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
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Bottom-up computation of sparse and Iceberg CUBE
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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
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Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
How to summarize the universe: dynamic maintenance of quantiles
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Diamond in the rough: finding Hierarchical Heavy Hitters in multi-dimensional data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
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Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
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ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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WWW '05 Proceedings of the 14th international conference on World Wide Web
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Foundations and Trends® in Theoretical Computer Science
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ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Analysis and Interpretation of Visual Hierarchical Heavy Hitters of Binary Relations
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
SLEUTH: Single-pubLisher attack dEtection Using correlaTion Hunting
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Computers & Mathematics with Applications
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Information Processing Letters
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APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
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Computer Networks: The International Journal of Computer and Telecommunications Networking
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ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
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ICDT'05 Proceedings of the 10th international conference on Database Theory
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ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
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ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
International Journal of Sensor Networks
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Aggregation along hierarchies is a critical summary technique in a large variety of on-line applications including decision support and network management (e.g., IP clustering, denial-of-service attack monitoring). Despite the amount of recent study that has been dedicated to online aggregation on sets (e.g., quantiles, hot items), surprisingly little attention has been paid to summarizing hierarchical structure in stream data. The problem we study in this paper is that of finding Hierarchical Heavy Hitters (HHH): given a hierarchy and a fraction φ, we want to find all HHH nodes that have a total number of descendants in the data stream no smaller than φ of the total number of elements in the data stream, after discounting the descendant nodes that are HHH nodes. The resulting summary gives a topological "cartogram" of the hierarchical data. We present deterministic and randomized algorithms for finding HHHs, which builds upon existing techniques by incorporating the hierarchy into the algorithms. Our experiments demonstrate several factors of improvement in accuracy over the straightforward approach, which is due to making algorithms hierarchy-aware.