Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Space-efficient online computation of quantile summaries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Approximate counting of inversions in a data stream
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Counting Distinct Elements in a Data Stream
RANDOM '02 Proceedings of the 6th International Workshop on Randomization and Approximation Techniques
Continuously Maintaining Quantile Summaries of the Most Recent N Elements over a Data Stream
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Medians and beyond: new aggregation techniques for sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Effective Computation of Biased Quantiles over Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Power-conserving computation of order-statistics over sensor networks
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Space efficient mining of multigraph streams
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Holistic aggregates in a networked world: distributed tracking of approximate quantiles
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Tributaries and deltas: efficient and robust aggregation in sensor network streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Approximate Processing of Massive Continuous Quantile Queries over High-Speed Data Streams
IEEE Transactions on Knowledge and Data Engineering
Space-efficient Relative Error Order Sketch over Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Space- and time-efficient deterministic algorithms for biased quantiles over data streams
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient quantile retrieval on multi-dimensional data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Adaptive spatial partitioning for multidimensional data streams
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
A Streaming Parallel Decision Tree Algorithm
The Journal of Machine Learning Research
An Ω(1/ε log 1/ε) space lower bound for finding ε-approximate quantiles in a data stream
FAW'10 Proceedings of the 4th international conference on Frontiers in algorithmics
| Hi-index | 0.00 |
A rank query is essentially to find a data element with a given rank against a monotonic order specified on data elements. It has several equivalent variations [8, 17, 30]. Rank queries over data streams have been investigated in the form of quantile computation. A &phis;-quantile (&phis; ∈ (0,1]) of a collection of N data elements is the element with rank [&phis;N] against a monotonic order specified on data elements. Rank and quantile queries have many applications [1, 3, 6, 7, 10, 14-16, 26, 27], including monitoring high speed networks, trends and fleeting opportunities detection in the stock market, sensor data analysis, Web ranking aggregation and log mining, etc. In these applications, they not only play very important roles in the decision making but also have been used in summarizing data distributions of data streams. The following example shows a popular tool to compare the distributions of two data sets (data streams).