Multi-query optimization of sliding window aggregates by schedule synchronization

Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06 ◽

10.1145/1183614.1183759 ◽

2006 ◽

Author(s):

Lukasz Golab ◽

Kumar Gaurav Bijay ◽

M. Tamer Özsu

Keyword(s):

Query Optimization ◽

Sliding Window ◽

Schedule Synchronization

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Multi-Query Optimization of Incrementally Evaluated Sliding-Window Aggregations

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2020.3029770 ◽

2020 ◽

pp. 1-1

Author(s):

Anatoli Shein ◽

Panos K. Chrysanthis

Keyword(s):

Query Optimization ◽

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Sliding Window-Based Transmit Antenna Selection Technique for Large-Scale MU-MIMO Networks

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e97.a.1640 ◽

2014 ◽

Vol E97.A (7) ◽

pp. 1640-1641 ◽

Author(s):

Tae-Won BAN ◽

Bang Chul JUNG

Keyword(s):

Large Scale ◽

Antenna Selection ◽

Sliding Window ◽

Transmit Antenna Selection ◽

Selection Technique ◽

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Query Optimization: Fund Data Generation Applying NonClustered Indexing and MapReduced Data Cube Numerosity Reduction Method

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/1991.12020 ◽

2020 ◽

Vol 9 (1.1 S I) ◽

pp. 102-109

Author(s):

Mercy Burawis

Keyword(s):

Query Optimization ◽

Reduction Method ◽

Data Cube ◽

Data Generation

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Faculty Opinions recommendation of Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725982921.793526188 ◽

2016 ◽

Author(s):

Naoki Masuda

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Sliding Window ◽

Resting State Fmri ◽

Dynamic Functional Connectivity

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Multi-dimensional query optimization algorithm for bitmap index with binning

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.02013 ◽

2010 ◽

Vol 30 (8) ◽

pp. 2013-2016 ◽

Author(s):

Li-ming WANG ◽

Xiao CHENG ◽

Yu-mei CHAI

Keyword(s):

Query Optimization ◽

Optimization Algorithm ◽

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Application of geometric moving average martingale algorithm in anomaly analysis before earthquake based on sliding window

Journal of Computer Applications ◽

10.3724/sp.j.1087.2013.03608 ◽

2013 ◽

Vol 33 (12) ◽

pp. 3608-3610 ◽

Author(s):

Liping CHEN ◽

Xiangzen KONG ◽

Zhi ZHENG ◽

Xinqi LIN ◽

Xiaoshan ZHAN

Keyword(s):

Moving Average ◽

Sliding Window ◽

Anomaly Analysis

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Improved sliding window non-parameter cumulation sum algorithm

Journal of Computer Applications ◽

10.3724/sp.j.1087.2013.00088 ◽

2013 ◽

Vol 33 (1) ◽

pp. 88-91 ◽

Author(s):

Bo CHEN ◽

Jianlin MAO ◽

Guanhua QIAO ◽

Ning DAI

Keyword(s):

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A Query Optimization Method Based on User Profiles Reasoning

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2006.00758 ◽

2011 ◽

Vol 30 (1) ◽

pp. 33-37

Author(s):

Xiang Mei ◽

Xiang-wu Meng ◽

Jun-Liang Chen ◽

Meng Xu

Keyword(s):

Query Optimization ◽

Optimization Method ◽

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A Moving Object Detection Method Based on Sliding Window Gaussian Mixture Model

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2012.01449 ◽

2014 ◽

Vol 35 (7) ◽

pp. 1650-1656 ◽

Author(s):

Jian-ying Zhou ◽

Xiao-pei Wu ◽

Chao Zhang ◽

Zhao Lü

Keyword(s):

Object Detection ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Detection Method ◽

Sliding Window ◽

Gaussian Mixture ◽

Moving Object Detection ◽

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An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

Recent Patents on Computer Science ◽

10.2174/2213275912666190419215231 ◽

2019 ◽

Vol 12 ◽

Author(s):

Pankaj Dadheech ◽

Dinesh Goyal ◽

Sumit Srivastava ◽

Ankit Kumar

Keyword(s):

Big Data ◽

Query Optimization ◽

Spatial Data ◽

Spatial Information ◽

Big Data Analytics ◽

Spatial Query ◽

Data Process ◽

Boolean Queries ◽

Spatial Query Optimization ◽

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

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