Y. Zhu, D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In Proc. Int. Conf. on Very Large Data Bases, 2002, pp. 358--369.  Home/Search Document Details and Download
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Flux: An Adaptive Partitioning Operator for.. - Shah, Hellerstein, .. (2002) (12 citations) (Correct)
....streams (one per group) and computes a statistic over the recent history of the extracted streams. Such an operator can be used, for example, in a network monitoring scenario to compute the average packet size per client over the most recent packets received. Variants of this operator exist [32] where both the statistic computed and the notion of recent history can vary. To isolate and illustrate the effects of Flux, we restrict ourselves to a simple hash based windowed group by that, on every new tuple, computes a statistic over a fixed size history. In terms of resource use, the ....
Y. Zhu and D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In VLDB, 2002. 15
SWAT: Hierarchical Stream Summarization in Large Networks - Bulut, Singh (2003) (8 citations) (Correct)
....Therefore, they propose two methods to tune the old buckets: wholesale approach and piecemeal approach. In the wholesale approach, the buckets are revised from scratch, while in the piecemeal approach, the existing bucket al..location is preserved whenever possible. A recent paper by Zhu and Shasha [17] considers the problem of monitoring a large number of streams in real time. The authors subdivide a sliding window into a fixed number of basic windows and maintain DFT coefficients for each basic window. This transforms each stream into a feature space consisting of a set of DFT coefficients. ....
.... 54 8 12 8 4 4 32 14 2 18 12 4 26 2 54 8 46 44 32 18 2 26 2 10 2 16 98 36 16 98 46 8 44 8 54 36 4 4 22 10 12 16 98 46 8 44 54 8 4 32 36 4 22 2 8 14 32 2 8 16 98 54 32 46 8 12 44 46 44 8 [3 18] 0 3] 0 7] 0 1] 1 8] 1 16] [2 17] [0 1] 4 19] 0 1] 5 20] 8 15] 4 7] 2 3] 1 2] 2 5] 4 11] 9 16] 5 8] 0 1] 2 3] 5 12] 1 4] 3 6] 1 2] 10 17] 2 9] 4 7] 0 3] 2 3] 0 1] 6 13] 2 5] 1 2] 11 18] 3 10] 5 8] 1 4] 2 3] 0 1] 7 14] 3 6] 1 2] 0 7] 8 15] 4 7] 0 3] 2 3] ....
[Article contains additional citation context not shown here]
Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB, 2002. 25
Flux: An Adaptive Partitioning Operator for.. - Shah, Hellerstein, .. (2002) (12 citations) (Correct)
....streams (one per group) and computes a statistic over the recent history of the extracted streams. Such an operator can be used, for example, in a network monitoring scenario to compute the average packet size per client over the most recent k packets received. Variants of this operator exist [31] where both the statistic computed and the notion of recent history can vary. To isolate and illustrate the effects of Flux, we restrict ourselves to a simple hash based windowed group by that, on every new tuple, computes a statistic over a fixed size history. In terms of resource use, the ....
Y. Zhu and D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In VLDB, 2002. 12
Efficient Elastic Burst Detection in Data Streams - Zhu, Shasha (2003) (2 citations) Self-citation (Zhu Shasha) (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002.
Efficient Elastic Burst Detection in Data Streams - Yunyue Zhu Courant (2003) (2 citations) Self-citation (Zhu Shasha) (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002.
Efficient Elastic Burst Detection in Data Streams - Yunyue Zhu Courant (2003) (2 citations) Self-citation (Zhu Shasha) (Correct)
....need to be considered, point monitoring can be implemented without much e#ort. Aggregate monitoring is much more challenging. Aggregates of time series are computed based on certain time intervals (windows) There are three well known window models that are the subjects of many research projects [10, 11, 12, 22]. 1. Landmark windows: Aggregates are computed based on the values between a specific time point called the landmark and the present. For example, the average stock price of IBM from Jan 1st, 2003 to today is based on a landmark window. 2. Sliding windows: In a sliding window model, aggregates ....
