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Distributed Streams Algorithms for Sliding Windows

Phillip B. Gibbons, Srikanta Tirthapura
2004 Theory of Computing Systems  
This paper presents algorithms for estimating aggregate functions over a "sliding window" of the N most recent data items in one or more streams. Our results include: 1.  ...  We also present the first ( , δ)approximation scheme for the number of distinct values in a sliding window over distributed streams that uses only logarithmic memory words.  ...  Algorithms for Distributed Streams.  ... 
doi:10.1007/s00224-004-1156-4 fatcat:ffznllggdrc77ivzdsanddkcki

Distributed streams algorithms for sliding windows

Phillip B. Gibbons, Srikanta Tirthapura
2002 Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures - SPAA '02  
This paper presents algorithms for estimating aggregate functions over a "sliding window" of the N most recent data items in one or more streams. Our results include: 1.  ...  We also present the first ( , δ)approximation scheme for the number of distinct values in a sliding window over distributed streams that uses only logarithmic memory words.  ...  Algorithms for Distributed Streams.  ... 
doi:10.1145/564870.564880 dblp:conf/spaa/GibbonsT02 fatcat:uxmasq6inbautmdnpx7elnhwre

Distributed streams algorithms for sliding windows

Phillip B. Gibbons, Srikanta Tirthapura
2002 Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures - SPAA '02  
This paper presents algorithms for estimating aggregate functions over a "sliding window" of the N most recent data items in one or more streams. Our results include: 1.  ...  We also present the first ( , δ)approximation scheme for the number of distinct values in a sliding window over distributed streams that uses only logarithmic memory words.  ...  Algorithms for Distributed Streams.  ... 
doi:10.1145/564879.564880 fatcat:sr2djoa4cvax7bkhnbkmcsjerm

Stream Classification Algorithm Based on Decision Tree

Jinlin Guo, Haoran Wang, Xinwei Li, Li Zhang, Fazlullah Khan
2021 Mobile Information Systems  
to adjust the size of the sliding window.  ...  In this respect, a dynamic stream data classification algorithm is proposed for the stream data.  ...  In the stream data processing algorithm based on the sliding window, the sliding window size is fixed or only changes with the concept drift.  ... 
doi:10.1155/2021/3103053 fatcat:7qe3vjnjondive7gupskf4jzqe

Sliding windows over uncertain data streams

Michele Dallachiesa, Gabriela Jacques-Silva, Buğra Gedik, Kun-Lung Wu, Themis Palpanas
2014 Knowledge and Information Systems  
We extend the semantics of sliding window to define the novel concept of uncertain sliding windows and provide both exact and approximate algorithms for managing windows under existential uncertainty.  ...  Keywords Data stream processing · Sliding windows · Uncertainty management Introduction The strong demand for applications that continuously monitor the occurrence of interesting events (e.g., road-tunnel  ...  Algorithms such as finding quantiles, heavy hitters, and frequent itemsets over sliding windows can be extended to support uncertain data streams by using our proposal as a foundation for more advanced  ... 
doi:10.1007/s10115-014-0804-5 fatcat:cloe7aktpzhp7ohb6dvawvyj7e

An Exploration of Online Missing Value Imputation in Non-stationary Data Stream

Wenlu Dong, Shang Gao, Xibei Yang, Hualong Yu
2021 SN Computer Science  
Moreover, the impact of time window size has also been investigated for guiding the parameter settings in future practical applications.  ...  Meanwhile, two slide time window-based strategies are proposed to alleviate this impact, where one is the plain average strategy, and the other is the logarithmic weighted average strategy that gradually  ...  That means the proposed slide time window-based strategies are virtually useful for missing data imputation in the drifting data stream. • For the baseline strategy, the imputation error relies on how  ... 
doi:10.1007/s42979-021-00459-1 fatcat:56i4lnibvfbtfoyh46ligv2ksi

Sliding Window Top-K Monitoring over Distributed Data Streams [chapter]

Zhijin Lv, Ben Chen, Xiaohui Yu
2017 Lecture Notes in Computer Science  
We also develop a framework that combines a distributed data stream monitoring architecture with a sliding window model.  ...  This paper studies how to monitor the top-k data objects with the largest aggregate numeric values from distributed data streams within a fixed-size monitoring window W, while minimizing communication  ...  Top-K Monitoring Algorithm In this section, we describe our algorithm in detail for sliding window top-k monitoring over distributed data streams.  ... 
doi:10.1007/978-3-319-63579-8_40 fatcat:jeanid6a5fcpzla7f2fho2eyta

Sliding Window Top-K Monitoring over Distributed Data Streams

Ben Chen, Zhijin Lv, Xiaohui Yu, Yang Liu
2017 Data Science and Engineering  
We also develop a framework that combines a distributed data stream monitoring architecture with a sliding window model.  ...  This paper studies how to monitor the top-k data objects with the largest aggregate numeric values from distributed data streams within a fixed-size monitoring window W, while minimizing communication  ...  Top-K Monitoring Algorithm In this section, we describe our algorithm in detail for sliding window top-k monitoring over distributed data streams.  ... 
doi:10.1007/s41019-017-0053-1 fatcat:af7khjwzk5cudbrzz4kgksn7eu

Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size

V. SiddaReddy, T.V. Rao, A.Govardhan A.Govardhan
2014 International Journal of Computer Applications  
By the motivation gained from our earlier proposed models, here we devised a novel closed frequent itemset mining model for high speed distributed data streams.  ...  The said model is referred as Parallel Closed Frequent Itemsets Mining (PCFIM) over High Speed Distributed Data streams by Manifold Varying Size Windows (MVSW).  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their valuable comments. We also thank the authors of all references for helping us setup the paper.  ... 
doi:10.5120/17662-8479 fatcat:ykuilwrrpfbqbipdnivignntx4

Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection

Guang Li, Jie Wang, Jing Liang, Caitong Yue
2018 Symmetry  
The proposed algorithm SNWCAD is compared with Automatic Outlier Detection for Data Streams (A-ODDS) and Distance-Based Outline Detection for Data Stream (DBOD-DS).  ...  Since data stream anomaly detection algorithms based on sliding windows are sensitive to the abnormal deviation of individual interference data, this paper presents a sliding nest window chart anomaly  ...  The nested sliding window data stream anomaly detection algorithm is outlined below.  ... 
doi:10.3390/sym10040113 fatcat:e2t2ygn6dzhjjiqjirwfrv54ma

Querying Sliding Windows Over Online Data Streams [chapter]

Lukasz Golab
2004 Lecture Notes in Computer Science  
We outline previous work in streaming query processing and sliding window algorithms, summarize our contributions to date, and identify directions for future work.  ...  Processing continuous queries over data streams introduces a number of research problems, one of which concerns evaluating queries over sliding windows defined on the inputs.  ...  In [14] , we proposed three statistical models for the distribution of item types in data streams, and presented algorithms for finding frequent items in sliding windows with multinomially-distributed  ... 
doi:10.1007/978-3-540-30192-9_1 fatcat:wsecmjqfxbcfjl5pm4lpiyeyo4

Efficient Processing of ContinuousSkylineQuery over Smarter Traffic Data Stream for Cloud Computing

Wang Hanning, Xu Weixiang, Jiulin Yang, Lili Wei, Jia Chaolong
2013 Discrete Dynamics in Nature and Society  
Besides, we proposedmMR-SUDSalgorithm (Skylinequery algorithm of uncertain transportation stream data based onmicro-batchinMap Reduce) based on sliding window division and architecture.  ...  Strong computing ability and valid mass data management mode provided by the cloud computing, is feasible for handlingSkylinecontinuous query in the mass distributed uncertain transportation data stream  ...  distributed uncertain transportation data stream and provides formal description. (3) This research develops an mMR-SUDS algorithm based on sliding window division and the architecture proposed.  ... 
doi:10.1155/2013/209672 fatcat:wr6jler3wrdhfgwm64mi2vh6zm

Challenges and Issues in DATA Stream: A Review

Muhammad Arif, Khubaib Amjad Alam, Mehdi Hussain
2015 International Journal of Hybrid Information Technology  
, for business improvement and other applications where data arrived in stream.  ...  Memory usage for mining data stream should be limited due to the new data elements are continuously generated from the streams.  ...  So for this purpose introduce an algorithm named MFI-Trans SW so working of this algorithm in sensitive sliding window having three phase 1stinitialize the window, 2nd widow sliding and 3rd and last phase  ... 
doi:10.14257/ijhit.2015.8.3.15 fatcat:yi2rnr2mhzgatftrw75eugzkoy

The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection

Guang Li, Jie Wang, Jing Liang, Caitong Yue
2018 Symmetry  
Compared with automatic outlier detection for data streams (A-ODDS) and with sliding nest window chart anomaly detection based on data streams (SNWCAD-DS), the DCUSUM-DS can account for concept drift and  ...  This paper proposes a data stream anomaly detection algorithm combined with control chart and sliding window methods.  ...  Related Work Sliding Window There are three kinds of window methods for the data stream: landmark [25] , snapshot [26] , and sliding window [2] .  ... 
doi:10.3390/sym10070264 fatcat:4xavlltw2jdhzdmjcbdnlgtwnq

Incremental maintenance of maximal cliques in a dynamic graph

Apurba Das, Michael Svendsen, Srikanta Tirthapura
2019 The VLDB journal  
We present the first communication-efficient distributed algorithms for tracking persistent items in a data stream whose elements are partitioned across many different sites.  ...  We consider both infinite window and sliding window settings, and present algorithms that can track persistent items approximately with a probabilistic guarantee on the approximation error.  ...  window, per the distinct counting algorithm for sliding windows.  ... 
doi:10.1007/s00778-019-00540-5 fatcat:lnk5zuge4bcuveklilfxmhpymy
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