • log(1/ε)/ε2) and O(log (1/δ) • (log Δ/ε)O(d)) bits of memory, respectively (we assume that the dimension d is a constant), and they are correct with probability 1-δ. These results are based on a new data structure that maintains a set of elements chosen (almost) uniformly at random from P.