....window is recognized as an important model for data stream. Based on the sliding window model, previous research studies the computation of di#erent aggregates of data stream, for example, correlated aggregates [11] count and other aggregates[7] frequent itemsets and clusters[10] and correlation[22]. The work [13] studies the problem of learning models from timechanging streams without explicitly applying the sliding window model. The Aurora project[4] considers the systems aspect of monitoring data streams. Also the algorithm issues in time series stream statistics monitoring are addressed ....
[Article contains additional citation context not shown here]
Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002.
Fast Approaches to Simple Problems in Financial Time - Series Streams Yunyue Self-citation (Zhu Shasha) (Correct)
....some threshold, in which case, #g r 0 , #g r 1 , #g r c replicate the index. Because the inner product between two normalized time series is their correlation coe#cient, the problem of index replication can be reduced to finding the highly correlated time series. We have developed StatStream[6] for real time monitoring of correlations among time series streams. The goal of StatStream is to detect time series stream pairs with high correlation over sliding windows. For example, the user might ask, which pairs of stocks were correlated with a value of over 0.9 for the last hour We ....
Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002, pages 358--369.
Query by Humming: a Time Series Database Approach - Zhu, Shasha (2003) (3 citations) Self-citation (Zhu Shasha) (Correct)
....in their work was on whole sequence matching. This was generalized to allow subsequence matching [8, 22] Ra ei and Mendelzon [26] improved this technique by allowing transformations, including shifting, scaling and moving average, on the time series before similarity queries. In addition to DFT [2, 26, 35], Discrete Wavelet Transform (DWT) 7, 31, 24] Singular Value Decomposition (SVD) 17] Piecewise Aggregate Approximation (PAA) 32, 14] and Adaptive Piecewise Constant Approximation [15] approaches have also been proposed for similarity searching. Allowing Dynamic Time Warping (DTW) in time series ....
....reduction transform # will reduce it to a lower dimension X ) N n. X is also called the feature vector of x . After the time series are mapped to a lower dimensionality space, they can be indexed byamultidimensional index structure such as an R tree [4] or a grid le [35]. To guarantee no false negatives in similarity ###### ## ### #### ###### ############### ## ### #### #### ### ### ######### ##### #### ##### #### ### ########### ## ##### ###### ##### ##### ## #### ## ####### D Euclidean distance function x time series time series of length n x i the i#th ....
Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002.
StatStream: Statistical Monitoring of Thousands of Data Streams .. - Zhu, Shasha (2002) (25 citations) Self-citation (Zhu Shasha) (Correct)
.... Sliding winde (Hours) Figure 4: Average approximation errors for correlation coefficients with different basic sliding window sizes for synthetic(above) and real(below) datasets parts: detecting correlation and updating the digest (Figure 3b) More experimental results can be found in [29]. 5.2 Precision Measurement Because the approximate correlations are based on DFT curve fitting in each basic window, the precision of the computation depends on the size of the basic window. Our experiments on the two data sets (Figure 4) show that errors increase with larger basic window size ....
Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. Technical Report TR2002-827, New York University, CS Dept, New York, NY 10012, 2002.
Issues in Data Stream Management - Lukasz Golab And (2003) (20 citations) (Correct)
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Y. Zhu, D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In Proc. Int. Conf. on Very Large Data Bases, 2002, pp. 358--369.
Streaming Pattern Discovery in Multiple Time-Series - Papadimitriou, Sun, Faloutsos (2005) (Correct)
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Y. Zhu and D. Shasha. StatStream: Statistical monitoring of thousands of data streams in real time. In VLDB, 2002.
Duplicate Detection in Click Streams - Metwally, Agrawal, Abbadi (2005) (Correct)
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Y. Zhu and D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In Proceedings of the 28th ACM VLDB pages 358--369, 2002. 21
Duplicate Detection in Click Streams - Metwally, Agrawal, Abbadi (2005) (Correct)
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Y. Zhu and D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In Proceedings of the 28th ACM VLDB pages 358--369, 2002. 21
A Framework for Mining Instant Messaging Services - Resig, Teredesai (2004) (Correct)
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Zhu, Y., and Shasha, D. Statstream: Statistical monitoring of thousands of data streams in real time, 2002.
GreedyDual-Join - Locality-Aware Buffer Management for .. - Li, Chang, Bestavros, .. (2004) (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In Proc. of VLDB, 2002. 21
Resource Sharing in Continuous Sliding-Window Aggregates - Arasu, Widom (2004) (2 citations) (Correct)
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Y. Zhu and D. Shasha. StatStream: Statistical monitoring of thousands of data streams in real time. In Proc. of the 28th Intl. Conf. on Very Large Data Bases, pages 358--369, Aug. 2002. 347
GreedyDual-Join - Locality-Aware Buffer Management for .. - Li, Chang, Bestavros, .. (2004) (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In Proc. of VLDB, 2002. 21
Quality of Service Adaptation in Data Stream Management.. - Tu, Xia, Prabhakar (Correct)
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Y. Zhu and D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In Procs. of the 28th VLDB Conf., pages 358--369, August 2002.
Distributed Data Streams Indexing using Content-Based.. - Bulut, Vitenberg, Singh (2004) (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB, pages 358--369, 2002.
Online Amnesic Approximation of Streaming Time Series - Palpanas, Vlachos, Keogh, .. (2004) (4 citations) (Correct)
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Yunyue Zhu and Dennis Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In VLDB International Conference, pages 358--369, Hong Kong, China, August 2002. 12
Using Triangle Inequality to Efficiently Process - Continuous Queries On (Correct)
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Yunyue Zhu and Dennis Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In Proc. of VLDB, 2002.
Internet-Scale Information Monitoring: A Continual Query Approach - Tang (2003) (Correct)
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Zhu, Y. and Shasha, D., "Statstream: statistical monitoring of thousands of data streams in real time," in Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), (Hong Kong, China), 2002.
Primitives for Online Time Series Analysis - December Summary Data (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002.
Primitives for Online Time Series Analysis - December Summary Data (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002.
A Rollback-Recovery Protocol for Wide Area Pipelined Data Flow .. - Smith, Watson (2004) (Correct)
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Yunyue Zhu and Dennis Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In International Conference on Very Large Databases, Hong Kong, China, August 2002. Morgan Kaufmann.
Efficient Evaluation of Composite Correlations - For Streaming Time (Correct)
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D. S. Yunyue Zhu. Statstream: Statistical monitoring of thousands of data streams in real time. In Proceedings of the 28th International Conference on Very Large Data Bases, pages 358--369, 2002.
Identifying Frequent Items in Sliding Windows over - On-Line Packet Streams (2003) (Correct)
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Y. Zhu and D. Shasha. StatStream: Statistical monitoring of thousands of data streams in real time. Proc. 28th Int. Conf. on Very Large Data Bases, pages 358--369, 2002.
Primitives for Online Time Series Analysis - December The Vision (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002.
Paper Reference No.: 192 - Authors Contact Author (2003) (Correct)
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Y. Zhu and D. Shasha. Statstream: Statistical monitoring of thousands of data streams in real time. In VLDB, pages 358--369, 2002.
Online Amnesic Approximation of Streaming Time Series - Palpanas, Vlachos, Keogh, .. (2004) (4 citations) (Correct)
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Yunyue Zhu and Dennis Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In VLDB International Conference, pages 358--369, Hong Kong, China, August 2002. 12
Ecient Evaluation of Composite Correlations - For Streaming Time (Correct)
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D. S. Yunyue Zhu. Statstream: Statistical monitoring of thousands of data streams in real time. In Proceedings of the 28th International Conference on Very Large Data Bases, pages 358#369, 2002.
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